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Challenges that generative artificial intelligence poses to higher education and management and the countermeasures— A visualized analysis of literature from 2019 to 2023 using CiteSpace

Published: 30 May 2024 Publication History

Abstract

In this study, a visualized analysis of the target literature is made from different perspectives using CiteSpace and the literature analysis module of the Chinese National Knowledge Infrastructure (CNKI) to identify the challenges that the generative artificial intelligence (GAI) technology poses to higher education and management and corresponding countermeasures. Our analysis revealed that GAI would reduce the demand for teachers, weaken the teacher-student tie, and exacerbate the students’ digital overdependence, causing conflicts in data ethics and intellectual property. Thus, recommendations are made here for the four parties involved in the research: the government should formulate laws to strengthen network supervision; schools need to advance the digital reform on talent training models; teachers need to keep up with the latest educational theories to shift their roles; and students need to remain critical and independent thinkers in the age of educational digitalization 2.0.

References

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UNESCO. Guidance for generative AI in education and research 2021 [EB/OL]. https://unesdoc.unesco.org/search/d66b0540-408b-4b88-8448-1dfb94d04c41.
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Villanueva, C.C.Education Management Information System (EMIS) and the Formulation of Education for All (EFA) Plan of Action, 2002-2015 [EB/OL]. https://unesdoc.unesco.org/ark:/48223/pf0000156818.
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Herodotou, C., Gilmour, A., Boroowa, A., Rienties, B., Zdrahal, Z. and Hlosta, M. 2017. Predictive modelling for addressing students’ attrition in higher education: The case of OU Analyse. The Open University, Milton Keynes, United Kingdom[J/OL]. http://oro.open.ac.uk/49470/.
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Ferguson, R., Brasher, A., Clow, D., Cooper, A., Hillaire, G., Mittelmeier, J., Rienties, B., Ullmann, T. and Vuorikari, R. 2016. Research Evidence on the Use of Learning Analytics: Implications for Education Policy. The Open University, Milton Keynes, United Kingdom[J/OL]. http://oro.open.ac.uk/48173/.
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Jobin, A., Ienca, M., and Vayena, E. Artificial Intelligence: The global landscape of ethics guidelines [J]. Nature Machine Intelligence, 2019, 1(9):389–399.
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Lupton, D. and Williamson, B. The datafied child: The dataveillance of children and implications for their rights’ [J]. New Media & Society, 2017, 19(5):780–794.
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Douglas, L. 2017. AI is not just learning our biases; it is amplifying them. Medium. [J/OL]. https://medium.com/@laurahelendouglas/ai-is-not-just-learningourbiases-it-is-amplifying-them-4d0dee75931d.
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National Telecommunications and Information Administration of China. Notice for collecting public opinions on the “management of GAI-enabled service (exposure draft)” [EB/OL], 2023. http://www.cac.gov.cn/2023-04/11/c_1682854275475410.htm.
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Chayka, K. My A.I. Writing Report. New York, The New Yorker 2023 [J/OL]. https://www.newyorker.com/culture/infinite-scroll/my-aiwriting-robot.

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    ICIEAI '23: Proceedings of the 2023 International Conference on Information Education and Artificial Intelligence
    December 2023
    1132 pages
    ISBN:9798400716157
    DOI:10.1145/3660043
    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 the author(s) 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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    Published: 30 May 2024

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