Generative AI and Academic Integrity: A Critical Analysis from an Education Policy Perspective
Keywords:
Academic Integrity, Critical Policy Analysis, Education Policy
Abstract
The penetration of generative AI in higher education has triggered an epistemological disruption. The changes has given rise to an academic integrity crisis characterized by new practices such as AI-assisted plagiarism, AI-based ghost-writing, and fabrication of data and references due to AI hallucination. The research aims to analyze the impact of generative AI on academic integrity, identify education policy dilemmas, and formulate adaptive governance models in higher education. The research uses a qualitative approach with a literature study design through critical policy analysis. Data was obtained from the policy documents, educational institution regulations, as well as scientific literature. The analysis was carried out using content analysis techniques with an interpretive-critical approach to normative changes in educational governance in the AI era. The research results show that a restrictive approach in the form of a total ban on the use of AI is no longer adequate because it is unrealistic, difficult to implement, and has the potential to widen the AI literacy gap. Academic integrity needs to be reconstructed from a prohibition-based compliance paradigm to a transparency-based accountability paradigm. In this paradigm, integrity is no longer measured using AI, but by how AI is used in a transparent, responsible and scientifically accountable manner.
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Bacchi, C. (2025). What's the Problem Represented to Be? A New Thinking Paradigm. London: Routledge.
Bender, E., Gebru, T., Major, A. M., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? ACM conference on fairness, accountability, and transparency.
Bretag, T. (2016). Handbook of academic integrity.
Chan, C. K. (2023). Is AI changing the rules of academic misconduct? An in-depth look at students' perceptions of'AI-giarism. airxiv.
Cotton, D. R., Cotton, P. A., & ShipwaY, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International .
Dijk, J. V. (2020). The digital divide. John Wiley & Sons.
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Karf, A. K., . . . Albashrawi, M. A. (2023). So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management.
Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of advanced nursing, 107-115.
Fawns, T., Henderson, M., Matthews, K., Oberg, G., Liang, Y., Walton, J., . . . McLean, J. (2024). Gen AI and student perspectives of use and ambiguity. Proceedings of Australasian Society for Computers in Learning in Tertiary Education (pp. 132-134). Society for Computers in Learning in Tertiary Education.
Feenberg, A. (1991). Critical theory of technology. New York: Oxford University Press.
Fischer, F., Torgerson, D., Durnová, A., & Orsini, M. (2015). Introduction to critical policy studies. Edward Elgar Publishing.
Floridi, L., & Chiriatti., M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and machines.
Holmes, W., & Miao, F. (2023). Guidance for generative AI in education and research. UNESCO.
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., . . . Fung, P. (2023). Survey of hallucination in natural language generation. ACM computing surveys.
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., . . . Michaeli, T. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and individual differences.
Kingdon, J. W., & Stano, E. (1984). Agendas, Alternatives, And Public Policies. Boston: Little, Brown.
Krippendorff, K. (2018). Content analysis: An introduction to its methodology. Sage publications.
Kuhlmann, S., & Rip, A. (2018). Next-generation innovation policy and grand challenges. Science and public policy, 448-454.
Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. Patterns.
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. Proceedings of the 2020 CHI conference on human factors in computing systems.
Luckin, R., & Holmes, W. (2016). Intelligence unleashed: An argument for AI in education.
Macfarlane, B., Zhang, J., & Pun, A. (2014). Academic integrity: A review of the literature. Studies in higher education.
Ng, D., Leung, J., Chu, K., & Qiao, M. (2021). AI literacy: Definition, teaching, evaluation and ethical issues. Proceedings of the association for information science and technology.
OECD. (2019). OECD principles on artificial intelligence. OECD.
Ostrom, E. (2010). Beyond Markets and States: Polycentric Governance of Complex Economic Systems . American Economic Review, 641–72.
Papaioannou, D., Sutton, A., & Booth, A. (2016). Systematic approaches to a successful literature review.
Perkins, M. (2023). Academic integrity considerations of AI large language models in the post-pandemic era: ChatGPT and beyond. Journal of University Teaching and Learning Practice.
Pudasaini, S., Miralles-Pechuán, L., Lillis, D., & Salvador, M. (2024). Survey on plagiarism detection in large language models: The impact of chatgpt and gemini on academic integrity. Arxiv.
Ragnedda, M., & Muschert, G. W. (2013). The digital divide. Florence: KY: Routledge.
Selwyn, N. (2019). Should robots replace teachers?: AI and the future of education. John Wiley & Sons.
Taeihagh, A. (2021). Governance of artificial intelligence. Policy and society 40.2, 137-157.
Tlili, A., Huang, R., Mustafa, M. Y., Zhao, J., Bozkurt, A., Xu, L., . . . Burgos, D. (2023). Speaking of transparency: Are all artificial intelligence (AI) literature reviews in education transparent? Journal of Applied Learning & Teaching.
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments.
Utami, P. N., Alhamid, I. N., Fadhilah, N. P., Tabitha, A. H., & Aini, A. Q. (2025). Dinamika Perubahan Bentuk Plagiarisme di Kalangan Mahasiswa DKI Jakarta: Studi Kasus Enam Perguruan Tinggi. Risenologi.
Wiggins, G. (1998). Educative Assessment. Designing Assessments To Inform and Improve Student Performance. San Francisco: Jossey-Bass Publishers.
Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology.
Xiao, Y., & Watson, M. (2019). Guidance on conducting a systematic literature review. Journal of planning education and research, 93-112.
Zawacki-Richter, O., Marín, V., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators? International journal of educational technology in higher education.
Published
2026-06-16
How to Cite
Faradila, A. (2026). Generative AI and Academic Integrity: A Critical Analysis from an Education Policy Perspective. International Journal of Education, Vocational and Social Science , 5(03), 1630-1647. https://doi.org/10.63922/ijevss.v5i03.4359
Contact Person +6282139474255
Email : ijevss@citakonsultindo.or.id / editor.ijevss@gmail.com
