Artificial intelligence in higher education: A human-centered implementation framework
Yuan Kang
City University Malaysia/Sanya City Vocational College
M. KazemChamran
Faculty of Information Technology, City University Malaysia
DOI: https://doi.org/10.59429/esta.v13i1.13388
Keywords: artificial intelligence; higher education; generative AI; assessment; academic integrity; governance; AI literacy
Abstract
Artificial intelligence (AI) is increasingly integrated into higher education through machinelearning (ML) and natural language processing (NLP) systems that support teaching, learning, student services, and institutional decision-making. Learning analytics models can enable earlier and more targeted student support, while adaptive and tutoring systems personalize practice and feedback. More recently, generative AI and large language models (LLMs) have introduced dialog-based assistance for explanation, feedback drafting, and study support at scale. However, these benefits come with nontrivial risks, including unreliable output, bias, privacy leakage, and uncertainty about acceptable use in assessment. This article presents a concise, human-centered implementation framework that links AI capability selection to learning outcomes, data governance, assessment design, and post-deployment monitoring, enabling institutions to deploy AI in ways that are pedagogically meaningful, accountable, and sustainable.
References
[1] E. M. Bender, T. Gebru, A. McMillan-Major, et al. "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?," in Proc. ACM Conf. Fairness, Accountability, and Transparency (FAccT), 2021, pp. 610–623, doi: 10.1145/3442188.3445922.
[2] D. R. E. Cotton, P. A. Cotton, J. R. Shipway, "Chatting and Cheating: Ensuring Academic Integrity in the Era of ChatGPT," Innovations in Education and Teaching International, 2024, doi: 10.1080/14703297.2023.2190148.
[3] H. Crompton and D. Burke, "Artificial Intelligence in Higher Education: The State of the Field," Int. J. Educ. Technol. Higher Educ., vol. 20, art. no. 22, 2023, doi: 10.1186/s41239-023-00392-8.
[4] E. Kasneci et al., "ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education," Learning and Individual Differences, vol. 103, art. no. 102274, 2023, doi: 10.1016/j.lindif.2023.102274.
[5] S. A. D. Popenici and S. Kerr, "Exploring the Impact of Artificial Intelligence on Teaching and Learning in Higher Education," Res. Pract. Technol. Enhanc. Learn., vol. 12, art. no. 22, 2017, doi: 10.1186/s41039-017-0062-8.
[6] UNESCO, "Guidance for Generative AI in Education and Research," Paris, France, Rep., 2023. UNESDOC 0000386693.
[7] O. Zawacki-Richter, V. I. Marín, M. Bond, and F. Gouverneur, "Systematic Review of Research on Artificial Intelligence Applications in Higher Education—Where Are the Educators?," Int. J. Educ. Technol. Higher Educ., vol. 16, art. no. 39, 2019, doi: 10.1186/s41239-019-0171-0.