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A study on the current development of Artificial Intelligence in education industry in China

Published: 20 July 2021 Publication History

Abstract

This article first explained the definition of AI in education (AIEd) and reported findings regarding the current development of AIEd industry in the Chinese context. The research design is a context-specific case study using the supply and demand theoretical framework. From a demand-side perspective, the author made an in-depth analysis of the specific AI applications employed in different educational scenarios, including the automated speaking assessment system, the content-based image retrieval system, adaptive learning system, AI-supported classrooms, and AI-assisted campus safety system. For the supply analysis of the AIEd industry, this article summarized key AIEd industry chains and technologies currently widely used in China, obtaining the industry market scale through data collected from different sources. In addition, the iFLYTEK company, as a typical enterprise in the AIEd industry, was taken as a medium to conduct a case analysis. The employment of various AI applications in smart classrooms, smart exams, and smart terminals were comprehensively discussed. In a nutshell, this article discussed the development status and future trends of Chinese AIEd industry, with an aim to offer suggestions and implications for education practitioners.

References

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Ingrid Nagyová. E-learning environment as intelligent tutoring system[C]// INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2016). AIP Publishing LLC, 2017.
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Jia J. Design, Implementation and Evaluation of Blended Learning for the Undergraduate Course “Education and Artificial Intelligence”[J]. 2018.
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Darko Obradovic. COSAIR: A Platform for AI Education and Research in Computer Strategy Games[C]// KI 2008: Advances in Artificial Intelligence, 31st Annual German Conference on AI, KI 2008, Kaiserslautern, Germany, September 23-26, 2008. Proceedings. DBLP, 2008.
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Wu Yonghe, Liu Bowen, Ma Xiaoling. Constructing an Ecosystem of “Artificial Intelligence + Education”[J]. Journal of Distance Education, 2017.
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Cited By

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  • (2024)AI-based learning style detection in adaptive learning systems: a systematic literature reviewJournal of Computers in Education10.1007/s40692-024-00328-9Online publication date: 27-Jun-2024

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ICETT '21: Proceedings of the 2021 7th International Conference on Education and Training Technologies
April 2021
163 pages
ISBN:9781450389662
DOI:10.1145/3463531
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 July 2021

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Author Tags

  1. AI Education
  2. Adaptive Learning
  3. Education Informatization
  4. Oral Assessment
  5. Smart Classroom

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Cited By

View all
  • (2024)AI-based learning style detection in adaptive learning systems: a systematic literature reviewJournal of Computers in Education10.1007/s40692-024-00328-9Online publication date: 27-Jun-2024

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