AI competency framework
for students
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S H O R T
S U M M A R Y
Preparing students to be responsible
and creative citizens in the era of AI
Artificial intelligence (AI) is increasingly integral to our lives,
necessitating proactive education systems to prepare students to
be responsible users and co-creators of AI. Integrating AI learning
objectives into official school curricula is crucial for students globally
to engage safely and meaningfully with AI.
The UNESCO AI competency framework for students aims to help
educators in this integration, outlining 12 competencies across four
dimensions: Human-centred mindset, Ethics of AI, AI techniques
and applications, and AI system design. These competencies span
three progression levels: Understand,
Apply, and Create. The framework details
curricular goals and domain-specific
pedagogical methodologies.
Grounded in a vision of students as AI
co-creators and responsible citizens,
the framework emphasizes critical
judgement of AI solutions, awareness of
citizenship responsibilities in the era of
AI, foundational AI knowledge for lifelong
learning, and inclusive, sustainable AI
design.
By 2022,
only
15 countries
had included AI
learning objectives
in their national
curricula
“Since wars begin in the minds of men and
women it is in the minds of men and women
that the defences of peace must be constructed”
AI competency framework
for students
Foreword
The past decade has seen widespread adoption of artificial
intelligence (AI) in all areas of human development, with the public
release of generative AI tools in November 2022 only accelerating
its permeation within social life. The education sector, which is at
the heart of the transformation of human societies, has been no
exception.
© UNESCO
This process of rapid technological change brings multiple
opportunities but also risks and challenges for students, teachers
and society at large. In the era of AI, school students need to be
prepared to become active co-creators of AI, as well as future leaders
who will shape novel iterations of the technology and define its
relationship with society.
This is exactly the ambition of UNESCO’s AI competency framework for students – the first ever
global framework of its kind. It aims to support the development of core competencies for
students to become responsible and creative citizens, equipped to thrive in the AI era. This will
help students acquire the values, knowledge and skills necessary to examine and understand AI
critically from a holistic perspective, including its ethical, social and technical dimensions.
The new framework embodies UNESCO’s mandate by anchoring its vision of AI and education
in principles of human rights, inclusion and equity. This approach seeks to ensure that AI
supports the development of human capabilities, protects human dignity and agency, and
promotes justice and sustainability.
The publication builds on UNESCO’s previous work in the field, such as the ICT competency
framework for teachers, AI and education: Guidance for policy-makers, and the more recent
Guidance for generative AI in education and research. It reflects the contributions of a wide range
of stakeholders, drawing on UNESCO Member States’ insights on developing and implementing
AI school curricula, the expertise of an international working group, three international
consultation meetings, and multiple rounds of online consultations.
The AI competency framework for students has been developed hand in hand with a
competency framework for teachers. It is my hope that these two frameworks will empower
students and teachers to shape the digital futures we want.
In a world characterized by rising complexity and uncertainty, it is our collective responsibility
to ensure that education remains the central space for transformation of our shared futures.
Stefania Giannini
UNESCO Assistant Director-General for Education
Acknowledgements
Under the leadership of Stefania Giannini, Assistant Director-General for Education, and
the guidance of Sobhi Tawil, Director of the Future of Learning and Innovation Division
at UNESCO, the drafting of the publication was led by Fengchun Miao, Chief of Unit for
Technology and AI in Education.
The framework was drafted by Fengchun Miao, Chief of Unit for Technology and AI in
Education at UNESCO and Kelly Shiohira, Director of the Global Science of Learning Education
Network. The development of the framework also benefited from the contributions of a
group of international experts, including Natalie Lao, Executive Director of the App Inventor
Foundation, and Lidija Kralj, Education Analyst at EduConLK. UNESCO is grateful to them for
contributing their expertise.
Special appreciation is also extended to the following experts for peer-reviewing the
publication: Kate Arthur, Co-founder and partner at Comz; Ke Gong, President of the World
Federation of Engineering Organizations (WFEO); Kaśka Porayska-Pomsta, Professor of AI
in Education at University College London; Nisha Talagala, Co-Founder and CEO of AIClub
and AIClubPro; Monique Brodeur, Hugo Couture, Sophie Gosselin, Yves Munn and Benoit
Petit from the Conseil supérieur de l’éducation du Québec; and Luc Bégin, Nicolas Bernier and
Guillaume Pelletier from the Commission de l’éthique en science et en technologie.
Thanks also go to the following UNESCO colleagues for contributing to the peer-review
process: Andrea Detmer at the Executive Office of the Culture Sector; Amal Kasry, Chief of
the Basic Sciences, Research, Innovation and Engineering Section; Karalyn Monteil, Head
of the Programmes and Stakeholder Outreach Unit at the Culture Sector; Renato Opertti,
Senior Education Expert at the International Bureau of Education; Arianna Valentini at the
International Institute for Higher Education in Latin America and the Caribbean; Soichiro
Yasukawa, Chief of the Disaster Risk Reduction Unit in the Science Sector; Martiale Kana
Zebaze, Senior Programme Specialist for Science, Technology and Innovation at the UNESCO
Harare Office; as well as Jaco Du Toit, Chief, and Zeynep Varoglu, Programme Specialist, at the
Section for Universal Access to Information and Digital Inclusion in the Communication and
Information Sector.
Special thanks go to Luisa Ferrara at the Unit for Technology and AI in Education within
the Future of Learning and Innovation Division, for managing expert inputs, and to Glen
Hertelendy from the same Unit for coordinating the production of the publication.
Additionally, UNESCO is grateful to Jenny Webster for copy-editing and proofreading the text.
Finally, UNESCO would like to thank the Tomorrow Advancing Life (TAL) Education Group of
China for generously supporting this publication project and, more broadly, for promoting the
potential of artificial intelligence for the future of education.
AI competency framework for students – Table of contents
Table of contents
Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
List of tables and boxes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
List of acronyms and abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Chapter 1: Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.1
Why an AI competency framework for students? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12
1.2
Purpose and target audience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13
Chapter 2: Key principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.1
Fostering a critical approach to AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14
2.2
Prioritizing human-centred interaction with AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15
2.3
Encouraging environmentally sustainable AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15
2.4
Promoting inclusivity in AI competency development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16
2.5
Building core AI competencies for lifelong learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17
Chapter 3: Structure of the AI competency framework for students . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.1
The framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18
3.2
Progression levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20
Level 1: Understand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20
Level 2: Apply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20
Level 3: Create . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21
3.3
Aspects
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21
Human-centred mindset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22
Ethics of AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23
AI techniques and applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24
AI system design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25
Chapter 4: Specifications of AI competencies for students . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.1
Level 1: Understand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .27
4.2
Level 2: Apply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37
4.3
Level 3: Create . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .45
8
AI competency framework for students – Table of contents
Chapter 5: Applying the framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5.1
Aligning AI competencies as the foundation for national AI strategies
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .53
5.2
Building interdisciplinary core and cluster AI curricula for AI competency . . . . . . . . . . . . . . . . . . . . . . . . . . . .56
5.3
Framing future-proofing and locally feasible AI domains as carriers of the curriculum . . . . . . . . .58
5.4
Tailoring age-appropriate spiral curricular sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .59
5.5
Building enabling learning environments for AI curricula
5.6
Promoting the professionalization of AI teachers and streamlining their support . . . . . . . . . . . . . . . .62
5.7
Guiding the cohort-based design and organization of pedagogical activities . . . . . . . . . . . . . . . . . . . . . .64
5.8
Constructing competency-based assessments on the progression of key AI aspects . . . . . . . . . . . .69
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .61
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
Endnotes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
9
AI competency framework for students – List of tables and boxes
List of tables
Table 1 . AI competency framework for students . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Table 2 . Competency blocks for level 1: Understand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Table 3 . Competency blocks for level 2: Apply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Table 4 . Competency blocks for level 3: Create . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
Table 5 . Examples of assessment tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
List of boxes
Box 1: Recommendation on the Ethics of Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
Box 2: Supporting human resource development: The Republic of Korea’s
National Strategy for Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
Box 3: The United Arab Emirates’ interdisciplinary approach to K-12 AI curricula . . . . . . . . . . . . . . 57
Box 4: The spiral curricular sequence of ‘Day of AI’ courses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
Box 5: Typical enabling learning environment set up by governments’ AI curricula . . . . . . . . . . 61
Box 6: An AI competency framework for AI subject teachers in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
Box 7: Pedagogical methodologies in the MIT curriculum on the ethics of AI for
middle school students . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
10
AI competency framework for students – List of acronyms and abbreviations
List of acronyms and abbreviations
AGI
Artificial general intelligence
AI
Artificial intelligence
AI CFS
AI competency framework for students
CCDI
Computing, Creative Design and Innovation
CG
Curricular goal
GAN
Generative adversarial networks
K-12
Kindergarten through 12th grade
ICT
Information and communication technology
IEA
International Energy Agency
IGO
Intergovernmental organization
ITU
International Telecommunication Union
MIT
Massachusetts Institute of Technology
NGO
Non-governmental organization
STEAM
Science, technology, engineering, arts and mathematics
STEM
Science, technology, engineering and mathematics
TVET
Technical and vocational education and training
UNESCO
United Nations Educational, Scientific and Cultural Organization
11
AI competency framework for students – Chapter 1: Introduction
Chapter 1: Introduction
1.1 Why an AI competency
framework for students?
The rapid iterations and proliferation of
artificial intelligence (AI) across all aspects of
life and all sectors are posing new challenges
regarding the nature of machine intelligence,
the collection and use of personal data, the
role of humans and machines in decisionmaking, and the impact of AI on social and
environmental sustainability. It is essential
that education systems prepare students
not only with the knowledge and skills
to use AI, but also with insight into the
potential impact of technology on societies
and the environment at large. Given the
transformative potential of AI for human
societies, it is crucial to equip students with
the values, knowledge and skills needed for
the effective use and active co-creation of AI.
Education, as a public sector, cannot be
reduced to a testing ground for the passive
adoption of AI. The role of the education
sector is not only to prepare students to
adapt to a society that is increasingly being
transformed by AI technologies; it also has
a key role to play in empowering young
people to help co-create sustainable futures
by rebalancing our relationships, not only
with others, but also with technology and
the environment. By defining the core
competencies that students are likely to
require as we move deeper into the AI era,
the ultimate aim of this AI competency
framework for students (AI CFS) is to help
shape responsible and creative citizens that
can co-create these desirable futures.
12
Governments acknowledged the urgent
need to develop AI literacy and more
advanced AI competencies as early as 2019,
when they adopted the UNESCO Beijing
Consensus on AI and Education. Indeed, the
Beijing Consensus underlined the need to
equip people with AI literacy across all layers
of society. However, according to a recent
survey conducted across 190 countries,
only some 15 countries were found to be
developing or implementing AI curricula
in school education (UNESCO, 2022b). The
survey also found that there was wide
variation in how countries defined AI literacy,
skills and competency. The results of the
survey therefore underscored the urgency
of developing a harmonized approach to
integrating AI-related teaching and learning
content in school curricula.
Far too often, the definition of AI
competencies for students is influenced
by training designed and/or provided by
private companies, which tends to focus
on technical skills to operate profit-driven
AI platforms. Such approaches seldom
engage with the broader critical issues
of the implications of AI for learning and
citizenship, more broadly. There is currently
a void in too many education systems when
it comes to public-approved frameworks
for introducing AI-related content and
methods to educational curricula. One
of the challenges that public education
systems are facing in filling this void is the
lack of an international reference framework
on AI competencies for students. Such
an international reference framework
can inform the design of national/local AI
competency frameworks for students that
AI competency framework for students – Chapter 1: Introduction
promote a critical and ethical approach to
AI tools, as well as develop the foundational
knowledge required for their effective and
meaningful use in education. The aim of this
AI CFS is to fill this void.
AI technology is a rapidly moving target. It is
therefore critical to ensure that all students
have a core set of knowledge, skills and
values for interacting ethically and effectively
with AI in the present. This foundation can
enable students to utilize future iterations of
AI technology in an appropriate and humancentred manner.
The AI CFS supports educational authorities
to respond to these needs by defining a
core set of competencies for students that
fall under four aspects: Human-centred
mindset; Ethics of AI; AI techniques and
applications; and AI system design. These
four aspects are articulated at three levels
of progression or mastery (understanding,
application and creation), resulting in a
total of twelve competency blocks. For each
of these competency blocks, the AI CFS
proposes detailed specifications on relevant
pedagogical methodologies and strategies
for the planning and provision of AI-related
curricular content.
1.2 Purpose and target audience
The AI CFS aims to serve as a guide for
public education systems to build the
competencies required of all students and
citizens for the effective implementation of
national AI strategies and the building of
inclusive, just and sustainable futures in this
new technological era.
More specifically, the AI CFS: (1) provides a
global reference framework on the core set
of AI competencies for students to inform
the design of national or institutional AI
competency frameworks; (2) specifies
typical attitudinal and behavioural
performance relating to the key aspects of AI
competencies at different levels of mastery
to help design AI-related curricular content
for school students; and (3) recommends
an open-ended roadmap to help plan the
learning sequence of AI curricula across
grade levels.
As a global reference framework, the AI CFS
is to be tailored to the diverse readiness
levels of local education systems in terms of
curricula, the enabling learning environment
for teaching AI, preparedness of teachers,
and the prior knowledge and capacities of
specific groups of students.
The AI CFS is aimed principally at policymakers, curriculum developers, providers of
education programmes on AI for students,
school leaders, teachers and educational
experts.
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AI competency framework for students – Chapter 2: Key principles
Chapter 2: Key principles
2.1 Fostering a critical
approach to AI
Critical thinking is a fundamental skill that
students need to meaningfully engage with
AI as learners, users and creators. Students
also have the responsibility to determine
what types of AI should be developed and
how they should be used to drive human
societies towards inclusive, environmentally
sound, shared futures. School students
need to be supported to become active
co-creators of AI, as well as potential leaders
who will define further iterations of AI and
its interactions with human society for
present and future generations. To support
this vision, the AI CFS is designed to foster
a critical approach to AI by engaging
students with fundamental questions, such
as: is AI poised to help solve real-world
challenges faced by humans, or does it pose
insurmountable threats to humans? Are
adverse impacts on climate of training and
using AI disproportionate to its anticipated
benefits? What social, economic, political and
demographic impacts of the use of AI should
be carefully reviewed?
The AI-driven transformation across
development sectors has profound
implications for human agency, human
interactions, social equity, economic
inclusiveness, and environmental
sustainability. Thus, in the first place, school
students are expected to be conscious
and knowledgeable of the advantages
and limitations of existing affordances of
AI. The pre-condition for responsible use
consists in students’ abilities to detect
the trustworthiness and proportionality
of AI tools. The AI CFS aims to prepare
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students with the values, knowledge
and skills necessary to critically examine
the proportionality of AI from an ethical
perspective. This includes examining and
understanding its impact on human agency,
social inclusion and equity, institutional
and individual security, cultural and
linguistic diversity, the construction and
expression of plural opinions, as well as
on the environment and on ecosystems.
Students are expected to move beyond
the misconception that AI is a solution to
everything. Rather, they are to become
conscious decision-makers on when AI
systems and applications should, or should
not, be used; what problems they may or
may not solve; and when and how AI should
be designed and used as one part of a wider
solution. The AI CFS aims to nurture students’
aspirations to apply and design AI tools to
serve meaningful specific purposes or to
address real-world challenges and promote
sustainable development.
Societies are moving into the era of AI at
different paces, but students everywhere are,
or will be, citizens in contexts characterized
by widespread AI integration. They will not
only have to comply with legal regulations
and ethical principles, but, as citizens,
they will also have to contribute to the
adaptation of AI standards and regulations.
The framework therefore highlights the
importance of supporting students to
become responsible and ethical users of,
as well as contributors to, AI. It engages
students to reflect on key controversies
surrounding AI, internalize ethical
principles, and become familiar with related
regulations.
AI competency framework for students – Chapter 2: Key principles
The AI CFS sets out a forward-looking
vision of the type of citizenship required
by societies increasingly shaped by AI. It
proposes that students be challenged and
enabled to make meaningful use of AI for
self-actualization; to evaluate its social,
economic and environmental impacts;
and to contribute, at a level appropriate
for their age or grade, to the development
of AI regulations, helping to shape our
relationship with technology in society
at large.
2.2 Prioritizing human-centred
interaction with AI
In the era of AI, interaction between humans
and AI systems and applications will become
an essential constituent element of public
service, production and commerce, social
practice, learning, and daily life. Establishing
the competencies needed to understand and
ensure human-centred interaction with AI in
these domains is a priority for the AI CFS.
UNESCO’s human-centric approach
advocates that the design and use of AI
should serve the development of human
capabilities, protect human dignity
and agency, and promote justice and
sustainability throughout the entire AI life
cycle and all possible human–AI interaction
loops. Such an approach must be guided
by human rights principles and respect
for the linguistic and cultural diversity that
defines the knowledge commons. A humancentred approach also requires that AI be
used in ways that ensure transparency and
explainability, as well as human control and
accountability.
As AI becomes increasingly sophisticated
and more widely used, a key danger is its
potential to undermine human agency and
compromise the development of human
intellectual skills. While AI can be used to
challenge and extend human thought, it
should not be allowed to usurp or replace
critical thinking. The protection and
enhancement of human agency should,
therefore, always be a core principle in
the design of AI curricula and education
programmes. The AI CFS aims to support
students to understand the types of
data that AI may collect from them, the
methods with which the data may be used
to train AI models, and the impact that the
data cycle may have on their privacy and
wider lives. It seeks to stimulate students’
intrinsic motivation to grow and learn as
individuals and to reinforce their autonomy
in contexts in which sophisticated AI
systems are increasingly being integrated.
Critical AI competencies, as proposed in
this framework, can also guide students
to understand the unique value of social
interaction and of the creative works
produced by humans that should not be
replaced by AI outputs. By developing
competencies for human-centred
engagement with AI, the framework aims to
prevent students from becoming addicted to
or dependent on AI, and to foster behaviours
that maintain human accountability for highstakes decisions.
2.3 Encouraging environmentally
sustainable AI
As co-creators and potential leaders of the
next generations of AI technology, students
need to have a critical understanding of the
adverse environmental impact of profitdriven approaches to the design, training
and deployment of AI models. Education
systems bear the responsibility of ensuring
that students understand carbon emissions,
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AI competency framework for students – Chapter 2: Key principles
analyse the root causes of climate change,
and act judiciously to protect the climate
and the environment.
In the race to produce increasingly powerful
AI models, environmental sustainability
is often considered to be of secondary
importance. In some instances, it has even
been intentionally obscured by claims that
AI holds the promise of solving climate
change. As global leaders and policy-makers
work to consider regulations around the
consumption of energy and the protection
of the environment, it is imperative that
students understand how the training of AI
models is contributing to the destruction of
the natural environment. Learning about AI
should empower them to urgently explore
more climate-friendly approaches to the
design, training and use of AI models. The
AI CFS attends to this by guiding students
to design and implement project-based
learning activities on the environmental
impacts of AI use and training, prompting
students to investigate potential solutions to
mitigate these impacts.
2.4 Promoting inclusivity in AI
competency development
Access to AI and AI competencies represent
the two sides of citizens’ basic rights in
today’s world. All students should have
inclusive access to the environments
required for learning about AI at the basic
level, and they should be supported to learn
how to embed the principle of inclusivity
into the design of AI and be prepared to
contribute to an inclusive AI society.
When defining AI competencies, school
students should be provided with
opportunities to understand and apply
the principle of inclusivity across the AI
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life cycle. This covers the selection of
representative data, the choice of biasagnostic algorithms and anti-discrimination
training methods, the design of accessible
functionalities, testing for the inclusiveness
of AI outputs, and impact assessment of the
use of AI on social inclusion. With regard
to AI system design, students can deepen
their understanding and application skills
to assess the needs of users with different
abilities as well as those from diverse
linguistic and cultural backgrounds.
In selecting the models and categories
of technologies as vectors of AI-related
teaching and learning, care is needed to
avoid favouring certain demographics over
others. When recommending specific AI tools
for educational purposes, rigorous public
validation mechanisms must be applied
to avoid algorithms with bias(es) related
to gender, ability, socio-economic status,
language, ethnicity and/or culture. AI tools
that are designed to support individuals
with disabilities and promote linguistic and
cultural diversity should be given priority.
Where such validation mechanisms are
unavailable, the recommendation of specific
AI tools for use at scale should be avoided.
Turning to delivery of the curriculum, specific
measures can be outlined to provide basic
enabling conditions for the implementation
of the AI CFS-based curriculum. While AI
frameworks or educational programmes
should be designed to be applicable to
all students, including those who live in
low-tech settings, engagement with AI
without access to the internet and AI tools
will limit the scope and mastery level of
AI competencies. Governments should
commit to promoting inclusive access to
basic internet connectivity, updated digital
devices, open-source or affordable AI
AI competency framework for students – Chapter 2: Key principles
programmes and software, and essential AI
devices, with the support of academia or
the private sector, where appropriate. Once
again, these efforts must pay particular
attention to students who have disabilities
and/or are from linguistic or cultural minority
groups.
2.5 Building core AI
competencies for lifelong
learning
AI-related teaching and learning should
serve to build core AI competencies that
allow students to accommodate new
knowledge, as well as adapt to solving
problems in new contexts with novel AI
technologies. First and foremost, these
core competencies must include values
associated with an ethical and humancentred mindset. Students need guidance to
progressively deepen their understanding
of particular human rights – such as rights
to equality, non-discrimination, privacy
and plural expression – as well as their
implications for varying forms of human–AI
interaction. The competencies also reflect
the need to understand controversies
surrounding AI and the key ethical
principles that guide regulation, as well
as foster practical skills to combat bias,
protect privacy, promote transparency and
accountability, and adopt an ethics-bydesign approach to the co-creation of AI.
The core competencies are brand-agnostic
and product-agnostic, ensuring that students
can appropriately engage with a range of
tools, as well as with future iterations of AI
technologies. It enables them to develop an
age-appropriate and progressively deeper
understanding of AI data, algorithms, models
and system design. Students must be
supported to construct this understanding
by connecting AI concepts with real-world
challenges to develop critical problemsolving skills. Students should be further
encouraged to exploit their creativity in
an effort to optimize existing AI models or
co-create more meaningful AI. These core
competencies constitute the foundation for
further learning and more specialized use of
AI in further education, work and life.
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AI competency framework for students – Chapter 3: Structure of the AI competency framework for students
Chapter 3: Structure of the AI competency
framework for students
3.1 The framework
The AI CFS specifies twelve competency
blocks based on a matrix of two dimensions.
The first dimension comprises four
interlinked aspects of AI competencies, while
the second dimension includes three levels
of progression or mastery that students are
expected to engage with iteratively.
While the AI CFS anchors the definition of
AI competency on three pillars that frame
wider core competencies for students
– namely, knowledge, skills and values
– it also aims to encourage an ethical
understanding of human-led methods
underlying AI systems. Based on this
conceptualization, the framework defines
four essential constituent elements of
students’ AI competency: a human-centred
mindset, ethics of AI, AI techniques and
applications, and AI system design. These
elements focus on fundamental values,
social responsibilities to uphold ethical
principles, foundational knowledge and
skills, and higher-order thinking skills for
system design. While different elements
can be developed through domain-specific
learning and pedagogical methodologies,
AI competencies are ultimately a set of
interdisciplinary, general abilities and value
orientations that extend beyond particular AI
domains or tools.
The first aspect positions students’
competencies within a human-centred
attitude towards the benefits and risks of AI.
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It also aims to foster a critical understanding
of the proportionality1 of specific AI tools
for our human needs and for the sustainable
development of the environment and
ecosystems. Ethics of AI, the second
aspect, encompasses the social and ethical
components of students’ AI competencies,
including the social skills to navigate,
understand, practise and contribute to the
adaptation of a growing set of principles that
regulate human behaviour throughout the
entire life cycle of AI.
The third aspect, AI techniques and
applications, represents an integrated
view of the intrinsically linked conceptual
knowledge on AI and associated operational
skills, using selected AI tools and authentic
tasks. The last aspect is AI system design,
which covers comprehensive engineering
skills that determine the problem scoping,
architecture building, training, testing and
optimization of AI systems. This aspect aims
to challenge and enable students to gain
a deeper understanding of AI systems and
scaffold their exploratory learning for the
pursuit of further study in the field of AI.
The second dimension of the framework
outlines three levels of progression:
Understand, Apply and Create, which are
designed to reflect levels of mastery across
all four aspects outlined above. They can be
used to furnish AI curricula or programmes
of study with a spiral learning sequence
AI competency framework for students – Chapter 3: Structure of the AI competency framework for students
across grade levels, to assist students in
progressively building a systematic and
transferable schema of competencies.
The framework matrix cuts across the four
aspects for the three levels of progression
or mastery (see Table 1). At the intersection
of these levels and aspects are twelve
constituent blocks of AI competencies whose
characteristics underpin the critical thinking,
ethical examination, practical use and
iterative co-creation of AI. These competency
blocks should be understood as interlinked
units for the framing of key components.
Rather than considering them as fragmented
and disparate topics to be learned in
isolation, they can be connected and woven
together as the operational organs of AI
competency.
The matrix provides a blueprint for learning
outcomes at a minimum level of mastery
within a certain competency block. More
specifically, the matrix is designed to guide:
(1) the scoping of main AI-related focus areas
and expected mastery levels, tailored to local
AI readiness and available instructional time;
(2) the identification of AI-related learning
content that can be integrated across
existing curricula, subject areas, and grade
levels; (3) the definition of proficiency levels
and the development of assessment criteria
to assess students’ general AI competencies
and progression; and (4) the design
and exploration of age-appropriate and
domain-specific agile teaching and learning
methodologies. Many of these factors will
be vital to consider when a country, district
or school localizes this framework; the
selection of focus aspects and specification
of the desired mastery levels, for instance,
will depend on students’ existing AI
competencies, the training and skills of
teachers, the availability of learning hours,
and local AI readiness, including affordability
and infrastructure.
Table 1. AI competency framework for students
Competency aspects
Progression levels
Understand
Apply
Create
• Human-centred
mindset
• Human agency
• Human
accountability
• Citizenship in the era
of AI
• Ethics of AI
• Embodied ethics
• Safe and responsible
use
• Ethics by design
• AI techniques and
applications
• AI foundations
• Application skills
• Creating AI tools
• AI system design
• Problem scoping
• Architecture design
• Iteration and
feedback loops
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AI competency framework for students – Chapter 3: Structure of the AI competency framework for students
3.2 Progression levels
The three levels reflect increasing
sophistication, proficiency and ethical
consciousness in using and co-creating
AI technology. Students are expected
to progress through them reciprocally.
These levels, and the specifications of each
competency block, can guide both the
formative and summative evaluations of
students’ AI competencies, as well as inform
the design of contextually relevant and agile
pedagogical methodologies.
Level 1: Understand
This first level is designed for all students. All
individuals are, or will be, interacting with
some form of AI over the course of their lives.
It is also true that AI providers have been
mining and manipulating data from almost
all internet users. All students must therefore
develop the human-centred values,
knowledge and skills needed to engage in
a safe, informed and meaningful manner
in their daily interaction with AI in various
spheres of life.
At the ‘Understand’ level, students are
expected to foster an understanding of
what AI is and construct age-appropriate
interpretations of the values, ethical issues,
concepts, processes and technical methods
underlying AI tools and their uses. They
should be able to explain or exemplify their
knowledge with connections to real-life
or social practices and assimilate novel
knowledge by integrating them into their
own knowledge schemas.
20
This level of mastery provides the essential
attitudinal, cognitive and practical
foundations for the further study of AI. It
does not define the exit-level competencies
for specific areas or domains of AI overall.
Level 2: Apply
Given that the use of AI has permeated all
sectors, as well as all aspects of life, including
education and work, students at school
should be prepared to become responsible,
active and effective users of AI, both for
the sake of their own individual interests,
as well as to address shared sustainability
challenges. The outcomes at the second
level, ‘Apply’, are therefore relevant for all
school students and can be used to tailor
the scope, breadth and level of difficulty of
thematic modules of a formal AI curriculum.
Studying at this level requires students to
have acquired a basic understanding of the
human-centred approach and essential
ethical principles for AI, as well as basic AI
knowledge and application skills.
At the ‘Apply’ level, students are expected
to enhance, transfer and adapt their learned
values, knowledge and skills to new learning
processes. They do so by addressing
theoretical questions and/or practical tasks
in more complex contexts, and by critically
examining advanced technical methods
behind AI tools. Upon achieving this level,
students will have constructed a sound
and transferable foundation of conceptual
knowledge and associated AI skill-sets. They
should also be able to apply the humancentred mindset and ethical perspective to
the assessment, study and practical uses of
AI tools.
AI competency framework for students – Chapter 3: Structure of the AI competency framework for students
Students at this level may progress to
the third, more specialized level, Create.
However, it is possible that some students
will not have a strong interest in AI, or will
lack sufficient time or opportunities to
finetune their AI competencies within the
formal learning environment at school. For
many, ‘Apply’ at Level 2 will be the point
of exit for their AI-related competency
development, at least at school.
Level 3: Create
The exponential pace of innovation within
the AI sector means that technology
providers are defining the terms of the
transformation of our societies. Developing
critical AI competencies is critical to ensuring
that the design, deployment and use of
AI responds to the needs of users and
benefits the public. School students should
be prepared to create trustable AI tools
and to take a leading role in the definition
and design of the next generation of AI
technologies. At the ‘Create’ level, students
are expected to become conscientious AI
co-creators, developing human-centred
solutions to positively impact the design
and use of AI. Study at this level requires
the integrated application of the acquired
values, knowledge and skills on AI to design,
implement and test AI solutions that can
help address real-world challenges.
Students will critically leverage their
knowledge and skills on data, algorithms and
ethical design; actively craft AI applications;
and deliberate on the adaptation of AI
regulations.
At the ‘Create’ level, students are expected to
reinforce their interest in AI innovation and
develop new AI tools based on open-source
and/or customizable datasets, programming
tools or AI models. Throughout the iterative
process of customizing and testing AI
technologies, students are expected to
reinforce the sense of being an AI co-creator
and belonging within a broader community,
helping to lead the human-centred design
and use of AI. At this level, students are
also expected to enhance their capacity to
critically assess the social implications of AI
and to personalize the responsibilities of
being a citizen in AI-driven societies.
Learning at the ‘Create’ level also aims
to foster students’ creative problemsolving skills and a proactive attitude to
advocating for ethical AI practices. Meeting
the requirements of this level in full will
require sufficient allocation of learning
time and space within the curriculum (e.g.
an entire semester or multiple semesters).
The learning programme must also provide
the necessary AI resources and facilitate
age-appropriate innovative pedagogical
methodologies. For students who do not
have a strong interest in pursuing deeper
study in the field, the learning outcomes at
this level, in particular under the ‘AI system
design’ aspect, should be offered as elective
programmes rather than as compulsory
requirements for all students.
3.3 Aspects
The four aspects specify the essential
constituent elements of AI competencies
that students need to build and continuously
update in order to become responsible users
and active co-creators of AI, and potential
leaders in defining and developing next
generations of AI.
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AI competency framework for students – Chapter 3: Structure of the AI competency framework for students
Human-centred mindset
Competency aspects
• Human-centred
mindset
Progression levels
Understand
Apply
Create
• Human agency
• Human
accountability
• Citizenship in the era
of AI
The ‘Human-centred mindset’ aspect focuses
on students’ values, beliefs and critical
thinking skills, applied to the examination
of whether AI is fit for purpose, whether its
use is justified, how humans should interact
with it, and what responsibilities individuals
and institutions should take on to contribute
to the building of safe, inclusive and just AI
societies. A human-centred mindset lays
the foundation for further engagement
with all aspects of AI. The full expression
of this aspect also encompasses human
identities in relation with AI, assuming social
and civic responsibilities, and the pursuit
or deepening of personal interests in the AI
era. The values and skills that this aspect is
intended to nurture can be characterized by
the following three competency blocks:
Human agency: Students are expected
to be able to recognize that AI is humanled and that the decisions of AI creators
influence the way in which AI systems
impact human rights, human–AI interaction,
as well as their own lives and societies.
They are expected to understand the
implications of protecting human agency
throughout the design, provision and use of
AI. Students will understand what it means
for AI to be human-controlled, and what the
consequences might be when this is not
the case.
22
Human accountability: Students are
expected to recognize that human
accountabilities are the legal obligations of
AI creators and AI service providers, and to
understand what human accountabilities
they should assume during the design
and use of AI. They should also develop an
awareness that human accountability is a
legal and social responsibility when using AI
to assist in decision-making, and that human
choice should not be ceded to AI when
making high-stakes decisions.
Citizenship in the AI era: Students are
expected to critically understand the impact
of AI on human societies and to promote
responsible and inclusive design and use
of AI for sustainable development. They
should have an awareness of their civic and
social responsibility as citizens in the era of
AI. Students are also expected to develop a
desire to continue learning about, and using,
AI throughout their lives to support selfactualization.
AI competency framework for students – Chapter 3: Structure of the AI competency framework for students
Ethics of AI
Competency aspects
• Ethics of AI
Progression levels
Understand
Apply
Create
• Embodied ethics
• Safe and responsible
use
• Ethics by design
The ‘Ethics of AI’ aspect represents the ethical
value judgements, embodied reflections,
and social and emotional skills students
require to navigate, understand, practise and
contribute to the adaptation of a growing set
of principles and regulatory rules relative to
the entire life cycle of AI systems. Students
are expected to understand and apply
knowledge on the governance of ethics at
the intersection of global implications and
local contexts. As the rapid iterations of AI
are triggering more profound controversies,
the scope of the ethics of AI is expanding,
and new regulations, laws and rules are
being adopted. The three competency
blocks for this aspect outline key steps for
students to gradually internalize ethical
principles as well as habituate compliance
with AI regulations.
Embodied ethics: Students are expected
to develop a basic understanding of the
issues underlying key ethical debates around
AI, including the impact of AI on human
rights, social justice, inclusion, equity and
climate change within their local context and
personal lives. They will have understood,
internalized, and adopted the following
principles in their reflective practices and
uses of AI tools in their learning and beyond:
●
Do no harm: Students demonstrate
an understanding that AI systems
should not be used for purposes that
might be harmful for humans (such as
facial recognition for surveillance or
assigning social status, or predictive
algorithms for grading examinations).
This includes the ability to assess
whether a certain AI solution infringes
upon human values and rights,
particularly data privacy, and to
decide on whether a particular AI
method complies with global or local
regulations.
●
Proportionality: Students develop
the capacity – as appropriate for their
age and ability level – to examine
whether or not the use of a specific AI
system is advantageous in achieving
a justified aim, and whether or not a
given AI method is appropriate to the
context.
●
Non-discrimination: Students are
aware of and are able to detect
gender, ethnic, cultural and other
biases embedded in AI tools or their
outputs. Further, students are aware
of AI divides within and between
countries, and understand the need
to make efforts to address these
and ensure greater accessibility and
inclusivity.
●
Sustainability: Students are able to
explain and illustrate the implications
of AI systems for environmental
sustainability.
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AI competency framework for students – Chapter 3: Structure of the AI competency framework for students
●
Human determination in human–AI
collaboration: Students are able to
demonstrate why humans should
bear ethical and legal responsibilities
for the use of AI; they are able to
exemplify how humans can remain
accountable in AI-assisted decisionmaking loops, rather than cede
determination to machines.
●
Transparency and explainability:
Students are aware that users are
entitled to request explanatory
information from designers and
providers on how AI tools work, how
their outputs are produced based
on algorithms and models, and the
degree to which the deployment and
application of certain AI tools are
appropriate for users of a certain age
or ability level.
●
of the risks of disclosing data privacy
and they take measures to ensure that
their data are collected, used, shared,
archived and deleted only with their
deliberate and informed consent.
They are also aware of the specific
risks of certain AI systems, and are
able to protect their own safety, as
well as that of their peers, when using
AI.
●
Safe and responsible use: Students
are expected to be able to use AI in
a responsible manner in compliance
with ethical principles and locally
applicable regulations. They are aware
Ethics by design: Students are
expected to adopt an ethics-bydesign approach to the design,
assessment and use of AI tools, as well
as to the review and adaptation of AI
regulations. Students are aware that
assessing the intent behind AI design
involves examining all steps of the AI
life cycle, starting with the stage of
conceptualization. Students should be
able to assess the compliance of an AI
tool with ethical regulations, as well
as review AI regulations and inform
adaptation.
AI techniques and applications
Competency aspects
• AI techniques and
applications
Progression levels
Understand
Apply
Create
• AI foundations
• Application skills
• Creating AI tools
The ‘AI techniques and applications’
aspect represents the intrinsically linked
conceptual knowledge on AI and associated
operational skills, in connection with
concrete AI tools or authentic tasks. This
aspect serves as the most important and
transferable technical foundation for a
concrete understanding and application of
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a human-centred mindset and its associated
ethical principles. The basic knowledge
structure and practical skills on data and
AI programming is the foundation for the
capacity to design and build AI systems,
especially for students who have strong
interests and abilities in the field. The ‘AI
techniques and applications’ aspect implies
AI competency framework for students – Chapter 3: Structure of the AI competency framework for students
that students are expected to look into
exemplar AI tools to gain insight on how AI
is developed, based on data and algorithms.
Students will synchronically acquire skills
in AI programming and reinforce the
transferability of their knowledge and skills
by applying them to the crafting of AI tools.
In the stream of the three progression levels,
students are also expected to integrate
ethical, cultural and social parameters, and
solidify the interdisciplinary foundational
knowledge and skills in science, technology,
engineering, mathematics, arts, languages
and social studies.
AI foundations: Students are expected
to be able to build basic knowledge and
skills on AI, particularly with respect to
data and algorithms, understanding
the importance of the interdisciplinary
foundational knowledge required to
gradually deepen understanding of data
and algorithms. Students should also be
able to connect conceptual knowledge on
AI with their activities in society and daily
life, concretizing a human-centred mindset
and ethical principles by understanding how
AI works and how AI interacts with humans.
Application skills: Students are expected
to be able to construct an age-appropriate
understanding of data, AI algorithms and
programming, as well as acquire transferable
application skills. Students are expected
to be able to critically evaluate and
leverage free and/or open-source AI tools,
programming libraries and datasets.
Creating AI tools: Students are expected to
be able to deepen and apply knowledge and
skills on data and algorithms to customize
existing AI toolkits to create task-based AI
tools. Students are expected to integrate
their human-centred mindset and ethical
considerations into the assessment of
existing AI resources. They are also expected
to develop the social and emotional skills
needed to engage in creating with AI,
including through adaptivity, complex
communication and teamwork skills.
AI system design
Competency aspects
• AI system design
Progression levels
Understand
Apply
Create
• Problem scoping
• Architecture design
• Iteration and
feedback loops
The aspect of ‘AI system design’ focuses
on the systemic design thinking and
comprehensive engineering skills required
for problem scoping, design, architecture
building, training, testing and optimization
of AI systems. This aspect aims to challenge
the explainability of AI systems and to enable
exploratory learning for students who will
pursue further programmes of study in the
field. Students are also expected to deepen
and practise ‘ethics by design’. Although
the systemic design thinking methodology,
associated human-centred values and ethical
principles, and required knowledge and
skills on AI may be embedded in all other
aspects of students’ AI competencies, this
aspect mainly targets students who have a
25
AI competency framework for students – Chapter 3: Structure of the AI competency framework for students
particular interest in, and commitment to,
deepening their knowledge and skills in this
field.
Problem scoping: Students are expected
to be able to understand the importance of
‘AI problem scoping’ as the starting point
for AI innovation. They are expected to
be able to examine whether AI should be
used in particular situations, from a legal,
ethical and logical perspective; and to
define the boundaries, goals and constraints
of a problem before attempting to train
an AI model to solve it. Students are also
expected to acquire the knowledge and
project-planning skills needed in order
to conceptualize and construct an AI
system, including the ability to assess the
appropriateness of different AI techniques,
define the need for data, and devise test and
feedback metrics.
Architecture design: Students are expected
to be able to cultivate basic methodological
knowledge and technical skills to configure
a scalable, maintainable and reusable
architecture for an AI system covering layers
of data, algorithms, models and application
interfaces. Students are expected to develop
26
the interdisciplinary skills necessary to
leverage datasets, programming tools
and computational resources to construct
a prototype AI system. This includes the
expectation that they apply deepened
human-centred values and ethical principles
in their configuration, construction and
optimization.
Iteration and feedback: Students are
expected to enhance and apply their
interdisciplinary knowledge and practical
methods to evaluate the appropriateness
and methodological robustness of an AI
model and its impact on individual users,
societies and the environment. They should
be able to acquire age-appropriate technical
skills to improve the quality of datasets,
reconfigure algorithms and enhance
architectures in response to results of tests
and feedback. They should be able to apply
a human-centred mindset and ethical
principles in simulating decision-making on
when an AI system should be shut down and
how its negative impact can be mitigated.
They are also be expected to cultivate their
identities as co-creators within the wider AI
community.
AI competency framework for students – Chapter 4: Specifications of AI competencies for students
Chapter 4: Specifications of AI competencies
for students
The following specifications of the AI CFS
clarify what each competency block entails
in terms of curricular goals, desirable
pedagogical methods and required learning
environments, with consideration given to
inclusivity as well as variation in levels of AI
readiness.
The specifications outlined below are
based on the assumption that students’
AI competencies are the result of the
integrated interventions of national AI
curricula; extracurricular programmes;
informal learning through various media,
including the internet; and engagement
with families and local communities. To
guide the development of an AI curriculum,
the AI CFS specifies the expected learning
and behavioural outcomes of a formal AI
curriculum while considering the impact
of informal learning in social contexts. AIrelated learning – introduced into curricula
as a specific subject, or as modules within
related disciplines, such as computer
science or information and communication
technology (ICT) – should be allocated
adequate instructional time within a
semester, or preferably, across multiple
semesters.
The specified curricular goals outline
domain-specific values, knowledge and
skills that can be applied to students at a
range of ages and ability levels, who are
exposed to AI-related learning for the first
time. It is up to national or institutional
curriculum agencies to define concrete
learning objectives for specific student
cohorts, based on their AI readiness and
that of their teachers, available instructional
time and local learning environments. The
specifications include recommendations
for configuring these environments in line
with the curricular goals, with regard to
inclusivity, the potential of open-source
options, and the sharing of AI resources with
academic institutes and the private sector.
Finally, the specifications also propose
pedagogical methodologies for specific
domains of AI at a certain progression level.
These may inspire teachers and students to
explore agile methods of delivery that are
relevant for specific contexts and needs.
4.1 Level 1: Understand
The overall goal of this level is to support all
students to acquire an understanding of
what AI is and to construct age-appropriate
interpretations of the values, ethical
issues, concepts, processes and technical
methods underlying AI tools and their uses.
Students should also be supported to make
connections between their knowledge of
AI and real-life experiences, and between
domain-specific knowledge of AI and
knowledge of related learning areas.
The curricular goals outlined in Table 2 help
to map the set of foundational values, ethical
principles, knowledge and understanding
that can ensure the proper and effective
use of AI by students – an ability sometimes
referred to as ‘AI literacy’. The suggested
pedagogical methods are designed to
27
AI competency framework for students – Chapter 4: Specifications of AI competencies
facilitate age- and domain-appropriate
teaching and learning practices that can
potentially stimulate students’ interests and
support their learning trajectory on the basis
of concrete tools, personal experiences, and
28
real-world use scenarios. The specifications
also recommend basic learning settings,
which include practising with unplugged
and low-tech options.
AI competency framework for students – Chapter 4: Specifications of AI competencies for students
Table 2. Competency blocks for level 1: Understand
STUDENT
COMPETENCY
Humancentred
mindset
CURRICULAR GOALS
(AI curricula or programmes
of study should…)
SUGGESTED
PEDAGOGICAL METHODS
LEARNING
ENVIRONMENTS
(Institutions and teachers
can consider and adapt the
following learning methods.)
(The following learning
settings can be
provided and adapted.)
• CG4.1.1.1 Foster an
• Visualizing the abstract
• Unplugged
understanding that AI
concept of human agency
learning settings
• Students are
throughout the AI life
is human-led: Based on
like paper-based
expected to be
selected AI tools, explain to cycle: Ask students to draw
articles, printed
able to recognize
concept maps of human
students that AI is humanreading materials
that AI is humanagency in key steps of the
led; facilitate students to
and worksheets.
led and that the
life cycle of selected AI tools,
develop a stepwise and
• Locally available
decisions of the AI
including data ownership,
integral comprehension of
creators influence
respecting data privacy when AI tools including
human agency which may
mobile phones with
how AI systems
collecting and processing
cover principles on data
AI applications.
impact human
ownership and data privacy, data, explainability of AI
rights, human–AI
algorithms and AI models,
protection of human rights
• Predownloaded
interaction, and
in collecting and processing human-controlled evaluation or recorded videos
their own lives
of AI outputs, and human
data, explainability of AI
and other resources
and societies. They methods, human control in
determination in AI-assisted
related to specific
are expected to
decision-making. The
deployment, and human
case studies, or
understand the
concept maps should also
determination in using AI
scenarios that
implications of
reflect on the potential
for decision-making. Guide
present a dilemma.
protecting human
students to understand that consequences of a loss of
agency throughout AI cannot replace human
human agency at each step, • Search engines,
the design,
online videos and
for the individual and for
thinking or intellectual
provision and use
supplemental
society.
development.
of AI. Students will
online learning
• Simulating an AI Act
understand what it • CG4.1.1.2 Facilitate an
courses.
courtroom debate to
understanding on the
means for AI to be
evaluate creators’ intents
human-controlled, necessity of exercising
underlying prohibited AI
sufficient human
and what the
systems: Based on an agecontrol over AI: Expose
consequences
appropriate interpretation
students
to
real-world
could be when that
of the definition of AI
scenarios and guide
is not the case.
systems prohibited under
students to experience the
the European Union’s AI Act,
consequences of human
organize students to act as
oversight in controlling AI
(e.g. weak regulations failing jury members to evaluate
selected examples of AI
to prevent the design and
systems that are due to be
production of harmful AI
tools, the institutional use of prohibited under the AI
Act, deliberating on what
AI to substitute for humans
their creators’ intents and
when making high-stakes
motivations may have been.
decisions, and the absence
Help students understand
of human validation of the
how these systems can do
accuracy of AI outputs).
harm to humans, especially
Help students to grasp
by undermining human
the necessity of exercising
agency: for example, an
human control over AI
4.1.1 Human agency
29
AI competency framework for students – Chapter 4: Specifications of AI competencies for students
STUDENT
COMPETENCY
Humancentred
mindset
CURRICULAR GOALS
(AI curricula or programmes
of study should…)
systems at regulatory,
institutional and individual
levels to protect human
safety, morality and dignity.
SUGGESTED
PEDAGOGICAL METHODS
LEARNING
ENVIRONMENTS
(Institutions and teachers
can consider and adapt the
following learning methods.)
(The following learning
settings can be
provided and adapted.)
AI system may deploy
techniques to weaken
a person’s awareness or
purposefully impair their
ability to make an informed
decision.
• CG4.1.1.3 Nurture critical
thinking on the dynamic
relationship between
• Scenario-based
human agency and
understanding of humanmachine agency: Expose
controlled interaction
students to real-world cases with AI: Select examples or
in which AI can support
scenarios in which AI tools
human agency and human
are used in workplaces or
decision loops, support
daily life, denoting what
students to understand
they and their human users
how humans can properly
are contributing to the
interact with AI to enhance
target task units. Encourage
human capacities. Guide
students to recognize the
students in holding conflict- contribution AI can make
based debates on dynamic
in scenarios where human
boundaries between human capabilities and intelligence
agency and AI agency,
may have limitations,
revealing situations in
underlining the importance
which a certain extent of
of using AI to enhance
machine agency might
human capacities while
be needed (e.g. detecting
ensuring human control.
medical patterns that are
• Debating the dynamic
undetectable for human
boundary between
doctors in diagnosing rare
human agency and
diseases, auto spell check
machine agency: Based
and autocorrection when
on the real-world cases
humans draft reports, auto
of dilemmas surrounding
captioning or automating
humans’ reliance on machine
video-production in the
agency, encourage students
development of course
to conduct a debate on the
materials, automatic
changing roles humans and
language translation, etc.).
AI may play in AI-supported
Foster a critical view that
problem-solving and
while human agency must
decision-making processes.
be upheld when using AI to
Guide student to visualize
make high-stakes decisions,
the abstract boundaries
the relationship between
between human agency and
human and machine agency
machine agency in various
in real-world situations
contexts.
should be examined based
on the specific needs and
contextual factors involved.
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AI competency framework for students – Chapter 4: Specifications of AI competencies for students
STUDENT
COMPETENCY
Ethics of AI
CURRICULAR GOALS
(AI curricula or programmes
of study should…)
4.1.2 Embodied ethics • CG4.1.2.1 Illustrate
SUGGESTED
PEDAGOGICAL METHODS
LEARNING
ENVIRONMENTS
(Institutions and teachers
can consider and adapt the
following learning methods.)
(The following learning
settings can be
provided and adapted.)
• Case studies on scenarios
dilemmas around AI and
containing controversies
• Students are
identify the main reasons around AI: Present ageexpected to be able behind ethical conflicts:
appropriate real-world or
to develop a basic
Based on concrete AI tools,
simulated scenarios, and
understanding of
guide students to surface
guide students to surface
the ethical issues
dilemma decisions that
controversies surrounding
around AI, and the
individual or corporate
the AI tools and their uses.
potential impact
creators need to make in the Discuss the main reasons
of AI on human
design and development of behind such ethical conflicts
rights, social
AI (e.g. maximizing the scale and facilitate students to
justice, inclusion,
of data collection versus
draw infographics or concept
equity and climate
protecting data ownership,
maps illustrating the core AI
change within their recording users’ private
ethical principles.
local context and
data for the training of AI
• Individual or group
with regard to their models versus protecting
reflection on the personal
personal lives. They their privacy, promoting
implications of ethical
will understand,
machine control to generate
dilemmas: Engage students
and internalize
profit versus guaranteeing
in group discussion and
the following key
the primacy of human
opinion taking on ethical
ethical principles,
agency, and prioritizing AI
dilemmas that may arise
and will translate
safety versus accelerating
from uses of AI in daily life
these in their
the iteration of AI). Support
and learning in local contexts
reflective practices students to associate
(e.g. whether large language
and uses of AI tools perspectives on these
models should use the
in their lives and
dilemmas with the reasons
data of local communities
learning:
behind ethical conflicts
in their training or not;
around AI.
• Do no harm:
to what extent AI has a
Evaluating
negative environmental
• CG4.1.2.2 Facilitate
AI’s regulatory
scenario-based
impact or mitigates climate
compliance and
understandings of ethical change; how much of their
potential to
principles on AI and their
privacy users should forego
infringe on human
personal implications:
to exchange benefits of AI
rights
Offer students opportunities services). Guide students
to discuss age-appropriate
to present their opinions
• Proportionality:
real-world cases around
through age-appropriate
Assessing AI’s
the
six
core
AI
ethical
formats such as essays,
benefits against
principles: (1) ‘do no
posters, drawings or
risks and costs;
storyboards.
evaluating context- harm’, 2) proportionality,
(3)
non-discrimination,
appropriateness
• Searching for and
(4) sustainability, (5)
validating examples of
• Non-discrimination: human determination,
‘AI for the public good’:
Detecting biases
and (6) transparency
Organize individual or
and promoting
and explainability. Guide
group scoping of examples
inclusivity and
students to build a
of AI tools or approaches
sustainability
knowledge framework on
to the use of AI that
the ethics of AI and practice
• Unplugged
learning settings
and materials
including print
stories or case
studies, worksheets
and posters.
• Locally available
AI tools including
those available
through mobile
phone apps.
• Predownloaded or
recorded videos
and other resources
related to specific
cases or scenarios
that present a
dilemma.
• Search engines,
online videos or
resources related to
case studies.
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AI competency framework for students – Chapter 4: Specifications of AI competencies for students
STUDENT
COMPETENCY
Ethics of AI
(understanding AI’s
environmental and
societal impacts)
CURRICULAR GOALS
(AI curricula or programmes
of study should…)
them in evaluating the AI
tools being used in their
lives and schools.
• CG4.1.2.3 Guide the
• Human
embodied reflection and
determination:
internalization of ethical
Emphasizing
human agency and principles on AI: Guide
students to understand
accountability in
the implications of ethical
AI use
principles on AI for their
• Transparency:
human rights, data
advocating for
privacy, safety, human
the rights of users
agency, as well as for
to understand AI
equity, inclusion, social
operations and
justice and environmental
decisions
sustainability. Guide
students to develop
embodied comprehension
of ethical principles; and
offer opportunities to reflect
on personal attitudes that
can help address ethical
challenges (e.g. advocating
for inclusive interfaces
for AI tools, promoting
inclusion in AI and reporting
discriminatory biases found
in AI tools).
SUGGESTED
PEDAGOGICAL METHODS
LEARNING
ENVIRONMENTS
(Institutions and teachers
can consider and adapt the
following learning methods.)
(The following learning
settings can be
provided and adapted.)
support the public good,
including promoting
equity and inclusion for
people with disabilities,
preserving linguistic and
cultural diversity, and
increasing social justice and
environmental sustainability.
Guide students to collect
evidence on and discuss
examples that genuinely
serve the public good;
validate and categorize these
examples.
• CG4.1.3.1 Exemplify the • Example-based definition • Unplugged
definition and scope of
and scope of AI: Investigate learning settings
and experiment with
AI: Based on examples
and resources,
expected to
examples of AI tools (e.g.
of AI tools (e.g. for facial
including
develop basic
in the medical field using
recognition, social media
textbooks, essays,
knowledge,
supervised learning and
recommendations, pattern
worksheets and
understanding
image classification for
analyses underlying
printed materials.
and skills on AI,
cancer diagnosis, or in
scientific data, medical
• Online or
particularly with
business contexts using
diagnoses, self-driving cars
downloaded
respect to data and and predicting the risk of
natural language processing
videos and other
algorithms, and
and
generative
AI
for
loan defaults), facilitate
media introducing
understand the
automated
minute-taking
students to understand
AI innovations or
importance of the
and composing literature
what AI is and is not; guide
tools.
interdisciplinary
reviews).
Based
on
selected
students to find and share
foundational
examples, help students
exemplar tools under
• Locally available
knowledge
to understand what AI is
the main categories of AI
AI tools including
required for
and is not, and the main
technologies and explain
basic AI-assisted
gradually
categories of AI technologies
deepening
adopted in daily life, as well
AI techniques 4.1.3 AI foundations
and
• Students are
applications
32
AI competency framework for students – Chapter 4: Specifications of AI competencies for students
STUDENT
COMPETENCY
AI techniques
and
applications
CURRICULAR GOALS
(AI curricula or programmes
of study should…)
SUGGESTED
PEDAGOGICAL METHODS
LEARNING
ENVIRONMENTS
(Institutions and teachers
can consider and adapt the
following learning methods.)
(The following learning
settings can be
provided and adapted.)
understanding
their main functions and
of data and
techniques in an agealgorithms.
appropriate manner.
Students should
• CG4.1.3.2 Develop
also be able to
conceptual knowledge on
connect conceptual
how AI is trained based
knowledge on AI
on data and algorithms:
with their activities
Foster students’ examplein society and daily
based abstraction of
life, concretizing
conceptual knowledge
a human-centred
on how machine-learning
mindset and ethical
models are trained using
principles through
data and algorithms;
an understanding
help students to develop
of how AI works
an age-appropriate
and how AI
understanding of the three
interacts with
types of AI algorithms,
humans.
namely, supervised learning,
unsupervised learning and
reinforcement learning. This
should include how data
behind the three types of
AI algorithms are acquired
and labelled. Debunk the
claims that AI will automate
the programming of
algorithms and that humans
do not need to learn about
algorithms.
as in economic and social
applications
activities. Guide students
installed on
to explore the key steps
smartphones.
of the AI life cycle; where
appropriate, draw a diagram • Online AI tools,
for example image
of the cycle for particular AI
and/or video
systems and label the key AI
creators, generative
techniques used.
AI model and video
• Spiral learning from
recommendations
examples to abstract
on social media.
concepts and from
concepts to specific
techniques: Use selected
examples to guide
students to abstract how
a machine learning model
is trained, including
the steps of problem
definition, data collection,
data processing, training,
evaluation, deployment
and iteration based on tests
and feedback. Support
students’ development of
age-appropriate knowledge
about (and where possible,
basic operational skills on)
the use of AI techniques
involving datasets,
algorithms, AI architectures,
setting up of computing
• CG4.1.3.3 Foster openenvironments, design of
minded thinking on AI
functionalities and interfaces,
and an interdisciplinary
and planning of deployment
foundation for AI: Enable
students to gain appropriate scenarios.
knowledge on AI methods
• Case analysis of innovative
and research topics such as
AI tools and innovative
the uses of artificial neural
uses of AI: Organize
networks and the difference students to search for
between strong AI and
potential innovative AI tools
weak AI. Offer extended
and/or innovative uses of AI;
learning opportunities on
guide students to identify
data and algorithms to
the key techniques and main
students who have strong
categories of AI used in these
interests and abilities
applications. Facilitate them
in AI. Guide students to
to write an argumentative
understand the interplay
essay or provide an oral
between knowledge
defense on the extent to
33
AI competency framework for students – Chapter 4: Specifications of AI competencies for students
STUDENT
COMPETENCY
AI techniques
and
applications
CURRICULAR GOALS
(AI curricula or programmes
of study should…)
SUGGESTED
PEDAGOGICAL METHODS
LEARNING
ENVIRONMENTS
(Institutions and teachers
can consider and adapt the
following learning methods.)
(The following learning
settings can be
provided and adapted.)
on AI and knowledge
in STEM, languages and
social studies, and invite
them to solidify related
interdisciplinary knowledge
and the reflections on the
reciprocal impact of AI on
related subjects.
• CG4.1.3.4 Concretize
•
human-centred
considerations in the
design and use of AI:
Organize tool-based
reflections on AI to give form
to students’ understanding
of its impact on life, work
and societal relationships.
Highlight humans’ roles
in the key steps of the AI
life cycle (e.g. researchers,
architecture engineers, data
engineers, data workers,
beta testers, regulators of
ethics and safety, specialists
in human–AI interfaces
and auditors of system
compliance). Guide students
toward a deep familiarity
with the main ethical issues
related to the use of data for
training AI systems.
34
which these AI technologies
may help humans engineer
innovations in their personal
practices, economic or
business models, or social
services, and/or the risks that
specific AI technologies may
pose to ethical principles and
human agency.
Solidifying
multidisciplinary
foundation for AI with
a specific focus on
mathematics: Based on
lectures and problem-based
inquiry, help students
grasp that modern AI
systems are rooted in
mathematics, and learning
about data and algorithms
requires a strong command
of mathematics and a
multidisciplinary knowledge
set. Nurture students’
essential mathematical
and interdisciplinary
skills for AI development,
including relevant material
on algebra, probability and
statistics, data structures and
algorithms such as K-nearest
neighbours, K-means
clustering, linear regression
and CART/decision trees.
Cultivate students’ higherlevel knowledge on linear
algebra for complex data
representation and matrix
mathematics, calculus
for back propagation
and gradient descent for
understanding machine
learning and neural
networks. Support students
to solidify and extend their
other multidisciplinary
foundational knowledge as
well, especially in science,
technology and engineering.
AI competency framework for students – Chapter 4: Specifications of AI competencies for students
STUDENT
COMPETENCY
AI system
design
CURRICULAR GOALS
(AI curricula or programmes
of study should…)
SUGGESTED
PEDAGOGICAL METHODS
LEARNING
ENVIRONMENTS
(Institutions and teachers
can consider and adapt the
following learning methods.)
(The following learning
settings can be
provided and adapted.)
4.1.4 Problem scoping • CG4.1.4.1 Scaffold critical • Simulating the review of
thinking skills on when
AI should not be used:
Drawing from examples,
guide students to develop
critical analysis skills to
examine reasons why AI
should or should not be
used to address certain
real-world challenges (e.g.
improving institutional
productivity, the
sustainable development
of communities, or the
precision and efficiency of
human decision-making)
with reference to human
and environmental
implications. Provide clarity
on when, and under what
conditions, AI cannot and/
or should not be applied
•
to problems (e.g. where
non-AI solutions would
offer the same performance
with lower ethical risk and
environmental impact,
or where the use of AI
would weaken human
consciousness or manipulate
human actions).
• Unplugged
project proposals: Organize learning settings
students to simulate the
including
review of a project proposal
worksheets, paperand justification process. The based case studies,
proposals could, for example, and printouts of
be on building or selecting
prototypes or
an AI system. Conduct a
plans for AI system
debate on whether AI should design.
or should not be used in the
• Digital devices
project to solve the problem,
with an internet
considering factors such as
connection.
the availability of sufficient
training data, ethical
• Selected online AI
implications, environmental
systems.
impact and whether non-AI
solutions could achieve
similar outcomes with fewer
risks. Guide students to
outline a checkbox for the
review.
• Students are
expected to be able
to understand the
importance of ‘AI
problem scoping’ as
the starting point
for AI innovation.
They are expected
to be able to
examine whether
AI should be used
in certain situations
from legal, ethical
and logical
perspectives;
students are able
to define the
boundaries, goals
and constraints of
a problem before
Simulating the problemattempting to train
scoping and justification
an AI model to
for the design of new AI
solve it; students
system: Facilitate students
are also expected
to research problems in their
to acquire the
daily lives or communities
knowledge and
(e.g. at school or in volunteer
project-planning
work) and identify a problem
skills needed
that could potentially
in order to
be addressed by AI (e.g.
conceptualize and
automatic watering the
• CG4.1.4.2 Support
construct an AI
the acquisition and
school garden or helping a
system, including
reinforcement of skills in
hard-of-hearing grandparent
by assessing the
scoping a problem to be
to detect alarms). Support
appropriateness
solved by an AI system:
students to scope and define
of different AI
Based on a simulation
the problem by anticipating
techniques,
project, support the
the key features including
defining the need
learning and practice of
AI algorithms and datasets,
for data, and
skills to identify and scope
and produce a corresponding
devising test and
a problem that should and
problem statement.
feedback metrics.
could possibly be solved by
• Data preprocessing lab:
building a new AI model
Using a basic dataset and the
(e.g. training an AI model
architecture of an existing AI
on a minority language to
better serve its community, model, organize experiments
on training the model
or building a model for
35
AI competency framework for students – Chapter 4: Specifications of AI competencies for students
STUDENT
COMPETENCY
AI system
design
CURRICULAR GOALS
(AI curricula or programmes
of study should…)
the automated tracking
of migration across target
regions). Students can
sharpen their analytical skills
by formulating problem
statements that can help
avoid wastage of time and
effort on poorly defined
problems.
• CG4.1.4.3 Develop skills
on assessing AI systems’
need for data, algorithms
and computing resources:
Offer opportunities for
students to develop
planning skills by
assessing the need for
data, algorithms and
programming languages,
software, computing
capabilities and hardware;
study the feasibility of an
AI project in terms of the
data available given the
regulatory and ethical
restrictions and the total
costs of the required
processing and engineering
of data, computing
capabilities and hardware.
36
SUGGESTED
PEDAGOGICAL METHODS
LEARNING
ENVIRONMENTS
(Institutions and teachers
can consider and adapt the
following learning methods.)
(The following learning
settings can be
provided and adapted.)
based on variations of the
dataset (e.g. a challenge of
classifying mystery images).
Support students to apply
various data preprocessing
techniques, such as adjusting
the coding (e.g. data
augmentation, handling
outliers and analysing
dataset skew/imbalance).
Support them to train the
model based on the modified
datasets and observe how
the data preprocessing
has affected the model’s
performance.
AI competency framework for students – Chapter 4: Specifications of AI competencies for students
4.2 Level 2: Apply
The overall goal of the ‘Apply’ level is for
students to construct a solid and transferable
conceptual knowledge structure and
associated skill sets on AI and to habituate
their application of the human-centred
mindset and ethical principles to guide
the assessment, learning and practice of AI
tools. The curricular goals in Table 3 aim
to guide the charting of a core set of value
orientations, practical ethical principles
and methodological knowledge that can
be used to tailor curricular modules and
specify exit competencies for all students.
The suggested pedagogical methods are
intended to catalyse problem-based inquiry
of conceptual knowledge and task-based
appreciation of operational skills while
integrating strategies to maintain students’
curiosity for further study. Providing desirable
learning environments at the ‘Apply’ level
involves setting up hardware, software
and applications to support practices
of AI operation and co-creation, with
considerations of open source options.
Table 3. Competency blocks for level 2: Apply
STUDENT
COMPETENCY
Humancentred
mindset
4.2.1 Human
accountability
• Students are
expected to be
able to recognize
that human
accountabilities
are the legal
obligations of AI
creators and AI
service providers,
and understand
what human
accountabilities
they should
assume during the
design and use of
AI. They should also
foster an awareness
that human
accountability is
a legal and social
responsibility when
using AI to assist
decisions on that
affect humanity
CURRICULAR GOALS
(AI curricula or programmes
of study should…)
• CG4.2.1.1 Develop a view
that human accountability
is a legal obligation of AI
creators and AI service
providers: Leveraging
prior knowledge on the
human-led AI life cycle and
real-world lawsuits, guide
students to understand
that human AI creators
and service providers, and
institutions deploying AI
tools, are accountable for
legal issues, violations and
infringements that the
AI system or service may
cause. Explain how to hold
AI creators, providers and
institutional users to assume
human accountability for
safety incidents, ethical
risks in designing and
training AI, and misuses
of the AI service to control
users. Guide students to
understand what human
accountabilities they should
SUGGESTED
PEDAGOGICAL METHODS
LEARNING
ENVIRONMENTS
(Institutions and teachers
can consider and adapt the
following learning methods.)
(The following learning
settings can be
provided and adapted.)
• Writing guidelines on
• Unplugged
human accountability for
and/or offline
AI creators and service
learning settings
providers: Facilitate
and resources,
students to play the roles of
including printAI creators and data owners
based case studies,
and discuss their key legal
role-play scripts,
and ethical accountabilities
videos, worksheets
in terms of maintaining
and flipcharts.
human control of the
• Online AI tools, for
collection and processing
example learning
of data, training AI models,
designing functionalities and management
systems, social
interfaces, deployment of
media platforms
AI systems, and monitoring
and generative AI
and feedback loops. Guide
platforms.
them to write self-discipline
guidelines for their studies
on the design, training
and iteration of AI systems,
holding AI creators to
account for protecting the
rights of data owners and
AI users.
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AI competency framework for students – Chapter 4: Specifications of AI competencies for students
STUDENT
COMPETENCY
Humancentred
mindset
38
CURRICULAR GOALS
(AI curricula or programmes
of study should…)
SUGGESTED
PEDAGOGICAL METHODS
LEARNING
ENVIRONMENTS
(Institutions and teachers
can consider and adapt the
following learning methods.)
(The following learning
settings can be
provided and adapted.)
and uphold the
assume themselves when • Investigating the impact
of AI-assisted decisions
principle that
learning how to create AI
on humans and avenues
humans should
tools or design AI systems.
of redress within AI
not cede the
• CG4.2.1.2 Generate
regulations: Ask students
determination to
the understanding that
to find examples in which
AI when making
human accountability
decisions about humans
high-stakes
is a legal and social
are determined or greatly
decisions. They
responsibility when using
influenced by AI (e.g. an AIare also expected
AI in making decisions
assisted assessment system
to enhance their
about humanity: Guide
used by a bank to approve
judgement on,
students to analyse the
or deny a student loan
and attitudinal
capabilities of AI tools used
application, or a profiling
resilience to, the
to assist decision-making.
system used by a hotel to
illusive claims on
Critically interrogate the
predict a person’s socioabout the use of
genuine capabilities of
economic background based
outputs and as
certain AI tools and debunk
on their location and the
well as predictions
the hype around AI’s
device they were using when
that AI can usurp
supposed ability to make
they made their booking).
humans’ thinking
decisions. Assist students to
Facilitate students to reveal
and decisionevaluate the consequences
the roles of humans and AI
making.
of the institutional use
in the key steps of decision
of AI to make decisions
loops, and check whether
about humans in complex
human accountability for the
situations such as profiling
decisions is in compliance
the aptitude of students
with locally applicable or
to take up further
international regulations (e.g.
learning opportunities
the EU AI Act).
or determining the
employability of job
• Scenario-based practices
candidates. Lead
of using AI with purpose:
discussions on why human
Engage students in activities
accountability in using AI
where they use AI tools
is essential to safeguard
to purposefully practise
human rights and human
their writing skills and
dignity. Facilitate students
foster their inquiry-based
to understand why we
learning, higher-order
should not use AI to replace thinking and creativity. Lead
humans when making
students to discuss how the
high-stakes decisions,
use of AI without human
for example to assess the
accountability (e.g. handing
values, infer the emotions
in an essay produced by
or predict the aptitudes
AI) may reduce human
of a natural person. AI
intellectual development.
algorithms should not be
Prompt them to outline
used to assign students’
concrete actions to protect
themselves and their peers
from the use of AI outputs
AI competency framework for students – Chapter 4: Specifications of AI competencies for students
STUDENT
COMPETENCY
CURRICULAR GOALS
(AI curricula or programmes
of study should…)
scores (as happened during
COVID-19) or decide on
university admissions.
Humancentred
mindset
• CG4.2.1.3 Nurture the
personal attitude that
human accountability
requires personal
competencies to steer the
purposeful use of AI: Guide
students to interrogate
how the automation of
literature reviews, writing
and artistic creation may
undermine human thinking
processes and intellectual
development. Guide
students to discuss concrete
actions that they can take
to protect themselves and
their peers from the use of
AI outputs or predictions
to usurp human thought,
intellectual practices
and continuous capacity
enhancement.
Ethics of AI
• CG4.2.2.1 Foster
self-awareness and
habitual compliance
• Students are
with ethical principles
expected to be
for the responsible use
able to carry
of AI: Illustrate the ethical
out responsible
principles or regulatory
AI practices in
articles concerning the
compliance
responsible use of AI,
with ethical
drawing on concrete AI
principles and
tools and real-world use
locally applicable
scenarios. Support students
regulations. They
to iteratively build and
are expected to be
update a checkbox of ethical
conscious of the
principles for ensuring their
risks of disclosing
own lawful and responsible
data privacy and
practices when engaging
take measures to
with AI systems. Guide
ensure that their
students to practise and
data are collected,
habituate their compliance
used, shared,
with these principles, such
archived and
4.2.2 Safe and
responsible use
SUGGESTED
PEDAGOGICAL METHODS
LEARNING
ENVIRONMENTS
(Institutions and teachers
can consider and adapt the
following learning methods.)
(The following learning
settings can be
provided and adapted.)
or predictions to usurp
thinking processes, and
give them insight into the
competencies that students
need in order to steer the use
of AI toward serving human
capability development.
• Designing an ‘ethics kit’
• Unplugged
for the self-disciplined,
learning settings
responsible use of AI:
and resources,
Design simulated scenarios
including papercontaining potential ethical
based worksheets,
conflicts (e.g. sharing
posters and
private data or protected
checklists of ethical
content when chatting
principles.
with AI systems, putting
• Predownloaded
AI-generated content
privacy policies
in a school assignment,
and AI regulations,
creating a video using
and examples of
images of other people, or
legal or ethical
distributing misinformation,
cases concerning
disinformation or hate
AI safety, data
speech). Organize the
privacy and forms
drafting of an ‘ethics kit’ that
of consent.
users need to habitually
check when using AI,
• Locally available
including articles drawn
AI tools including
from locally applicable
smartphone apps.
39
AI competency framework for students – Chapter 4: Specifications of AI competencies for students
STUDENT
COMPETENCY
Ethics of AI
CURRICULAR GOALS
(AI curricula or programmes
of study should…)
deleted only with
as protecting personal data
their deliberate
and privacy, respecting
and informed
copyright, clearly marking
consent. They are
where AI-generated content
also expected to be appears, and avoiding
conscious of typical inputs or interactions
AI incidents and
in AI systems that
the specific risks of involve disinformation,
certain AI systems,
misinformation, hate speech
and be able to
or sensitive details about
protect their own
identifiable individuals.
safety and that of
• CG4.2.2.2 Offer
their peers when
opportunities to
using AI.
reinforce self-discipline
in the responsible use
of AI: Provide students
with opportunities to
gain an age-appropriate
understanding of their
personal, legal and ethical
responsibilities when
using AI; highlight the
consequences of violating
regulations; and build and
reinforce self-disciplined
behaviours, especially
with regard to sensitive
personal data, copyrighted
materials, images
depicting identifiable
people, content that is
AI-generated or digitally
synthetized, and the
spread of misinformation,
disinformation and hate
speech.
SUGGESTED
PEDAGOGICAL METHODS
LEARNING
ENVIRONMENTS
(Institutions and teachers
can consider and adapt the
following learning methods.)
(The following learning
settings can be
provided and adapted.)
regulations and personal
• Online AI tools
responsibilities in making
especially platforms
legal and ethically proper use containing
of AI tools. Guide students to recommender
practice the compliance with algorithms and
the principles when using AI
content generators.
without supervision.
• Simulation of typical
AI incidents and risk
management: Expose
students to simulated AI
incidents that directly harm
humans or AI hazards that
threaten harm. Familiarize
them with precautionary
and interactive strategies
for ensuring their personal
data is collected, used,
shared, archived and deleted
only with their informed
consent. Suggest tips for the
safe use of AI, and promote
awareness of the regulations
that can protect their privacy
and well-being and/or
mitigate negative impacts in
the case of AI incidents.
• Users’ reviews of AI
creators’ policies on data
privacy: Encourage students
to search for and download
examples of AI creators’
policies on data privacy.
Guide them to leverage
their knowledge on the
rights of data owners and
legal responsibilities of AI
• CG4.2.2.3 Deepen
practical knowledge on
creators to check whether
the safe use of AI and
the policies comply with
awareness of locally
relevant regulations. When
applicable regulations:
they discover a violation, ask
Facilitate students to
them to draft a complaint to
categorize the general safety the regulatory agencies
risks of AI, potential safety
and/or a recommendation for
risks of specific AI tools,
the AI creator to improve the
and typical AI incidents.
conformity of their policies
Guide students to deepen
and practices.
40
AI competency framework for students – Chapter 4: Specifications of AI competencies for students
STUDENT
COMPETENCY
CURRICULAR GOALS
(AI curricula or programmes
of study should…)
SUGGESTED
PEDAGOGICAL METHODS
LEARNING
ENVIRONMENTS
(Institutions and teachers
can consider and adapt the
following learning methods.)
(The following learning
settings can be
provided and adapted.)
their knowledge on human •
rights to data protection
and privacy and the legal
responsibilities of AI creators
to collect data with consent,
and guide them to practise
strategies for ensuring their
personal data is collected,
used, shared, archived and
deleted only with their
informed consent. Expose
students to simulated
scenarios containing typical
AI incidents, so they can
practise precautionary and
interactive strategies for the
safe use of AI and become
familiar with regulations
that can protect their safety
or mitigate the negative
impacts of AI incidents.
Ethics of AI
AI
4.2.3 Application skills • CG4.2.3.1 Offer
opportunities to
techniques
• Students are
strengthen knowledge
and
expected to be
and skills on data
applications
able to construct
an age-appropriate
knowledge
structure on data,
AI algorithms and
programming, and
acquire transferable
application skills.
Students are
expected to be
able to critically
evaluate and
leverage free and/
or open-source AI
tools, programming
libraries and
•
datasets.
modelling, engineering
and analysis: Provide
students with task-based
learning opportunities to
acquire age-appropriate
knowledge and skills on
datasets, including applying
age-appropriate tools or
programming languages to
acquire, clean and transform
data into a suitable format
for storing, processing, and
analysing databases (e.g.
SQL, NoSQL, SparkSQL or
Apache Flink).
Debate the ownership of
AI-generated content and
outputs from human–AI
interactions: Organize a
debate to trigger students’
reflections around the
ownership of content created
using AI. Examine the
availability and applicability
of regulations on the
recognition of copyright for
AI-generated content and
resources, and how relevant
regulations recognize
intellectual work that
integrates different extents
of AI-generated content.
• Data biases lab: Provide
students with sample
datasets with and without
outliers, guide students
to conduct hands-on
experimentation on how
the outliers impact the
model (e.g. in regression
or clustering examples).
For image classification,
ask students to conduct an
experiment on how class
imbalance (e.g. significantly
more data in one class than
the other) affects model
performance per class.
Guide students to learn
age-appropriate skills in
data engineering to remove
identifiable biases and
compare the results.
• Computers
with internet
connection.
• Computer-based
samples of datasets
or locally accessible
public datasets.
• Computer-based
applications for AI
programming or
locally accessible
online open-source
AI programming
libraries.
• Computer-based or
locally accessible
online AI tools.
CG4.2.3.2 Provide
opportunities to acquire
age-appropriate technical • Tailored optional modular
courses on various AI
skills in AI programming:
algorithms to support
Explain examples of AI
cohort-based learning:
systems that use different
Tailor free and/or open-source
41
AI competency framework for students – Chapter 4: Specifications of AI competencies for students
STUDENT
COMPETENCY
AI
techniques
and
applications
CURRICULAR GOALS
(AI curricula or programmes
of study should…)
SUGGESTED
PEDAGOGICAL METHODS
LEARNING
ENVIRONMENTS
(Institutions and teachers
can consider and adapt the
following learning methods.)
(The following learning
settings can be
provided and adapted.)
categories of AI algorithms
AI datasets and AI algorithm
libraries according to the age
to scaffold students’ ageand prior knowledge of target
appropriate understanding
students. Develop optional
of AI algorithms including
modular courses on various
supervised learning,
AI algorithms and support
unsupervised learning and
cohorts of students to choose
reinforcement learning.
the courses that align with
This should include how
their interests, to acquire
they scrape and process
methodological knowledge
data, how they’re trained,
and skills in applying AI
how they function, and
algorithms.
the concrete types of
algorithms that underlie
• AI hackathons based on
these categories. Where
variations of authentic
appropriate, provide
tasks: Schedule a significant
students with task-based
amount of continuous
learning opportunities to
learning hours to challenge
cultivate methodological
interested students to
knowledge on selected AI
conduct task-based
algorithms.
hackathons. Design a series
of tasks with variations
• CG4.2.3.3 Encourage
to enable students to
students to develop
practise their transferable AI
analytical and synthesis
programming skills.
skills to leverage opensource datasets and AI
• Debunking claims that
tools: Organize problemAI will automate coding
based learning to facilitate
and human students
students’ acquisition of
don’t need to learn AI
skills to critically evaluate
programming: Facilitate
and leverage open-source
students’ research into the
AI datasets (e.g. MNIST,12
professional knowledge
CIFAR,13 or ImageNet)14
and skills demanded by the
and tools from free and/or
creation and iteration of
open-source AI algorithm
AI systems, especially the
libraries (e.g. Teachable
foundation of methodological
knowledge necessary to
Machine,15 MIT App
explore more human-centred
Inventor,16 PyTorch,17 or
Keras)18 to address authentic and innovative AI algorithms
tasks. Drawing on variations and methods. Challenge
of problems, guide students students to contemplate
how using AI to replace
to practise and enhance
humans’ programming skills
the transferability of their
will lead to fewer people
knowledge and skills on
acquiring these foundational
data and algorithms into
skills, and exacerbate the
complex contexts.
inequality between those
with and without AI-related
knowledge.
42
AI competency framework for students – Chapter 4: Specifications of AI competencies for students
STUDENT
COMPETENCY
AI system
design
4.2.4 Architecture
design
CURRICULAR GOALS
(AI curricula or programmes
of study should…)
SUGGESTED
PEDAGOGICAL METHODS
LEARNING
ENVIRONMENTS
(Institutions and teachers
can consider and adapt the
following learning methods.)
(The following learning
settings can be
provided and adapted.)
• CG4.2.4.1 Scaffold
•
the acquisition of
methodological
knowledge and technical
skills on AI architecture:
Facilitate students to acquire
and practise the necessary
engineering thinking
and operational skills to
evaluate a variety of AI
architectures with an aim
to choose an appropriate
solution based on a defined
problem statement,
while considering opensource options. Provide
project-based learning
opportunities to support
their acquisition of
methodological knowledge
on the configuration of a
prototype AI architecture
encompassing an antibias data structure, an
energy-efficient AI model
to minimize the negative
environmental impact, the
human-centred design of
performance and services,
and metrics to test and
improvise the maturity of
the configuration.
• Students are
expected to be able
to cultivate basic
methodological
knowledge and
technical skills
to configure
a scalable,
maintainable
and reusable
architecture for an
AI system covering
layers of data,
algorithms, models
and application
interfaces. Students
are expected
to develop the
interdisciplinary
skills necessary to
leverage datasets,
programming tools
and computational
resources to
construct a
prototype AI
system. This
includes the
expectation
that they apply
deepened human- • CG4.2.4.2 Support the
centred values and preparation of advanced
ethical principles in technical skills and
their configuration, project management
competencies needed by
construction and
AI system building: Offer
optimization.
project-based learning
opportunities to facilitate
students to acquire and
apply the interdisciplinary
technical skills demanded
by the building of a
prototype AI system
designed for a simple
specific task (e.g. a chatbot
imitating the responses of
Simulating the
evaluation of frameworks
and components
for AI architectural
configuration: Based on
the problem statement and
feasibility study, facilitate
students to evaluate a
variety of frameworks
for AI architectures (e.g.
TensorFlow, PyTorch, or
Scikit-learn). Simulate the
evaluation and selection of
solutions to the components
of the architecture (e.g. data
layer, algorithm layer, AI
model layer and interface
layer) based on the selected
framework. Configure a
prototype architecture
encompassing the required
datasets, algorithm tools,
AI model and required
computational resources, the
design of main functionalities
and interface, and the plans
for deployment. Guide
students to communicate
the configuration through
abstractions such as
flowcharts, diagrams or
pseudocode.
• Videos and metrics
showing how to
conduct ethical
and technical
evaluations of AI
models.
• Computer-based or
locally accessible
online examples of
AI systems.
• Computer-based
samples of datasets
or locally accessible
public datasets.
• Computer-based
applications for AI
programming or
locally accessible
online open-source
AI programming
libraries.
• Locally hosted or
open-source cloud
computing and
other resources
shared by
institutions through
cloud platforms.
• Simulating the leveraging
of resources to build an AI
system: Facilitate students to
build the simulated AI system
based on locally hosted
computing devices or locally
accessible cloud computing
platforms (e.g. Hadoop or
Spark), and the operating
systems (e.g. GNU) and
software needed to train the
machine-learning models.
Guide students to conduct
trade-offs between cost and
computing capability needs,
and between the robustness
43
AI competency framework for students – Chapter 4: Specifications of AI competencies for students
STUDENT
COMPETENCY
AI system
design
44
CURRICULAR GOALS
(AI curricula or programmes
of study should…)
an experienced teacher).
Explore the leveraging
and normalization of
datasets, assembling of
virtual computational
resources, and selection and
enhancements of AI models
(e.g. hyperparameter
optimization). Guide
students to simulate the
training of a machinelearning model, including
the practical use of
computational resources
and calling of data to train
the models based on the
selected and preprocessed
datasets. Design and
arrange opportunities
for students to acquire
project management skills
including balancing the
scope of the AI systems
with the resources
available, coordinating
the division and sharing of
responsibilities, and critically
evaluating and leveraging AI
resources.
SUGGESTED
PEDAGOGICAL METHODS
LEARNING
ENVIRONMENTS
(Institutions and teachers
can consider and adapt the
following learning methods.)
(The following learning
settings can be
provided and adapted.)
of the AI models and their
environmental impact,
aiming to optimize efficiency
and minimize the waste of
computational resources.
Simulate the enhancement
of the architecture including
the optimization of
hyperparameters and/or finetuning of existing AI models
to solve simple problems
(e.g. transfer learning on top
of a pre-existing model, or
apply novel neural networks
or non-trivial modifications
to foundational models).
Practise using computational
resources and calling data
to train machine-learning
models based on the
selected and preprocessed
datasets.
AI competency framework for students – Chapter 4: Specifications of AI competencies for students
4.3 Level 3: Create
The overall goal of the ‘Create’ level is to
challenge and enable students to develop
advanced competencies to configure AI
solutions or craft new AI tools based on
customizable datasets, programming
tools or AI models, with consideration
of open-source options. Students will
also be supported to reinforce a sense of
belonging to a broader community of AI
co-creators and enhance their intellectual
engagement with the social responsibilities
that are required for being a citizen in AI
societies. The curricular goals shown in
Table 4 aim to inspire the outlining of a set
of high-level competencies composed of
advanced methodological knowledge on
AI, engineering skills for AI system design,
and adaptivity in compliance with personal
and corporate social responsibilities
when creating and testing AI systems.
The suggested pedagogical methods
and approaches are designed to help
solve ill-structured problems and nurture
higher-order thinking, including through
project-based learning, problem-based
exploration of methodological knowledge,
and multi-faceted ethical assessments. The
suggested learning environments present
recommendations on the configuration
of datasets, AI programming tools and
necessary computational devices to support
complex learning with consideration of
sharing AI resources and critically leveraging
open-source options.
Table 4. Competency blocks for level 3: Create
STUDENT
COMPETENCY
Humancentred
mindset
CURRICULAR GOALS
(AI curricula or programmes
of study should…)
• CG4.3.1.1 Foster
awareness of being a
critical AI citizen: Enable
• Students are
students’ to gain evidenceexpected to be
based insights into the
able to build
pervasive adoption of AI as
critical views on
a supporting infrastructure
the impact of AI on
of social activities in
human societies
human societies. Foster
and expand
their awareness and critical
their humanviews on challenges that
centred values to
human societies are facing,
promoting the
such as prioritizing the
design and use
acceleration of AI innovation
of AI for inclusive
while sacrificing safety and
and sustainable
inclusivity, or prioritizing
development. They
safety first, or and inclusive
should be able
access. Develop students’
to solidify their
skills in critiquing AIcivic values and
amplified biases against
the sense of social
females, marginalized
responsibility as
4.3.1 AI society
citizenship
SUGGESTED
PEDAGOGICAL METHODS
LEARNING
ENVIRONMENTS
(Institutions and teachers
can consider and adapt the
following learning methods.)
(The following learning
settings can be
provided and adapted.)
• Case studies on conflicts
• Unplugged
between an inclusive
learning settings
and just AI society and
and resources,
the threats AI poses to
including
inclusion, justice and
worksheets,
sustainability: Organize
flipcharts, reports
case studies or projector videos on
based learning on the
jobs and career
typical conflicts between an
development in AI
inclusive and just AI society
societies, and printand the risks AI poses to
based analytical
human-centred values.
case studies
Organize a discussion of
on the societal
what is meant by sustainable, implications and
inclusive and just societies.
environmental
Ask students to analyse
impact of AI.
cases where AI has been
• Online AI
pervasively embedded
systems or locally
into the infrastructure of
available AI tools
societies, and interrogate
for experiential
how AI may amplify biases,
and analytical
widen economic and social
45
AI competency framework for students – Chapter 4: Specifications of AI competencies for students
STUDENT
COMPETENCY
Humancentred
mindset
46
a citizen in an AI
society. Students
are also expected
to be able to
reinforce their
open-minded
attitude and
lifelong curiosity
about learning and
using AI to support
self-actualization in
the AI era.
CURRICULAR GOALS
(AI curricula or programmes
of study should…)
ethnic groups and socioeconomically disadvantaged
people, and the effects of
AI on social relationships,
norms and structures. Help
reveal the reasons behind
AI’s profound impact on
societies and assess how
legal, ethical and social
rules should be adapted to
respond to the challenges.
SUGGESTED
PEDAGOGICAL METHODS
LEARNING
ENVIRONMENTS
(Institutions and teachers
can consider and adapt the
following learning methods.)
(The following learning
settings can be
provided and adapted.)
inequality, undermine
human agency and worsen
climate change. Challenge
students to take and defend
positions on how existing AI
technology can be regulated
and how the design of the
next generations of AI could
be steered to make positive
contributions to the building
of inclusive and just societies.
• Inquiry on the personal
• CG4.3.1.2 Nurture
social responsibilities of
personal and social
being an AI society citizen:
responsibilities in AI
Arrange for students to
societies: Encourage
conduct group discussions
students to share their
on the rights of citizens in an
views on what desirable
AI society, and jointly outline
AI societies would look
the main obligations and
like and delineate the
responsibilities that citizens
main responsibilities and
should assume, taking into
obligations that citizens
consideration both global
need to undertake in
and local contexts, as well as
order to build an inclusive,
the perspectives of inclusion,
sustainable and just
equity, social justice, humanAI society, from the
centred purposes and
perspectives of both users
and designers of AI. Support impacts on the environment
and ecosystems. This includes
students to continuously
ensuring humans have
refine their personal
responsibilities as AI society control and accountability
citizens. Challenge students over all key steps of the AI
life cycle. Allow students
to examine challenges in
upholding ethical principles to conduct and share their
self-reflections on personal
for the design and use of
social responsibilities in an AI
AI in complex authentic
society.
situations with an aim to
reinforce the resilience
• Case studies on selfof their human-centred
actualization in AI societies
mindset.
and their implications for
lifelong learning: Organize
• CG4.3.1.3 Nurture the
sense of self-actualization case studies for students on
the adoption of AI in work,
as an AI citizen and the
lifelong learning attitude life and social practices, and
challenge them to review the
to AI: Guide students to
implications of the adoption
dynamically review the
of AI for their personal
impact of the adoption of
goals, career development
AI across sectors and the
tests including
applications on
smartphones that
provide personal
assistants, chatbots,
and intelligent
tutoring systems.
AI competency framework for students – Chapter 4: Specifications of AI competencies for students
STUDENT
COMPETENCY
Humancentred
mindset
Ethics of AI
CURRICULAR GOALS
(AI curricula or programmes
of study should…)
competency sets that living
and working in an AI society
may require. Reflect on
personal goals in a society
where AI is pervasive, and
evaluate the roles of AI in
relation to self-actualization.
Support students to build
an adaptive and persistent
attitude towards the lifelong
study of AI to support
their self-actualization and
personal contribution to the
sustainable development of
societies.
4.3.2 Ethics by design • CG4.3.2.1 Build
SUGGESTED
PEDAGOGICAL METHODS
LEARNING
ENVIRONMENTS
(Institutions and teachers
can consider and adapt the
following learning methods.)
(The following learning
settings can be
provided and adapted.)
and self-actualization.
Guide students to build
an adaptive and curious
attitude to the lifelong study
and use of AI to support
their self-actualization and
personal contribution to the
sustainable development of
societies.
• Simulating the due
awareness and
diligence of a ‘chief
• Students are
understanding on ‘ethics
ethics officer’ in an AI
expected to be
by design’: Provide
development team: Design
able to adopt an
conflict-based learning
project-based learning
ethics-by-design
opportunities so students
practices, ask students
approach to the
can apply an integral
to simulate the role of a
design, assessment set of ethical principles
chief ethics officer of an AI
and use of AI
throughout the life cycle of
company, including drafting
tools as well as
the design and creation of
a checklist of ethical criteria
the review and
AI. Guide students to assess for auditing key steps of AI
adaptation of
the ethical properness of
system design, and defining
AI regulations.
AI tools when they are
the key due-diligence
Students are
under conceptualization,
procedures to follow when
expected to be
anti-bias measures in data
overseeing the safety and
aware that the
collection and engineering, ethics of the AI system
assessment and
discrimination-free methods being designed by a team or
ratification of the
for training machine
company.
intent of the AI
learning, human-centred
• Simulating the use of
design should
‘guardrails’ for generating AI
‘ethics label’ to audit
start from the
outputs, and the inclusive
selected AI tools or
conceptualization
testing and auditing of AI
algorithms: Organize
stage and cover
tools.
students to undertake a
all steps of the AI
mock audit of ‘ethics by
life cycle. Student • CG4.3.2.2 Develop
design’ in selected AI tools
a critical attitude to
should be able to
or systems. Provide lectures
the ethics-by-design
apply parameters
principles behind
on this and support students
to assess the
existing AI systems
to research ethics labels for
compliance of an
and
algorithms:
Provide
AI systems (an ethics label
AI tool with ethical
regulations and use students with opportunities for AI systems is analogous
to a nutrition label for food
an ethical matrix of to take a holistic approach
• Unplugged
learning settings
and resources,
including
worksheets,
flipcharts and
print-based
examples of due
diligence checks
and reports, ethics
labels and matrices,
privacy policies
of AI creators and
regulations on AI.
• Locally available
AI tools including
smartphone apps.
• Online AI systems
for ethical analysis.
• Websites sharing
regulations on AI
and lawsuits or
court cases.
47
AI competency framework for students – Chapter 4: Specifications of AI competencies for students
STUDENT
COMPETENCY
Ethics of AI
multi-stakeholders
to review AI
regulations and
inform adaptation.
CURRICULAR GOALS
(AI curricula or programmes
of study should…)
SUGGESTED
PEDAGOGICAL METHODS
LEARNING
ENVIRONMENTS
(Institutions and teachers
can consider and adapt the
following learning methods.)
(The following learning
settings can be
provided and adapted.)
to applying principles
and regulations to the
evaluation of the ‘ethics
by design’ of specific AI
systems or tools. Develop
their critical thinking skills
by requesting them to
propose recommendations
to the creators of AI systems
to remedy any identified
violations of ethical
principles or regulations,
and mitigate any harms
their AI tools have caused.
• CG4.3.2.3 Cultivating
social responsibilities to
uphold ‘ethics by design’
in regulations on AI:
Drawing on selected AI
•
regulations, guide students
to evaluate how they align
with the ethics-by-design
approach and the extent
to which corresponding
measures are sufficient in
monitoring and regulating
typical ethical risks
embedded in algorithms
and AI systems. Enhance
students’ awareness of,
and skills in carrying out,
their social responsibilities
by guiding them to
recommend modifications
of existing local regulations
or draft proposals on the
development of regulations
to govern ethics by design in
their communities.
48
items). Guide students
to construct or adapt an
ethical label to audit the
intent of the designers of
selected AI systems and
services, including collecting
information beyond their
published statements (e.g.
the creators of a shoppingrecommendation platform
state that its intent is to help
customers find the most
appropriate products, while
the hidden purpose may be
to make users dependent
on or addicted to using the
platform). Write reports on
the findings of the audit.
Simulating the use of an
ethics matrix to review
regulations on AI and
suggest adaptations: Invite
students to research an
ethics matrix for involving
relevant stakeholders in
regulations on AI. Support
them to construct an
adaptive ethics matrix
with core ethical principles
forming its columns and
relevant stakeholders
forming the rows (e.g.
AI creators, regulators,
institutional deployers and
individual users). Students
can apply their matrix to
analyse relevant articles of
a selected regulation and
draft reports or reviews
including recommendations
for further adapting or
iterating the regulations.
Where local regulations
are not available, write a
proposal on the creation of
a new AI regulation with an
outline of articles for relevant
stakeholders.
AI competency framework for students – Chapter 4: Specifications of AI competencies for students
STUDENT
COMPETENCY
CURRICULAR GOALS
(AI curricula or programmes
of study should…)
SUGGESTED
PEDAGOGICAL METHODS
LEARNING
ENVIRONMENTS
(Institutions and teachers
can consider and adapt the
following learning methods.)
(The following learning
settings can be
provided and adapted.)
AI
4.3.3 Creating AI tools • CG4.3.3.1 Challenge and • Task-based enhancement
enable advanced skills
of datasets and
techniques
• Students are
to develop task-based AI
programming codes
and
expected to be
tools: Provide task-based
for crafting an AI tool:
applications
able to deepen and learning opportunities
apply knowledge
so students can transfer
and skills on data
their values, knowledge
and algorithms to
and skills to crafting an AI
customize existing
tool based on existing AI
AI toolkits to
models or toolkits. Support
create task-based
their mastery of advanced
AI tools. Students
skills in critically analysing
are expected to
the relevance of existing
integrate their
AI tools to specific tasks,
human-centred
assessing its data collection
mindset and ethical and processing needs,
considerations into deciding on whether a
the assessment
low-code approach will be
of the existing AI
adopted or AI algorithms
resources and the
and programming language
test of self-created
are required, and carrying
AI tools. They are
out the operational
also expected
customization and/or
to foster social
programming.
and emotional
• CG4.3.3.2 Enhance
skills needed to
students’ creativity in
engage in creation
applying AI knowledge
with AI including
adaptivity, complex and skills to customize
AI toolkits and coding:
communication
Design tasks around
and teamwork
customizing AI tools to
skills.
solve authentic tasks. Guide
students to acquire skills in
leveraging AI-development
platforms or toolkits,
enhancing datasets, and
modifying programming
codes, including those
based on open-source
options; challenge and
support students to explore
and test creative ideas on
the design of AI tools to
solve variations of problems.
• Locally accessible
free and/or opensource online
datasets, AI tools
Organize students to modify
and programming
a dataset or create a new
libraries.
one for real-world contexts,
by drawing on an authentic • Locally accessible
free and/or
task such as monitoring
open-source data
the energy consumption of
analytics tools.
local schools or households,
forecasting weather for a
• Locally accessible
specific location or route, or
cloud-based
tracking an epidemic disease. computing
Teach and facilitate students resources, locally
to leverage automatic
hosted computing
data-collection tools (e.g.
resources (e.g. a
BeautifulSoup19 for scraping
school server),
information from webpages); or computing
apply AI programming
resources shared
skills to clean, encode and
by trustable
preprocess the data; and
institutions or
use the data to customize AI
industry agencies.
models or craft AI tools.
• AI application performance
test lab: Guide students
to search for and adapt a
free and/or open-source
performance matrix for the
testing of AI applications
(e.g. accuracy, precision,
F-1 score, confusion
matrices and ROC curves).
Let students experience
the use of adapted tools to
test the performance and
technological robustness of
the crafted AI application,
and simulate users’ feedback
on ethical compliance.
Use automated tools
to generate visualized
reporting and summarize
recommendations on the
optimization of the AI
application.
49
AI competency framework for students – Chapter 4: Specifications of AI competencies for students
STUDENT
COMPETENCY
4.3.4 Iteration and
feedback
• Students are
expected to
enhance and
apply their
interdisciplinary
knowledge and
practical methods
to evaluate
the humanistic
appropriateness
and
methodological
robustness of
50
(AI curricula or programmes
of study should…)
SUGGESTED
PEDAGOGICAL METHODS
LEARNING
ENVIRONMENTS
(Institutions and teachers
can consider and adapt the
following learning methods.)
(The following learning
settings can be
provided and adapted.)
• CG4.3.3.3 Equip students • Comparing the creation
with skills to test and
of AI tools through
optimize their self-crafted customizing datasets
and programming codes
AI tools: Support students
with the building of AI
to customize assessment
applications based on
methods and instruments
low-code development
for the testing of their
platforms: Organize
self-crafted AI tools for
students to search for
robustness and ease of
information on the steps
use, learn how to organize
and skills required to create
peer assessments and
AI tools by customizing
share feedback, and build
the open-source datasets
collaborative skills as coand programming codes of
creators.
AI toolkits. Guide them to
study skills in building AI
applications based on lowcode development platforms.
Organize discussion on the
difference between the
two approaches in terms
of human agency and
human determination, the
inclusion of data from local
communities and reflection
of local cultural diversity, and
the scalability and reusability
of the resulting tools. Discuss
how to choose between the
two approaches according to
specific needs and situations.
AI
techniques
and
applications
AI system
design
CURRICULAR GOALS
• CG4.3.4.1 Develop
• Simulating the
• Locally accessible
the skills to critique
performance-test of
online free and/
AI systems: Provide
an AI system: Organize
or open-source
project-based learning
students to use adapted
AI tools including
opportunities for students
metrics to weigh whether
data analytics tools
to practise skills in critically
an AI model enhances or
and programming
testing the technological
weaken human capacities,
libraries.
robustness and critiquing
agency and consciousness,
• Locally hosted or
the ethical appropriateness and evaluate the extent of
locally accessible
of an AI system through
explainability of its method.
cloud computing
auditing whether the
Adapt performance metrics
resources.
model enhances human
on machine learning and
capacities, agency and
associated visualization
• Downloaded
consciousness, or weakens
tools including open-source
and adapted
them; checking the extent
options (e.g. the F1 score in
instruments for the
of its explainability and
machine learning, confusion
ethical auditing
AI competency framework for students – Chapter 4: Specifications of AI competencies for students
STUDENT
COMPETENCY
AI system
design
CURRICULAR GOALS
(AI curricula or programmes
of study should…)
SUGGESTED
PEDAGOGICAL METHODS
LEARNING
ENVIRONMENTS
(Institutions and teachers
can consider and adapt the
following learning methods.)
(The following learning
settings can be
provided and adapted.)
an AI model and
protection of data privacy;
matrices and ROC curves) to
and performance
its impact on
measuring the performance measure the performance
testing of AI
individual users,
of the AI system; and
of the AI system. Design and
models.
societies and the
studying users’ feedback to
apply research methods (e.g.
• Access to applicable
environment.
evaluate its broader societal gathering age-appropriate
regulations on
They should be
and environmental impact.
qualitative and quantitative
AI or governance
able to acquire
market data) including
• CG4.3.4.2 Support the
frameworks.
age-appropriate
feedback from (simulated)
building of technical
technical skills
end users to study the
• Locally accessible
skills and social
to improve the
societal implications and
online collaborative
responsibilities in
quality of datasets, optimizing, reconfiguring environmental impact of the platforms to
reconfigure
adoption of the AI model.
support resource
or shutting down an AI
algorithms
Synthesize the results and
sharing, peer
system: Offer simulation
and enhance
report
them
in
a
visual
learning, and
activities for students to
architectures
format.
the collaborative
understand corporate
in response to
design and creation
social responsibility and
• Simulating AI engineers’
results of tests
acquire interdisciplinary
corporate decision-making of AI tools (e.g.
and feedback.
GitHub, arXiV or
skills to make decisions
on the iteration of an AI
They should be
on the iteration of an
model: Organize students to forum groups).
able to apply
AI system based on the
play the roles of AI engineers
human-centred
results of testing and users’
to integrate and interpret
mindset and
feedback. The activities
results from feedback,
ethical principles
should involve development considering both AI system
in simulating
of students’ technical
design and corporate social
decision-making
skills for three possible
responsibility. Make an
on when an AI
scenarios: (1) optimization:
appropriate decision from
system should be
optimizing the datasets,
multiple choices on the
shut down and
algorithms, model,
iteration of the AI model:
how its negative
design functionalities
(1) optimization, where the
impact can be
and/or interface;
problem scoping is validated
mitigated. They are (2) reconfiguration:
and the datasets, algorithms,
also be expected
revisiting problem scoping
AI model or interfaces
to cultivate
and reconfiguring the AI
may need to be optimized;
their identities
system; and, (3) shutting
(2) reconfiguration, where
as co-creators
down: where it is proven
fundamental flaws are
in the larger AI
that the AI system violates
discovered through tests
community.
human rights or harms
and/or users’ feedback in
vulnerable groups, students the problem scoping and/
should learn to make
or configuration of the
decisions to shut down the
architecture; or (3) shutdown,
AI model and quickly put
where it is proven that an
remedial strategies in place. AI model violates human
rights or harms vulnerable
• CG4.3.4.3 Foster students’
groups. Support students
self-identities as coto acquire technical skills
creators in the AI era:
for optimization and
Guide students to nurture
reconfiguration, and learn
the responsibilities of being
51
AI competency framework for students – Chapter 4: Specifications of AI competencies for students
STUDENT
COMPETENCY
AI system
design
52
CURRICULAR GOALS
(AI curricula or programmes
of study should…)
SUGGESTED
PEDAGOGICAL METHODS
LEARNING
ENVIRONMENTS
(Institutions and teachers
can consider and adapt the
following learning methods.)
(The following learning
settings can be
provided and adapted.)
a co-creator of AI tools and
to negotiate and make
the ‘driver’ of the design
decisions about shutting
of the next generation of
down the AI model and what
AI technology. Develop
are the possible remedy
their sense of belonging to
strategies.
the larger AI community,
• Engagement with
and encourage them to
communities of AI creators:
critically analyse the longFacilitate interested
term impacts of AI systems
students to join local or
on social relations and
online communities of AI
individual behaviours by
co-creators. Encourage
drawing on real experiences
them to participate in online
of designing and building
discussions or collaborative
AI systems. Discuss how
development of AI tools, and
regulations or policies
share open-source datasets
should be adapted or
and examples of algorithms
created to enhance the
or AI toolkits.
governance of AI.
AI competency framework for students – Chapter 5: Applying the framework
Chapter 5: Applying the framework
This chapter provides some further guidance on the types of considerations that can feed into
the successful development and deployment of curricula.
5.1 Aligning AI competencies as
the foundation for national
AI strategies
The development and implementation
of national strategies for AI vary across
countries. Around 70 countries have released
strategy documents on AI, which often
position education as the sector to build
local human resources and talent in AI.
In countries with well-entrenched national
strategies, the AI CFS can be aligned with
existing policy frameworks as a foundation
to foster the human-centred mindset and
values needed to implement regulations
on the ethics of AI, prepare people to be
responsible AI users and citizens, and
develop local communities of AI co-creators
at scale.
Box 1: Recommendation on the Ethics of Artificial Intelligence
Member States should promote the acquisition of ‘prerequisite skills’ for AI education, such as
basic literacy, numeracy, coding and digital skills, and media and information literacy, as well
as critical and creative thinking, teamwork, communication, socio-emotional and AI ethics
skills, especially in countries and in regions or areas within countries where there are notable
gaps in the education of these skills.
Member States should promote general awareness programmes about AI developments,
including on data and the opportunities and challenges brought about by AI technologies,
the impact of AI systems on human rights and their implications, including children’s rights.
These programmes should be accessible to nontechnical as well as technical groups.
Source: UNESCO, 2022a, pp. 33–34
If a national strategy for AI is released and
well-implemented, the implementation
of the AI CFS and AI curricula for students
should be planned and supported
administratively and financially within the
broad framework of the AI strategy. Such
national strategies are usually triggered
by policy responses to the wide-ranging
and disruptive impact of AI on work, in
terms of both AI-driven job displacement
and AI-supported job creation, as well as
the prospect of new employment skills
that the adoption of AI may require. The
foremost policy response to this disruption
is system-wide strategies on AI competency
development that are comprised of funding
and incentive mechanisms as well as specific
courses on AI that streamline different
trajectories as appropriate for each sector,
including school education, technical and
53
AI competency framework for students – Chapter 5: Applying the framework
vocational education and training (TVET),
higher education, upskilling and reskilling
for employees, and lifelong learning
programmes for citizens. For countries
without an adopted strategy, the AI CFS can
serve as a trigger to raise awareness about
the importance of national policies on AI
in general, and on the development of AI
competencies in particular.
The implementation of such strategies
and policies is expected to start with the
assessment of readiness and programme
gaps. The processes and outcomes of the
implementation are usually monitored
and evaluated, and policy-makers should
establish early and regular monitoring of
AI competency development programmes
54
when setting up overall mechanisms and
methodologies to track implementation.
To evaluate AI curricula or agile education
programmes, it is particularly important to
formulate criteria that cover: the readiness
of students and teachers; deficiencies
in training and support for teachers’
professional development; gaps in curricular
goals and content that need to be addressed
to support the national AI vision; additions
needed to the curricular content to meet
immediate and near future needs of the
markets; mechanisms for the mobilization
and validation of intersectoral support; the
degree of curriculum integration; readiness
of learning environments; and the quality of
the implementation of the curriculum.
AI competency framework for students – Chapter 5: Applying the framework
Box 2: Supporting human resource development: The Republic of Korea’s
National Strategy for Artificial Intelligence
The Republic of Korea’s National Strategy for Artificial Intelligence has three main focus areas:
(1) Establish reliable AI infrastructure, including to support human talent and improve
technology; (2) expand the utilization of AI throughout the industrial and social sectors; and
(3) respond proactively to social changes, including labour market needs. The strategy seeks
to develop an AI ecosystem that results in the full-scale utilization of AI, and establishes the
Republic of Korea as a global leader in people-centred artificial intelligence.
To support the achievement of this goal, the Republic of Korea has focused on revising
regulations to create a more industry-friendly environment and nurture the productive use
of data and AI innovations, the use of AI to streamline governance, the establishment of
regulations on AI ethics, and the building of human capital in AI from as early as primary
school. The strategy proposes an interdisciplinary AI curriculum and the definition of AI
competencies based on the needs of four categories of populations: (1) the general public,
who need to be able to use AI, as well as acquire basic AI and data literacy, including
knowledge of AI ethics; (2) AI practitioners, who apply AI and software tools in ‘AI + X’
environments in the labour market; (3) AI professionals, who develop AI platforms and
systems; and (4) AI talents, who will resolve AI issues and develop new AI models and
algorithms.
In alignment with competency development for these four categories, the strategy suggests
regulations to upskill and upgrade industry professionals to the level of professorships in AI,
support the expansion of existing AI departments, and mobilize more departments to offer
programmes related to AI, including through expanding the scale and diversity of education
and research programmes in AI at the master’s and doctoral levels and through creating
interdisciplinary majors on AI.
As for school level, the strategy seeks to expand learning opportunities on AI with a focus
on computational thinking. At the lower grades of primary school, students are offered
experiential engagement with AI to foster their interest and curiosity; at the higher primary
grades, students are supported to extend their knowledge and skills through applying AI
in the learning of core subjects. Secondary-level students have the opportunity to attend
AI-centred schools to complete a more advanced AI curriculum. Teachers are also supported
to enhance their knowledge and skills on AI through integrating AI in their initial training
programmes and providing new degrees on AI pedagogy integration.
Source: Ministry of Science and ICT, Republic of Korea, 2019
55
AI competency framework for students – Chapter 5: Applying the framework
5.2 Building interdisciplinary
core and cluster AI curricula
for AI competency
of a human-centred mindset and the ethics
of AI are anchored in students’ broad social
and emotional skills.
The development of students’ AI
competency needs to integrate varied
channels for learning and practice, including
formal courses within the framework of
the national curriculum, extracurricular
programmes, and informal learning
through engagement with families and
local communities. While promoting the
development and implementation of a
national AI curriculum as the main channel
for the implementation of the AI CFS, it is
also important to consider whether the
study programmes provided by the private
sector or non-governmental channels are
in compliance with the human-centred
vision and ethical principles. Reviewing and
steering the impact of informal learning
channels including digital platforms is also
essential, and can be enacted by mandating
providers’ accountability for safety and
ethics if their programmes target students,
especially children.
It is therefore necessary to align the AI CFS to
countries’ general competency frameworks
for students, and examine whether the
latter need to be adapted or reformed to
respond to the new requirements of the AI
era. In countries where national digital or
ICT competency frameworks for students
have been adopted and implemented, an
adaptive approach can be considered to
integrate AI aspects into them. This requires
a redefinition of digital competencies to
cover the uncharted values, knowledge and
skills required for new iterations or novel
domains of AI, and their connections with
previous generations of digital technologies.
AI has an interdisciplinary nature and
complex intrinsic conceptual and practical
connections with mathematics, science,
engineering, languages, social studies,
art, civic and citizenship education, and
history as well as various combinations
of these subjects. AI also represents both
an iterative step and a technological leap
in the continuum of digital technologies.
In this context, the AI CFS is built upon
multidisciplinary knowledge and skills on
data, programming, computing structures
and the internet as well as the integrated set
of conceptual knowledge and skills based on
computing and engineering thinking, and
scientific reasoning. In parallel, the fostering
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A set of core AI curricula within formal
education programmes is usually considered
to be the main channel for providing
inclusive opportunities for all students,
particularly those who may not have access
to AI other than at school. This will require
the reconfiguration of national curricula to
accommodate the time to be committed to
AI courses. The cluster curricula related to AI
should be adapted or reformed to enhance
their connections with AI competencies,
without losing their focus on students’ other
core competencies. These interdisciplinary
core and cluster AI curricula can be
integrated into agile structures that are
appropriate to national or local educational
contexts.
The UNESCO report K-12 AI curricula: A
mapping of government-endorsed AI curricula
(2022b) identified four main strategies for
integrating AI curricula into K-12 education
systems. These include the implementation
of AI as a discrete subject; the integration
of AI into other existing subjects (usually
AI competency framework for students – Chapter 5: Applying the framework
ICT); cross-curricular approaches in which
AI outcomes are integrated into multiple
cluster subjects; and AI as an optional,
extracurricular or co-curricular activity
(e.g. for an extra-curricular club). AI as a
discrete subject may be mandated for all
students and can be supported by a series
of complementary courses in science,
technology, mathematics, engineering
and design, to meet the diverse abilities,
backgrounds and learning needs of students.
Under any one or combination of these
approaches, the interdisciplinarity has
double implications: the core AI curriculum
should mobilize students’ multidisciplinary
values, knowledge and skills in relevant
subjects, especially science, technology,
engineering, arts and mathematics (STEAM),
to act as the foundation of the AI curriculum
as exemplified by the United Arab Emirates’
Computing, Creative Design and Innovation
curriculum (UNESCO, 2024); and the
cluster AI curriculum should promote the
intrinsic integration of key aspects of the AI
competencies into the learning outcomes
and navigate them at corresponding
progression levels.
Box 3: The United Arab Emirates’ interdisciplinary approach to K-12 AI curricula
‘By covering computer science, engineering, design, sustainability and visual
communication, the Ministry of Education’s Computing, Creative Design and Innovation
curriculum offers a comprehensive and concise educational framework. It prepares students
to thrive in the dynamic and interconnected world by nurturing critical thinking, problemsolving abilities and innovation.’
The United Arab Emirates takes an interdisciplinary approach to its AI curriculum for K-12
schools by integrating it into a curriculum called Computing, Creative Design and Innovation
(CCDI). By including a focus on AI, the CCDI curriculum encourages students to develop their
creativity and problem-solving skills; build an awareness of ethics and ethical impacts; learn
and rehearse fundamental AI principles and concepts; and cross-fertilize their knowledge
across fields. The curriculum was first established in 2016 as a technology-focused subject
area, over and above the already-existing computer science curriculum.
Since then, and with the recent developments in the field of AI, the CCDI has progressively
integrated robotics, programming, 3D-modelling and electronics. In 2020 the curriculum
was revised to cover five domains: (1) computer science, with a focus on computer systems,
networks and the internet, data and analysis, algorithms and programming, and the
impacts of computing; (2) engineering principles and systems, with a focus on electricity
and electrons, robotics and systems, and embedded systems; (3) design and innovation,
including entrepreneurship and the engineering design process; (4) sustainability, with
an emphasis on the sustainable society; and (5) visual communication, concentrating on
graphics for design, computer-aided design and design realization.
Source: UNESCO, 2024
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AI competency framework for students – Chapter 5: Applying the framework
5.3 Framing future-proofing and
locally feasible AI domains as
carriers of the curriculum
After determining the interdisciplinary
alignment structure of core and cluster AI
curricula, curriculum developers will need
to integrate the AI CFS into national or
institutional core AI curricula. The framing of
the core AI curriculum is built on interlinked
key aspects of the AI competencies,
subdomains of AI under each aspect, and
specific AI systems to act as carriers of the
curriculum. Decisions about making the
curriculum compulsory or elective is framed
by at least three factors: the foundational
value of different aspects, the futureproofing potential of AI knowledge and
skills, and the feasibility of implementation
in local schools. The feasibility of AI domains
and systems is determined by the AI
readiness of teachers and students, and the
local availability and affordability of generic
AI systems and specific hardware, software,
programming languages and essential
applications for the majority of schools.
As explained in Chapter 4, the humancentred mindset, AI ethics, and AI techniques
and applications are crucial to all students’
lives and work in the AI era, and thus should
be included in all AI curricula. Some domains,
such as AI system design, may be more
appropriate for students who have a strong
interest and ability in AI. Assessing the extent
of local AI readiness can inform decisions on
whether AI system design should be defined
as a set of thinking skills that can be merged
into other aspects or should be taught as a
discrete domain if the necessary databases,
computing resources and AI models are
available.
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Within the framework of a selected aspect
or domain of AI, the next step is the scoping
of the subdomains of AI techniques and
technologies to be covered, and the specific
AI systems to recommended as carriers of
the curriculum or learning practices. This
is more pronounced for the aspect of AI
techniques and applications. The range of
techniques is vast, including logic systems or
algorithms crafted from general deductive
principles to solve specific problems (e.g.
human-coded decision trees, alpha-beta
pruning and minimax), as well as models
trained on large amounts of data (e.g. deep
learning and generative AI). Curriculum
developers need to choose subdomains
from a large list of exemplar AI techniques
and specify their relations, such as classical
AI or ‘rule-based AI’, machine learning, deep
learning and generative AI models. The
range of AI technologies and human-facing
products and services is expanding rapidly,
and it’s more challenging to choose from
AI technologies being developed across
sectors, including from the categories
of computer vision, natural language
processing, automated speech recognition,
and automated planning and scheduling
(AI planning). Following the selection and
scoping of subdomains of AI techniques
and technologies, examples of AI systems
and tools should be considered, with a
view to being agnostic towards commercial
brands or products as much as possible. As
stated in Principle 5 of Chapter 2, rigorous
public validation mechanisms should be
applied to prevent the use of AI systems that
discriminate against marginalized groups or
produce bias(es) related to gender, ability,
socio-economic status, language and/or
culture. The principle of inclusivity should be
upheld when choosing AI tools.
AI competency framework for students – Chapter 5: Applying the framework
Furthermore, which AI domains should be
defined as compulsory and which can be
elective will be determined by the national
context, including the aims and ambitions
of relevant policies and readiness as stated
above. The depth and breadth of domainspecific AI knowledge and skills should be
defined based on the typical readiness and
abilities of the target cohorts of students.
It is imperative for all students to reach
the first two levels of Human-centred
mindset, Ethics of AI, and AI techniques and
applications, but it is less necessary for them
to reach the third level, ‘Create’, especially
for AI system design. Therefore, it might be
useful to consider an agile or contextualized
implementation strategy, in which both
compulsory and elective subjects or courses
will be designed and offered to students for
different AI techniques and key domains of
AI knowledge.
By anchoring AI competencies in a humancentred mindset and embodied and social
knowledge and skills in ethics, the AI CFS
aims to prepare students to collaborate
with future-oriented AI in a range of
contexts. The systemic AI design thinking,
knowledge and skills are intended to foster
an open knowledge schema that can
support students to understand, use and
create future generations of AI systems.
The AI CFS emphasizes the importance of
transferable knowledge and skills under the
aspect of AI techniques and applications
that can help the majority of students to be
ready for the further iterations of AI tools.
While efforts have been made to ensure
that this curriculum framework responds
to emerging technologies, new tools and
innovations will emerge after it is published,
and the example tools and activities may
become obsolete or dated. The curriculum
itself will need to include content that
can be adjusted going forward in order
to remain relevant and ‘future-proof’. A
modular curriculum design is suggested,
in which multiple modules based on AI
domains or different AI systems or tools can
be developed and recommended to local
educational institutions. A modular structure
allows the curriculum to be reviewed and
updated more dynamically, as it is not
necessary to change the entire curriculum
to add or remove a specific tool, domain
concept or other content. On the other
end of the spectrum, future-proofing can
involve schools and students co-designing
AI curricula. This means encouraging the
drafting of school-based AI curricula and
teachers’ contextual adaptations of specific
domains or tools selected for general
competency development. To enact this
framework, curriculum developers should
consider the dynamism of an AI curriculum
and make efforts to future-proof the learning
process.
5.4 Tailoring age-appropriate
spiral curricular sequences
The AI CFS naturally entails a paradigm shift
towards competency-based education.
A competency-based education aims to
transition from models of fixed time and
flexible learning (implying completing
instruction within a fixed curricular schedule
regardless of whether all students have
reached the expected mastery level) to more
flexible time and fixed learning (implying
that flexible learning schedules are allowed
so that students of all abilities can reach the
expected mastery level). With competencybased education, students are expected
to demonstrate performance-based
knowledge, skills and values that constitute
the competencies, and students who do not
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AI competency framework for students – Chapter 5: Applying the framework
meet these minimal standards are provided
with additional support until they do (Patrick
and Sturgis, 2017).
This framework does not break down the
progression of learning or activities by
grade level, focusing instead on the exitlevel outcomes which systems should seek
to achieve for all students. Curriculum
developers will therefore need to leverage
the framework and its components to
develop a scaffolded spiral learning pattern
across all four aspects, allowing for students
to start the learning of AI with the domains
and difficulty level that match their abilities
and the readiness of their schools. The
spiral curricular pattern should provide
spaced and iterated engagement with a
set of foundational AI knowledge that will
encourage both memory retrieval and
cyclically upgraded practices to deepen
their understanding and associations with
problem-solving contexts. This design helps
ensure a transfer of information from the
working memory to the long-term memory
to support sustained learning gains, as well
as enable students to leverage existing
schemas to learn novel AI knowledge, or
adapt application skills to solve problems
in varied contexts. Conversely, a curriculum
developed and delivered as a one-off over
a short period of time (e.g. for hackathons
or bootcamps) may spark interest but is less
likely to lead to sustained AI competency.
The work of curriculum developers will be
to outline the main elements of AI ethics,
foundational knowledge and skills as well
as system design thinking, and then identify
appropriate levels of difficulty, breadth and
depth of these elements for different grade
levels. This will enable them to create spiral
iterations of lessons and project-based tasks
that help students to progressively advance
and expand their learning and practice.
Box 4: The spiral curricular sequence of ‘Day of AI’ courses
The AI curriculum developed by MIT’s RAISE2 initiative, Day of AI, adopted the spiral design
approach by clustering curricular content around key topics such as ‘What AI is, and what AI
does well and what AI does not do as well’, ‘How AI works’, ‘How a machine learns’ and ‘How
a machine creates’. Students at different ages were given opportunities to continuously
engage in topics such as ‘What is AI?’, while being gradually exposed to novel or upgraded
knowledge and skills such as algorithms and AI programming, teachable machines and
generative AI. Cross-cutting topics around ethics, including AI biases, human rights, human–
AI interaction and the social impact of AI were tailored to students at different age levels.
For more information: https://dayofai.org
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5.5 Building enabling learning
environments for AI curricula
While the required resources for the
implementation of AI curricula may vary
depending on the breadth and depth
of expected curricular goals and overall
digital readiness in local schools, a basic
learning environment is required to meet
minimum standards for effective study
of the essential aspects and domains of
AI to the basic mastery level. According
to UNESCO’s report K-12 AI curricula: A
mapping of government-endorsed AI curricula
(2022b), implementation for school students
requires the following essential conditions,
ranked by importance: teacher training and
support, teaching resources on AI, needs
analysis and school-based research, updated
digital infrastructure in schools, and the
provision of AI resources including through
procurement of hardware and software
as well as engagement with the private or
third sector to share AI devices and systems.
If these conditions are not provided, the
curriculum is unlikely to be implemented as
intended or achieve its anticipated learning
and competency objectives. The report
highlighted typical learning environments
that had been set up by the 11 countries that
were implementing their own governmental
K-12 AI curricula as of 2022, detailed below.
Box 5: Typical enabling learning environment set up by governments’ AI
curricula
●
Hardware and robotics: The hardware needed for AI curricula may include computers,
tablets, laptops and internet access. Not all AI curricula include content on robots or
robotics. When the learning on robots is required, curricula can leverage free online virtual
applications or locally affordable kits. Devices like Raspberry Pi are used by some curricula
that require students to create programs and test them using low-cost devices.
●
Software: The Ubuntu3 open-source operating systems were used by some curricula as
less expensive alternatives to other operating systems.
●
Programming languages: National AI curricula have often leveraged free programming
languages such as HTML, Javascript, Python, Micropython, NumPy, R and Scratch.
●
Tools for learning AI techniques: A number of tools have been developed or made
accessible free of charge to facilitate understanding and allow the exploration of complex
concepts and AI techniques, with the following mentioned in the 11 governmental AI
curricula: MachineLearningForKids (an educational tool for teaching kids about machine
learning by letting them train a computer to recognize text, pictures, numbers, sounds or
other inputs),4 Teachable Machine (a platform developed by Google to train a computer to
recognize the user’s own images, sounds and poses),5 TensorFlow (an end-to-end platform
for machine learning),6 and Keras (deep learning for humans).7
Source: UNESCO, 2022b, p. 47
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AI competency framework for students – Chapter 5: Applying the framework
To provide enabling learning environments
for AI competency development and the
implementation of an AI curriculum in
particular, governments should commit to
universal access to internet connectivity
for all schools and students, including
through agile ‘online + offline’ solutions, to
engage with online or mobile AI systems,
customizable applications, basic and
extendable learning resources, and peer
learners or co-creators. The prerequisite
digital infrastructure also includes a modest
number of well-functioning digital devices
with basic connectivity as well as a minimum
amount of software or applications for
students to learn operational skills, practise
programming, and train virtual machine or
AI models.
Where these essential conditions are not yet
realized, but the government is determined
to initiate an AI curriculum at the earliest
possible stage, alternative options should
be considered in the provision of enabling
learning environments. With regard to the
AI CFS, most objectives under the first two
aspects, Human-centred mindset and Ethics
of AI, can be engaged with, at least partially,
through online and offline solutions, which
are also defined as unplugged solutions. For
the aspect of AI techniques and applications,
some well-designed unplugged activities
have been made available by academic and
non-profit organizations to demonstrate
conceptual knowledge on AI tools and
the understanding of AI techniques (e.g.
the unplugged AI activities designed
by Everyday AI,8 AI Unplugged,9 and the
International Society for Technology in
Education).10 Even in fully connected
learning settings, unplugged solutions
have value by providing students with
opportunities to retreat from algorithmcontrolled information cocoons and
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interactions with digital platforms to practise
independent, autonomous contemplation,
which is critical for the progressive
construction and deepening of conceptual
knowledge on AI.
5.6 Promoting the
professionalization of AI
teachers and streamlining
their support
As stated above, the most important
preconditions for the implementation of
AI curricula for school students are teacher
training and support as well as the provision
of teaching resources on AI. The achievement
of the goals outlined by the AI CFS will
require teachers, particularly those in ICT
or AI, to continuously develop and update
their subject knowledge and pedagogical
capacities in designing and facilitating ageappropriate learning activities on AI. National
and institutional strategists need to plan and
implement an integrated approach to the
reform of pre-service programmes to prepare
qualified AI teachers, design and provide
competency-based training and long-term
support for in-service ICT or AI teachers,
and enhance upskilling for teachers in other
core subjects to foster interdisciplinary
AI competency. All these training and
support programmes aim to strengthen
the competencies of teachers who are
tasked with teaching AI or implementing
the national AI curriculum, implying a trend
toward the professionalization of AI teachers.
This professionalization includes setting
up frameworks specifically for AI teachers,
or alternative and more agile mechanisms,
that define and develop a set of professional
competencies to fully realize the goals of
the AI curriculum for students. As ICT and AI
are often categorized as marginal subjects
AI competency framework for students – Chapter 5: Applying the framework
in school curricula, the professional status
of ICT and AI teachers has not been fully
recognized. The professionalization of AI
teachers also means that AI should be
classified as one of the core subjects and
AI teachers should be entitled to the same
professional status as teachers in other core
subjects, with their teaching hours and
performance being equally recognized in
personnel management systems.
Box 6: An AI competency framework for AI subject teachers in China
In China, an AI competency framework for AI subject teachers was developed by the
National Institute for Education, East China Normal University and Tencent. Even though
it’s not a government-driven national AI competency framework, it is a clear indication
of the professionalization of AI teachers. It defines a comprehensive set of competencies
for AI teachers, which encompasses six dimensions: understanding and awareness, basic
knowledge, basic skills, problem-solving capability, teaching practices, and ethics and
security. Accordingly, teachers must grasp AI’s foundational conceptual logic and societal
impact, appreciating the distinctions between human and machine intelligence, and the
significance of human-machine collaboration, with a view to AI’s educational roles. Unlike
the UNESCO AI competency framework for teachers, the framework is aimed at AI teachers;
the aspects of human-centred mindset and professional development are not covered, and
no progression levels are provided.
For more information: http://www.jyb.cn/rmtzcg/xwy/wzxw/202203/t20220325_686401.html
In countries where public teacher education
institutions do not have sufficient capacities
to upskill teachers to keep pace with the
rapid changes of AI technologies, public–
private partnerships for the development
and provision of AI curricula are often
mobilized to leverage the human and
material resources of the private AI industry
or NGOs to partly or fully substitute for a
public AI curriculum and ICT or AI teachers.
As these resourceful AI companies and NGOs
have a strong interest in reinforcing their
presence and dominance in the teaching
of AI based on their own brands, this
approach risks the de-professionalization of
public AI teachers. It is recommended that
public–private partnerships are mobilized
with a clear purpose of contributing to the
preparation of upskilling public AI teachers
and supporting their continuous professional
development. Moreover, the comprehensive
competency frameworks for AI teachers
to meet the needs of implementing the
AI CFS and national AI curriculum should
be used to define a rigorous set of criteria
to validate whether the AI courses and
trainers developed by the AI industry
are trustworthy, anti-bias, relevant for AI
competency development and sufficiently
brand-agnostic. Such frameworks should
also help verify how the AI courses can be
properly integrated into school curriculum
systems to supplement rather than replace
the public curriculum. The accountability of
public schools for continuously improving
teachers’ capacities in implementing the AI
curriculum should be prioritized instead of
being weakened.
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To promote the professionalization of public
AI teachers, it is also important to adopt the
requirement of implementing the AI CFS as
a benchmark to streamline pre-service and
in-service training and continuing support
for teachers’ professional development,
to ensure they are aligned with a set of
clearly defined competencies and are
complementary in scaffolding teachers’
progressive improvement throughout their
career. Special attention should be given
to the engagement, review and adaptation
of continuing education initiatives for
teachers and school-based support for
their professional development according
to the value orientation, knowledge and
practical skills required to teach the national
AI curriculum.
5.7 Guiding the cohort-based
design and organization of
pedagogical activities
AI competency development is a three-helix
bundle spanning the social and emotional
learning of values and ethical principles,
self-directed and collaborative construction
of conceptual knowledge on AI, and practical
skills to apply and co-create AI tools. A
combination of innovative pedagogical
methodologies is required in order to help
students progress through the three helixes
of competencies altogether, bridging
between what they know and what they
can do as well as transferring their prior
knowledge and skills to novel concepts and
new problem-solving contexts in the AI-rich
workplaces and social spaces of the future.
The pedagogical innovations that
are tailored to the particularities of AI
domains and varied abilities of students
can be unlocked through the design and
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organization of activities based on a cohort
of students who are enrolled in a certain
AI course or share an interest in the same
domain of AI. In this cohort-based approach
to the design and organization of learning
scenarios or projects, a certain cohort of
students may be grouped together from
different classes and grade levels. This
approach does not represent any particular
learning theory, and typically involves a
wide range of pedagogical methods and
practice-orientated learning scenarios
including interactive activities, collaborative
projects and peer support. Students build
a community of practice and their learning
often follows a curricular schedule where
they share accountability and motivate
and coach each other, and work with their
teachers to get feedback. In this way, they
deepen their understanding and tackle
challenging questions together; collaborate
in hands-on projects to apply knowledge
and skills in practical ways; and exchange
views and engage in debates on the societal
impact and ethical issues of AI to enhance
social construction.
When choosing or designing pedagogical
methodologies for the understanding,
application and creation of different aspects
of the AI CFS, it is also important to consider
the domain-specific needs for pedagogical
practices:
●
The nurturing of human-centred
values and mindset, by nature, is built
upon social and emotional learning
processes, and requires conflict-based
opinion-taking, social construction
and social interactions.
●
The learning of ethics is a process of
understanding abstract principles
and regulatory rules through practical
AI competency framework for students – Chapter 5: Applying the framework
case studies, scenario-based critical
evaluations, contextual application
and collaborative rule-making.
●
AI techniques and applications
represents a domain that seamlessly
blends the practice-oriented
construction of conceptual
knowledge on AI with authentic taskbased application, and requires real
AI tools as the basis for constructing
knowledge schema on AI techniques
and technology, problem-based
learning and practices of transferable
application and scenariobased inquiry, and a deepened
understanding of the values and
ethics underlying AI tools and their
uses.
●
AI system design simulates real-world
engineering projects, involving the
life cycle of the creating, realizing
and iterating AI systems to practise
engineering thinking processes and
foster integrated problem-solving
skills. It requires teachers to design
and organize project-based learning
to allow students to identify and
delineate the problems that can and
should be solved by AI; assess needs
for data and plan methods of data
collection; configure the architecture
of AI models; and train AI models or
create prototypes, tests and iterations
of them.
As AI competency is a three-helix bundle,
specific pedagogical practices can
potentially cover multiple aspects of AI
competency within one lesson or unit.
This requires instructional planners or
teachers to infuse and navigate various
pedagogical methods so students can
engage with multiple aspects of the learning
and practice of AI. The real-world research
and development of AI technology and
applications often leverages intensive and
continuous conceptualization of AI methods
and iterative programming, configuration
and optimization. This prerequisite for
developing practical AI competencies has
been validated by the effectiveness of the
pedagogical methodologies practised
at hackathons and bootcamps using AI
applications. To improve the efficacy of
pedagogy in schools, opportunities should
be scheduled for students to be engaged in
more intensive units of lessons or activities
that align with the formal AI curriculum.
The national or institutional AI curriculum
should frame recommendations or
guidance on pedagogical methodologies
around the principles of engaging shared
accountability and peer learning in the
target cohort of students and the specificity
of the AI domain and expected learning
outcomes. When updated or novel
pedagogical methodologies are introduced
in AI curricula, sufficient training, practical
guidance and instantly responsive services
(e.g. online chatbots) should be made
available for teachers. Locally relevant
incentive mechanisms should be planned
and implemented to review, validate and
recognize practices in pilot testing and
scaling up pedagogical innovations.
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Box 7: Pedagogical methodologies in the MIT curriculum on the ethics of AI for
middle school students
An ethics of artificial intelligence curriculum for middle school students was created by Blakeley
H. Payne with support from the MIT Media Lab Personal Robots Group, directed by Cynthia
Breazeal (Payne, 2019). The curriculum is designed to be implemented online and/or offline
with students aged 12 to 14 who are early in their AI learning journeys. The curriculum
focuses on improving students’ understanding of AI and the relationships between humans,
technology and society. Parts of this curriculum have also been integrated into the MIT DAILy
Curriculum,11 and into How to Train Your Robot: A Middle School AI and Ethics Curriculum.
Research into the latter demonstrated the potential for such a curriculum to be delivered even
by teachers with a limited computer science background (Williams et al., 2021).
This curriculum exemplifies a student-centred and inquiry-based approach, with learning
outcomes that are aligned to enable a cycle of: initial orientation or information-gathering
that supports students to build knowledge schemas on a new topic; conceptualization,
where students begin to form a hypothesis around the purpose(s) of AI; investigation, in
which students delve deeper into the different perspectives, benefits, values and risks of AI
for different populations, and design potential solutions for the problems that emerge; and
finally, the development of a potential solution prototype using a project-based approach.
Throughout, discussion and reflection are leveraged to deepen understanding and thinking
about the problem.
The curriculum includes six core goals, which are pursued through different online or offline
activities depending on the context. The chart below outlines the goals as well as example
activities for teachers or other facilitators that can help to achieve them
Learning outcomes
Example activities and pedagogical advantages
Understand the basic mechanics of artificial
intelligence systems. This learning outcome
includes sub-outcomes such as recognizing
AI uses in everyday life; understanding
algorithms as a process of input, changes to
input and output; and understanding AI as
a specific type of algorithm with a dataset,
learning and prediction.
Play ‘AI Bingo’ with AI systems. Using a worksheet,
each student tries to find another classmate who
has used or experienced various AI applications
(for example, a tool that suggests emojis to
replace words or an app that maps a route to a
destination). Together the pair must determine
the dataset used and prediction made by each
different type of AI system until one student has
completed five in a row. This represents an example
of gamification, which can increase student interest
and motivation, and is designed to support recall
in order to begin building knowledge schemas
around core AI concepts.
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Learning outcomes
Example activities and pedagogical advantages
Write an algorithm to make the ‘best’ peanut butter
and jelly sandwich (or noodle, rice or tamale dish,
or another locally-relevant food the children are
familiar with). This can be undertaken individually
or as a group. The core of the activity requires
students to practise recall through accessing
knowledge on what an algorithm is and how it is
structured, and apply this to a specific mandated
problem framed in a familiar context.
Identify the AI systems on the YouTube platform
as a group. In this recall-and-identification activity,
students engage in recalling, reflection on and
building of knowledge schemas. In this curriculum,
this activity forms foundational schemas for more
advanced reflective and creative collaborative
problem-solving in the later stages in the
curriculum.
Build a classifier in Google’s Teachable Machine.
In this activity, students are asked to build an
AI in Teachable Machine that will sort pictures
of cats and dogs, but are given a biased dataset
that does not yield consistent results. This is an
example of facilitated experiential learning, where
students leverage a knowledge base about AI
and develop practical skills through hands-on,
guided exploration. They need to reflect on the
outcome of their work and determine the causes
of the inconsistencies (bias). Confirmation design,
in which students are provided with a question
and methodology to confirm a known result, can
be used. At more advanced levels, students can
generate explanations of their results.
Understand that all technical systems are
socio-technical systems, and that sociotechnical systems serve political agendas
and are not neutral sources of information.
Students engage concepts such as the stated
and hidden goals of algorithms, algorithmic
bias and human agency.
Create an ethical matrix of the stakeholders and
their values invested in the student’s peanut butter
and jelly sandwich (or other food item). Undertaken
as a group or individual activity, this builds on
earlier recall/identification tasks by requiring
students to engage in reflection and early critical
analysis as they identify different stakeholders and
their potentially conflicting interests and values.
This enables students to develop procedural
knowledge that can then be applied to more
complex challenges and even ill-defined problems.
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AI competency framework for students – Chapter 5: Applying the framework
Learning outcomes
Example activities and pedagogical advantages
Using YouTube as an example, students
construct an ethical matrix around the YouTube
Recommender Algorithm. This activity exemplifies
a student-centred critical thinking exercise which
pushes students to connect classroom learning
(both procedural and content) to their lived
realities.
Recognize that there are many stakeholders
in a given socio-technical system and that
the system can affect these stakeholders
differently. Students identify AI stakeholders
and their values, and the goals that
systems should have in order to meet those
stakeholders’ needs.
Students reflect on the stakeholders for a range
of technologies such as generative adversarial
network (GANs), emotional recognition and
speech-to-text software. In this exercise, students
demonstrate the ability to transpose the
procedural knowledge gained from the ethical
stakeholder matrix example for the food item and
YouTube to other technologies, an important step
in developing translational skills.
Apply both technical understanding of AI
and knowledge of stakeholders in order to
determine a just goal for a socio-technical
system.
Students brainstorm and redesign the YouTube
algorithm to support new goals. They identify the
datasets and design features necessary to reflect
the new goals they have set. This project-based
group learning approach leverages constructivist
principles as well as technical knowledge gained
from the course to date, in order to work through
the early stages of a design thinking process (up to
the prototype stage) and co-create a solution to,
in this case, a given problem of creating a different
ethical stakeholder profile for YouTube. Sharing
designs facilitates co-learning and reflection across
groups, and a second cycle of iteration can be
used to give students opportunities to leverage
feedback or knowledge gained from peers.
Consider the impact of technology on the
world.
Students interact with different technologies and
respond to creative writing and/or discussion
prompts, reflecting on their direct and extended
impacts. In addition to following an inquiry
approach and leveraging design thinking for
project-based learning, the curriculum seeks
to engage learners experientially in a range of
AI technologies, and foster debate, discussion
and reflection on the interactions between the
technology, people who use it, broader society
and the environment.
Source: Adapted from Payne, B. H. 2019. Available under CC BY-NC 4.0
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AI competency framework for students – Chapter 5: Applying the framework
5.8 Constructing competencybased assessments on the
progression of key AI aspects
The assessment of students’ AI competencies
naturally requires the use of competencybased assessments that need to be adapted
to the specificity and integration of
multiple aspects of AI. Methodologies and
instruments designed for such assessments
are essential to benchmark students’ starting
point, measure their mastery levels of the
key aspects of AI, and provide references
for evaluating the effectiveness of teaching
practices and overall implementation of
the AI curriculum. However, few attempts
have been made to develop these sorts of
instruments for assessing comprehensive
AI competencies cross-cutting multiple
progression levels. Therefore, the
implementation of the AI CFS or the local AI
curriculum needs to include the construction
of a competency-based assessment system
encompassing purpose and objectives,
authentic tasks and methodologies,
benchmarking standards or indicators, and
domain-appropriate criteria associated with
a corresponding grading scale.
Frame criterion-referenced
assessments to measure the mastery
of AI competencies
The primary purpose of competencybased assessments is to measure students’
mastery level against predefined standards
or benchmarking frameworks, implying the
use of criterion-referenced assessments.
As stated above, competency-based
education aims to support all students
to achieve the minimum mastery level
of competencies, meaning the fixed
learning outcomes with more flexible time
schedules. Under these models, students
who do not meet the minimum standards
within a certain timeframe should be given
additional support until they can reach
them. To support this aim, a set of reference
criteria should be defined to diagnose
students’ mastery levels compared with the
predefined standards, and to recommend
further learning experiences. In the context
of the cohort-based design and organization
of pedagogical activities, the criterionreferenced ipsative assessment of a single
student or a target cohort of students
should be implemented to diagnose the
gaps between their mastery level and
the minimum standard as well as their
progressive performance over time. While
the ipsative assessment of learning may help
tailor students’ personalized experiences,
the emphasis on the criterion reference can
prevent the loss of targeted achievement
of AI competencies. This can be extended
to students’ self-assessment and setting of
personal curricular goals.
The AI CFS interprets AI competencies into
measurable learning outcomes and outlines
expected exit-level behavioural performance
for each competency block. These can
be used as a basis for framing predefined
benchmarking standards, against which a
repository of criterion-referenced assessment
items can be created to measure the mastery
level of the cohort of students, including,
more specifically, the aspects, domains or
specific topics they have mastered and any
areas in need of improvement.
Norm-referenced assessments, which
compare individual students to the rest
of the cohort on the same course, are not
the main focus of the competency-based
assessments in the AI curriculum. However,
national or institutional agencies in charge
of AI curricula may consider building a
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AI competency framework for students – Chapter 5: Applying the framework
set of dynamically adjusted norms of
students’ competency development in key
aspects or domains of AI through the longterm tracking of students’ performance.
The norm-referenced assessments can
also provide a comprehensive view of a
student’s abilities compared to their peers,
and a benchmarking of local students’
competencies compared to same-age
students in other countries. The mean of
the norm should be measured against the
predefined standards of AI competencies
to monitor whether the learning outcomes
of the majority of students exceed, meet or
are below the minimum standards. Finally,
the performance of different groups of
students compared to the norms should
be disaggregated and analysed by age,
gender or demographic background, to help
provide evidence for policies or strategies
that enable remedial or supplementary
support for students who are disadvantaged
in learning AI.
involving the human-centrality of mindset
and ethics, transferability of conceptual
knowledge, adaptivity of practical skills, and
creativity in AI system design, the objectives
and methods for assessing performance
should be adapted as follows:
●
Assess both observable
performance and latent
competencies: Move from pure
assessment of observable behaviours
(what students already do) to the
psychometric testing or validation of
students’ latent knowledge schema
on AI techniques and application
abilities (what they can potentially
do), human-centred critical thinking
and ethical evaluation and selection
of AI tools to serve specific purposes
(how they apply ethics to their use
of AI).
●
Shift from assessing rote learning
to testing transferability, adaptivity
and creativity: Assessment methods
should move from the measurement
of fixed, repetitive operations
to the design and use of varied
tasks to assess how students can
transfer knowledge and skills across
contexts (how students can transfer
knowledge and skills) and adapt to
novel situations. Methods should
also shift (how students can adapt);
move from a limited focus on the
fluency of operating existing AI tools
to how students can critically evaluate
existing tools and collaboratively
craft or co-create new AI tools (what
students can co-create).
●
Balance domain-specific and
integrative AI competency
assessments: Building upon the
domain-specific assessments of
Adapt performance scenarios to
assess overt performance and latent
competencies
AI technology is designed to address
real-world problems, and its practiceorientated nature requires the use of
real-world scenarios and authentic tasks to
measure students’ performance in applying
their mindset, ethical principles, skills
and knowledge, and to psychometrically
validate students’ development of multiple
aspects of AI. The competency-based
assessment should fully leverage tasks
showing students’ measurable or overt
behavioural performance (what they can
do), which is often termed ‘performancebased assessment’. However, to fully meet
the needs of assessing both observable
behaviours and latent competencies
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AI competency framework for students – Chapter 5: Applying the framework
mindset, understanding and practices
of ethics, knowledge and skills, design
and use authentic project-based
testing to assess students’ integral
competencies to blend and integrate
ethical principles, AI knowledge
and skills, and computational and
engineering thinking to critically
evaluate AI tools, redesign algorithms
or co-create AI systems. These projectbased assessments of how students
can integrate AI competencies to
solve problems require curriculum
developers or teachers to design open
and authentic tasks; the breadth of
the required competencies should be
adapted to the different progression
levels, and appropriate grading
scales need to be designed to reflect
the measurement of open and
multilayered competencies.
●
Configure authentic assessment
tasks and grading scales for
AI competencies: The design of
assessment items can be framed
by the detailed specifications of
each competency block provided
in Chapter 4. The configuration
of assessment tasks, methods of
administering assessment and
formats of responses should be
aligned with the requirements of each
domain (mindset, ethics, conceptual
knowledge on AI, operational AI
skills and comprehensive AI system
design). This means the specific
assessment tasks should be tailored
according to the cognitive and
behavioural performance that can
psychometrically validate the mastery
of ‘Understand’, ‘Apply’ and ‘Create’. For
the ‘Understand’ level, the tasks may
focus more on the comprehension of
the concepts and ethical principles
underlying performance, with
less focus on concrete practical
skills, while tasks at the ‘Apply’
level can centre on problem-based
practical skills and adaptivity in
coping with task variation. For
‘Create’, the measurement tasks
might be more about synthesis
and algorithmic programming
on the conceptualization of new
ideas, design of virtual or physical
prototypes of new AI tools or
systems, the knowledge and skills
to test and optimize AI models, the
comprehensive computational skills
and engineering demonstrated in
the co-creation of AI, as well as the
human-centred mindset and ethical
principles underlying the design and
testing.
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AI competency framework for students – Chapter 5: Applying the framework
The focuses of domain-specific assessments divided by three progression levels are
recommended as follows for further deliberation, and a non-exhaustive list of examples of
assessment items is provided in Table 5 to inspire configurations of assessment instruments
that cover all topics and progression levels of the local curriculum.
1.
1.1
1.2
1.3
Human-centred mindset:
Conflict-based opinion taking
Conflict-based critical evaluation
Conflict-based social actions
2.
2.1
2.2
2.3
Ethics of AI:
Scenario-based ethical value orientation
Scenario-based ethical behaviour
Scenario-based rule-making
3.
3.1
3.2
3.3
AI techniques and applications:
Problem-based AI knowledge and understanding
Tool-based conceptual insights and transferable operation
Task-based tool crafting
4.
4.1
4.2
4.3
AI system design:
Project-based design thinking
Project-based system configuration
Project-based iteration
The three forms of assessment under AI system design are based on the virtual environment
of Teachable Machine and a simulation project on the design, training, testing and
optimization of an AI system. The project should be defined around themes relating to the
real-world needs of promoting social inclusion, and using data on local languages or cultural
features when training AI models. One critical aspect of the integrated AI competency is the
comprehensive ability to iterate AI systems based on feedback, and therefore traditional
methods such as paper-based testing should be expanded upon to include metrics that
capture a student’s ability to conduct technological conceptualization, and create prototypes
and processes for improvements, together with their technical expertise demonstrated in
the projects.
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AI competency framework for students – Chapter 5: Applying the framework
Table 5. Examples of assessment tasks
COMPETENCY
ASPECTS
PROGRESSION LEVELS
Understand
Human-centred 1.1 Conflict-based opinion
taking
mindset
1.1.0 An integral paper
and/or computer-based
test on the main points of
‘Human agency’.
Apply
Create
1.2 Conflict-based critical
evaluation
1.3 Conflict-based social interactions
1.2.0 An integral paper and/
or computer-based test on
the main points of ‘Human
accountability’.
1.3.0 An integral paper and/or
computer-based test on the main
points of ‘Social responsibility’.
1.3.1 Will AI eventually help
humans remove the drivers of
1.1.1 Can AI be used
1.2.1 The media reported
climate change and protect the
in supporting human
that artificial general
planet’s well-being? Should
decisions on values and
intelligence will arrive by
human societies mobilize all
social issues? Name a
2030 and will overpower
resources to unlimitedly train AI
weakness of current AI
humans in almost all areas,
models? Or has the training of
technologies in supporting while some AI experts have
AI models generated irreversible
decisions in relation
said AGI may never emerge. impacts on climate change?
to values, social issues
Who is correct? Evaluate
Analyse how some AI systems can
and personal emotional
whether some selected media affect environments and climate
reactions.
reports of AI go beyond the
change, and how their methods
genuine capabilities of AI
could be optimized.
1.1.2 What will happen
technologies.
if humans don’t take
1.3.2 Will AI become
accountability in the
1.2.2 In the future, will all
indispensable and trustworthy
conceptualization and
minutes of daily meetings
co-workers of humans or will AI
design of AI systems?
and administrative reports
threaten the safety, inclusion,
be drafted by AI? Do the
equity, justice and other social
1.1.3 Will machine agency
next generation of students norms of human societies?
become stronger than
still need to learn how to
Critically reflect on the potential
human agency, and take
synthesize materials and draft impact of AI on human societies.
over more and more
reports? Assess whether a
human agency? Explain
1.3.3 Will AI create jobs for all
particular problem in life or
your opinion.
groups
of people equally or
subject learning can and/
will the deployment of AI cause
or should be solved with AI
more inequality in economic
methods.
development in the connection
of global markets and your local
context? Critically evaluate why
AI has become increasingly
important and how it may affect
your local economy and job
market.
1.3.4 AI companies have claimed
that they are developing AI tools
for all. Will AI enhance or threaten
inclusion and equity? Critically
evaluate the implications of the
wide adoption of AI for inclusion
and equity in your local context.
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AI competency framework for students – Chapter 5: Applying the framework
COMPETENCY
ASPECTS
Ethics of AI
PROGRESSION LEVELS
Understand
Apply
Create
2.1 Scenario-based ethical
value orientation
2.2 Scenario-based ethical
behaviours
2.3 Scenario-based rule-making
2.1.0 An integral paper
and/or computer-based
test on the main points of
‘Ethical principles’.
2.2.0 An integral paper and/
or computer-based test on
the main points of ‘Safe and
responsible use’.
2.1.1 You have never
expressed consent to
the use of your personal
data to train AI models,
so are your personal
data protected and safe?
Describe how personal
online data have been
collected and used without
consent.
2.2.1 Explain why data
security must be considered
when developing and using
AI applications.
2.2.2 If we want to benefit
from the useful services
offered by an AI system, is
it necessary to forego some
of our personal privacy to
enjoy the benefits? Explain
2.1.2 You have only
why data privacy must be
entered your personal data considered when developing
in the prompt to request
and using AI applications.
a ‘trustworthy’ generative
2.2.3 ‘I have tried many AI
AI system to help you
platforms and they always
draft a recommendation
provided service exceeding
letter. Can you be sure
my expectations, so I don’t
your private data won’t be
need to be provided with the
disclosed? Describe how
explanation on how these AI
sensitive personal data
models work?’ Evaluate this
may be collected through
statement and describe the
prompts or interactions
concept of explainable AI.
with AI systems.
2.2.4 ‘I used a photo of one
2.1.3 Video-sharing
of my friends to generate a
platforms such as YouTube
video using a generative AI
and TikTok look as if they
tool and it looks very real,
can understand what
and I posted it online for fun;
sorts of videos the users
I used a generative AI system
may like and know how
to author essays based on
to recommend videos
my ‘creative’ prompts and I
that will be of interest
published them in my name.’
to users. Please identify
Evaluate one or both of these
ethical issues around the
statements and describe
video-recommendation
potential legal problems
algorithms used by video
that may arise when using
platforms.
AI-generated content or
claiming it as ‘your’ work.
74
2.3.0 An integral paper and/or
computer-based test on the main
points of ‘Co-creating ethical rules’.
2.3.1 Has your country or school
(district) developed regulations
on the use of AI (or generative
AI)? If yes, critically evaluate the
regulations against core principles
of UNESCO’s Recommendation on
the Ethics of AI and/or with the EU
AI Act. If no, develop a proposal to
justify the necessity of regulations
and outline the main points they
should cover.
2.3.2 Co-create ethical guidance
for yourself and your peers on the
use of video-recommendation
platforms or generative AI
systems.
2.3.3 Co-create a set of ethical
rules for the safe and responsible
use of AI in your schools and at
home.
2.3.4 Co-create regulatory rules
for the brain-computer interface
(BCI) technology
AI competency framework for students – Chapter 5: Applying the framework
COMPETENCY
ASPECTS
AI techniques
and
applications
PROGRESSION LEVELS
Understand
Apply
Create
3.1 Problem-based
AI knowledge and
understanding
3.2 Tool-based conceptual
insights and transferable
operation
3.3 Task-based tool crafting
3.1.0 Competency-based
or criterion-referenced
examination on key
conceptual knowledge
on AI.
3.2.0 Criterion-referenced,
computer-based
examination on the
fluency, transferability and
adaptability of operational
skills on data, algorithms and
programming.
3.1.1 Describe or
exemplify (using tools)
what AI is and is not; or
exemplify personal, schoolbased or public tools that
are supported by AI.
3.1.2 Explain the
difference between strong
AI and weak AI.
3.1.3 Describe the basic
concept of big data; give
a couple of examples of
misusing big data.
3.2.1 Exemplify applications
which use any of the
following: natural language
processing, computer vision,
speech recognition, image
recognition, autonomous
agent systems, emotion
detection, data-based
prediction or generative AI.
3.3.0 Computer-based individual
or group work to customize
existing AI toolkit(s) to create a
task-based AI tool.
3.3.1 Explain how sensors,
crawling software, and other tools
are used by AI researchers and
designers to collect data that can
be used to train AI models.
3.3.2 Explain and/or demonstrate
by operation how to find and
reuse open-source datasets
and libraries of AI algorithms;
evaluate the benefits and risks in
comparison with AI options from
proprietary enterprises.
3.3.3 Draft a design-anddevelopment plan on a task-based
AI tool to address real-world
needs in and beyond the local
context. The plan should cover
3.1.4 Explain how
the following criteria on an agemachine-learning models
3.2.3 Exemplify typical
appropriate level: critical analysis
are trained, tested and
AI algorithms under the
of existing AI tools, assessment of
optimized; explain why
categories of supervised
need for data, methods to collect
data play an important
learning, unsupervised
and process data, appropriate
role in the training,
learning and reinforcement
AI algorithms and programming
development and further
learning; exemplify tools that languages, open-source AI tools or
iterations of an AI model.
use some of these typical
systems that can be customized or
algorithms.
3.1.5 Explain how deep
fine-tuned, and parameters for the
learning relates to machine
testing of the AI tools.
3.2.4 Explain what AI
learning.
algorithms are used and
integrated by a given
3.1.6 Define the term
generative AI system.
‘artificial neural network’
(or other key concepts
3.2.5 Exemplify two to three
applicable for the
open-source datasets and
‘Understand’ level).
libraries of AI algorithms;
explain the advantages and
limitations of open-source
datasets and algorithm
libraries.
3.2.2 Explain how supervised
learning, unsupervised
learning and reinforcement
learning work on a basic level.
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AI competency framework for students – Chapter 5: Applying the framework
COMPETENCY
ASPECTS
AI system
design
PROGRESSION LEVELS
Understand
Apply
Create
4.1 Project-based design
thinking
4.2 Project-based system
configuration
4.3 Project-based iteration
4.1.0 Simulated tests
on problem-scoping
for AI system design.
Request that students
produce a report and/or
oral defence on problem
scoping or on a project
proposal. The report can
be evaluated according
to the following criteria:
why AI should be used for
the problem based on a
checklist; and the problem
statement including key
requirements or features
of the AI systems such as
algorithms, datasets and
functionalities.
4.2.0 Computer-based
tests on the architectural
configuration of AI. The
simulated operation can be
evaluated using the following
criteria: assessment and
selection of frameworks for
AI architectures; evaluation
and choice of solutions for
the layers and components
of the AI architecture;
the configuration of a
prototype architecture;
and the presentation of the
configuration.
4.1.1 Explain why a
specific real-world
challenge (given by
teachers) should not be
solved by an AI tool.
4.1.2 Computer-based
test on data preprocessing
techniques, drawing on
open-source datasets
including adjusting the
data augmentation,
handling outliers, analysing
dataset skew or imbalance,
training the model based
on modified datasets,
and observing how data
preprocessing affects
the model’s performance
compared to the given
dataset.
76
4.2.1 Explain how opensource datasets and libraries
of AI programming can be
leveraged to build an AI
system including locally
accessible cloud computing
platforms or operating
systems, and software
needed by the training of
machine-learning models.
4.2.2 Explain what criteria
should be considered to
optimize for efficiency and
minimize computational
resource waste when
configuring AI architecture.
4.2.3 Calculate the selected
AI model’s consumption
of computing resources,
and design strategies for
improving the efficiency of
AI methods to reduce its
environmental impact.
4.3.0 Computer-based simulated
optimization of a simple AI model,
including operational optimization
of the datasets, algorithms and
parameter adjustment, and the
design of functionalities and
interfaces; and/or reconfiguration
of the architectures, including
modifying the problem-scoping.
4.3.1 Design a set of metrics
for the performance-testing of
an exemplar AI system. Explain
what metrics can be designed
or adapted to support the
measurement of the system’s
performance and to collect
feedback from end users on
the societal implications and
environmental impact. Exemplify
open-source tools that can
conduct and report on the
performance-testing of an AI
system.
4.3.2 Draft a report to explain
what decision should be taken
on an AI system and why, based
on the findings of simulated
performance tests and user
feedback. Include explanations
of decisions to optimize,
reconfigure and shut down the
system; present the plan for
optimization or reconfiguration, or
for mitigation strategies if the AI
system has the potential to cause
harm.
4.3.3 Exemplify locally accessible
online communities of AI cocreators; explain what a student
can do in these communities.
AI competency framework for students – Chapter 5: Applying the framework
Agile formats of concrete assessments
and corresponding grading scales that
fit neatly into different assessment items
and objectives should be designed,
tested and optimized. These may include
formative and peer assessments in the
form of reflective essays, oral presentations
or reports of users’ tests of AI tools; and
summative examinations on paper and/or
via computer-based or unplugged design,
including prototypes of AI tools or drawing
of algorithms, essays about case studies
on AI’s ethical issues, technical reports on
the design and development of AI tools
or systems, the fine-tuning or simulated
training of AI models, and the assembling or
creation of hardware.
This large array of concrete methods should
be examined in a nuanced manner against
the specific needs of aspects and infused
flexibly in the implementation of the AI CFS.
The use of AI tools for assessments also
emerges as a new supplementary method
of assessment, for example automating the
collection of data on learning processes and
formative mastery directly from students or
learning management systems, personalizing
assessments for students according to their
ability or linguistic and cultural background,
or facilitating teachers’ decision-making on
teaching strategies. While the opportunities
being enabled by AI to enhance assessments
should be dynamically reviewed and
properly leveraged, it is critical to examine
and regulate ethical issues concerning the
collection and use of students’ data; the
risks of using AI recommendations and
predictions in assessments, especially those
with high stakes; and the reduction of
teachers’ agency in assessments, particularly
the opportunities for teachers to gain
insights from analysing learning processes.
77
AI competency framework for students – Conclusion
Conclusion
The AI competency framework for students
charts an action-oriented programme
based on three basic assumptions about
the role of education in responding to the
pervasive adoption of AI in today’s world.
The first is that the education sector, rather
than merely adapting to AI systems and
tools, must be proactive in developing the
competencies required to shape ethical
and environmentally-friendly AI. Second,
that students should be equipped with the
competencies to act both as critical and
responsible users and co-creators of AI, as
well as leaders in defining and designing
the next generation of AI technologies.
The third assumption is that students’ AI
competencies are to be constructed around
the convergence of a human-centred
mindset and attitudes, internalized ethics
of AI, transferable conceptual knowledge
and skills on AI, as well as future-proof
thinking relative to AI system design. As
AI competency development goes far
beyond mere technical skills associated
with learning to code or to operate AI
tools, the integration of AI-related learning
78
requires an interdisciplinary approach to
curricular integration spanning subjects
related to science, technology, engineering,
art and mathematics, to social studies and
citizenship education.
This AI competency framework for
students is the first attempt to provide
a global blueprint to steer a humancentred integration of AI-related learning
in curriculum. Informed by expertise and
consultations at the international level, the
framework serves as a global reference to
be adapted across diverse local educational
contexts. It is only through adapting and
testing the framework among teachers and
teacher educators in diverse settings, and
surfacing insights from their contextualized
practice, that the global framework can
be further refined. As such, the framework
is a living document which will need to
be continuously reviewed on the basis of
analysis of practice in a diversity of contexts,
as well as in response to new iterations of AI
technologies that will emerge.
AI competency framework for students – References
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AI competency framework for students – Endnotes
Endnotes
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The principle of proportionality in AI encompasses the idea that AI systems should be designed
and deployed in a manner that appropriately balances risks and benefits, respects human rights,
and aligns with societal values and interests. See the Recommendation on the Ethics of AI (UNESCO,
2022a) for more on the proportionality of AI.
RAISE stands for ‘Responsible AI for Social Empowerment and Education’
See https://ubuntu.com
See https://machinelearningforkids.co.uk
See https://teachablemachine.withgoogle.com
See https://www.tensorflow.org
See https://keras.io
See https://everyday-ai.org/resources/search?f%5B0%5D=tools%3A201
See https://www.aiunplugged.org
See https://iste.org/blog/3-unplugged-activities-for-teaching-about-ai
See https://raise.mit.edu/daily
See http://yann.lecun.com/exdb/mnist
See https://www.cs.toronto.edu/%7Ekriz/cifar.html
See https://www.image-net.org/index.php
See https://teachablemachine.withgoogle.com
See https://appinventor.mit.edu
See https://pytorch.org
See https://keras.io
See https://pypi.org/project/beautifulsoup4
AI competency framework
for students
The AI competency framework for students presented here is based on an
ambitious vision that extends well beyond popular notions of AI literacy.
It aims to support students to grow towards being not only effective and
ethical users of AI tools, but also co-creators in the design of more inclusive
and environmentally sustainable AI. The framework defines the values, as well
as the foundational knowledge and transferable skills, required to critically
understand and use AI systems in a safe, effective and meaningful manner at
different levels of mastery. The framework also proposes detailed specifications
on what AI topics can be covered and what pedagogical methods may be
deployed to facilitate students’ understanding, application, and creation of AI.
It further provides guidance for the curricular integration of AI-related learning,
the organization of learning sequences, and the design of competence-based
assessments. Seen as an integral set of capabilities required for responsible
citizenship in the era of AI, the competencies outlined in this framework
are based on principles of inclusivity, the centrality of human agency, nondiscrimination, and respect for linguistic and cultural diversity.
9 789231 007095
Sustainable
Development
Goals