The Governance of AI in Education: Developing Ethical Policy Frameworks for Adaptive Learning Technologies
Abstract
The integration of artificial intelligence (AI) in education has transformed adaptive learning by personalizing instruction, automating assessments, and enhancing student engagement. However, the ethical challenges of AI governance, including algorithmic bias, data privacy concerns, transparency gaps, and accountability issues, necessitate a structured regulatory framework. This study develops the Ethical AI Governance Framework for Adaptive Learning (EAGFAL) to address governance gaps and ensure fair, transparent, and responsible AI adoption in education. Using secondary data analysis and comparative case studies, the study examines global AI governance models, regulatory best practices, and ethical risks in AI- driven education. Findings highlight that inconsistent regulatory approaches contribute to disparities in AI deployment, with some regions favoring market-driven AI policies, while others enforce strict legal oversight. The study recommends bias mitigation strategies, explainable AI mechanisms, and robust data governance policies to ensure AI-driven education remains inclusive, ethical, and student-centered. EAGFAL offers practical policy insights for governments, educators, and AI developers, advocating for cross-sector collaboration to align AI innovation with ethical responsibility. Future research should explore long-term impacts, scalability, and evolving governance structures for ethical AI integration in education.
Keywords
Ai governance in education
Ethical ai policy
Adaptive learning systems
References
Akavova, A., Temirkhanova, Z., & Lorsanova, Z. (2023). Adaptive learning and artificial intelligence in the educational space. E3S Web of Conferences, 451, 06011. https://doi.org/10.1051/e3sconf/202345106011 Aggarwal, D. (2023). Exploring the scope of artificial intelligence (AI) for lifelong education through personalised & adaptive learning. Journal of Artificial Intelligence, Machine Learning and Neural Network. 4(01), 21–26. https://doi.org/10.55529/jaimlnn.41.21.26 Chen, X. (2020). AI + Education: Self-adaptive learning promotes individualized educational revolutionary. Proceedings of the 2020 6th International Conference on Education and Training Technologies. 44 – 47 https://doi.org/10.1145/3399971.3399984 Chetyrbok, P. V., Shostak, M. A., & Alimova, L. U. (2021). Adaptive learning using artificial intelligence in distance education. V.I. Vernadsky Crimean Federal University; Crimean Engineering and Pedagogical University. Ezzaim, A., Dahbi, A., Haidine, A., & Aqqal, A. (2023). AI-based adaptive learning: A systematic mapping of the literature. Journal of Universal Computer Science. 29(10): 1161-1198. https://doi.org/10.3897/jucs.90528 Jiao, D. (2024). AI-driven personalization in higher education: Enhancing learning outcomes through adaptive technologies. Adult and Higher Education. 6, 42-46. https://doi.org/10.23977/aduhe.2024.060607 Li, H., Xu, T., Zhang, C., Chen, E., Liang, J., Fan, X., Li, H., Tang, J., & Wen, Q. (2024). Bringing generative AI to adaptive learning in education. ArXiv. https://doi.org/10.48550/arXiv.2402.14601 Sehgal, P., Zaidi, N. N., & Rana, S. (2024). Application of AI in the field of education: Dynamic course generation. International Journal for Research in Applied Science and Engineering Technology. 12(5), 31-40. https://doi.org/10.22214/ijraset.2024.61320 Slomp, E. M., Ropelato, D., Bonatti, C., & Da Silva, M. D. (2024). Adaptive learning in engineering courses: How artificial intelligence (AI) can improve academic outcomes. 2024 IEEE World Engineering Education Conference (EDUNINE), 1-6. https://doi.org/10.1109/EDUNINE60625.2024.10500580 Strielkowski, W., Grebennikova, V., Lisovskiy, A., Rakhimova, G., & Vasileva, T. (2024). AI?driven adaptive learning for sustainable educational transformation. Sustainable Development. 1-27. https://doi.org/10.1002/sd.3221 Chinta, S. V., Wang, Z., Yin, Z., Hoang, N., Gonzalez, M., Quy, T. L., & Zhang, W. (2024). FairAIED: Navigating fairness, bias, and ethics in educational AI applications. arXiv. https://doi.org/10.48550/arXiv.2407.18745 Devi, S., Boruah, A. S., Nirban, S., Nimavat, D., & Bajaj, K. K. (2023). Ethical considerations in using artificial intelligence to improve teaching and learning. Tuijin Jishu/Journal of Propulsion Technology, 44(4). https://doi.org/10.52783/tjjpt.v44.i4.966 Ebers, M. (2019). Regulating AI and robotics: Ethical and legal challenges. Materials Science Research Network. https://doi.org/10.2139/ssrn.3392379 Farooqi, M. T. K., Amanat, I., & Awan, S. M. (2024). Ethical considerations and challenges in the integration of artificial intelligence in education: A systematic review. Journal of Excellence in Management Sciences, 3(4). https://doi.org/10.69565/jems.v3i4.314 Geist, M. A. (2021). AI and international regulation. In F. Martin-Bariteau & T. Scassa (Eds.), Artificial intelligence and the law in Canada. LexisNexis Canada. Available at SSRN: https://ssrn.com/abstract=3734671 Ibrahim, S. M., Alshraideh, M., Leiner, M., AlDajani, I. M., & Bettaz, O. (2024). Artificial intelligence ethics: Ethical consideration and regulations from theory to practice. IAES International Journal of Artificial Intelligence (IJ-AI), 13(3), 3703–3714. https://doi.org/10.11591/ijai.v13.i3.pp3703-3714 Li, Z. (2024). The shadow of the algorithm: The ethical blind spot of artificial intelligence education. Journal of Education, Humanities and Social Sciences, 26, 1146–1152. https://doi.org/10.54097/pk0ajq60 Oladele, I., Orelaja, A., & Akinwande, O. T. (2024). Ethical implications and governance of artificial intelligence in business decisions: A deep dive into the ethical challenges and governance issues surrounding the use of artificial intelligence in making critical business decisions. International Journal of Latest Technology in Engineering, Management & Applied Science, 13(2), 48–54. https://doi.org/10.51583/ijltemas.2024.130207 Slimi, Z., & Carballido, B. V. (2023). Navigating the ethical challenges of artificial intelligence in higher education: An analysis of seven global AI ethics policies. TEM Journal, 12(2), 123–130. Wang, Y. (2024). Government approaches and discussions on addressing AIGC challenges in education. Frontiers in Educational Research, 7(6), 192–196. https://doi.org/10.25236/fer.2024.070629 Arcilla, A. O., Espallardo, A. K. V., Gomez, C. A. J., Viado, E. M. P., Ladion, V. J. T., Naanep, R. A. T., Pascual, A. R. L., Artificio, E. B., & Tubola, O. D. (2023). Ethics in AI governance: Comparative analysis, implication, and policy recommendations for the Philippines. 2023 27th International Computer Science and Engineering Conference (ICSEC), 319-326. https://doi.org/10.1109/ICSEC59635.2023.10329756 Bai, J. Y. H., Marin, V., Jung, I., & Zawacki-Richter, O. (2023). Future prospects of artificial intelligence in education: A preliminary analysis of educator perspectives from focus groups in Japan, Spain, and Germany. Ubiquity Proceedings. https://doi.org/10.5334/uproc.74 Bobrytska, V., Krasylnykova, H., Beseda, N. A., Krasylnykov, S. R., & Skyrda, T. S. (2024). Artificial intelligence (AI) in Ukrainian higher education: A comprehensive study of stakeholder attitudes, expectations, and concerns. International Journal of Learning, Teaching and Educational Research. 23(1), 400-426, https://doi.org/10.26803/ijlter.23.1.20 Franzke, A. S. (2022). An exploratory qualitative analysis of AI ethics guidelines. J. Inf. Commun. Ethics Soc., 20, 401-423. https://doi.org/10.1108/jices-12-2020-0125 Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O., Rodrigo, M. T., Cukurova, M., Bittencourt, I., & Koedinger, K. (2021). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 32, 504-526. https://doi.org/10.1007/s40593-021-00239-1 Krebs, A. (2024). Redefining the role of educators in an AI-enriched educational landscape – a project proposal. Proceedings of the International Academic Conference on Education, Teaching and Learning. 1(1), https://doi.org/10.33422/iacetl.v1i1.598 Hong, Y., Nguyen, A., Dang, B., & Nguyen, B. P. T. (2022). Data ethics framework for artificial intelligence in education (AIED). 2022 International Conference on Advanced Learning Technologies (ICALT), 297-301. https://doi.org/10.1109/ICALT55010.2022.00095 Lee, T., Liu, S., Wei, Y., & Chang, L. (2020). A comparative study on ethics guidelines for artificial intelligence across nations. In T. X. Bui & R. H. Sprague Jr. (Eds.), Proceedings of the 53rd Hawaii International Conference on System Sciences, 289-295. https://doi.org/10.1007/978-3-030-63885-6_33 Uygun, D. (2024). Teachers’ perspectives on artificial intelligence in education. Advances in Mobile Learning Educational Research, 4(1), 931-939. https://doi.org/10.25082/AMLER.2024.01.005 Dwii, B., & John, P. J. (2024). Embracing AI in education: Indonesian university students' perspectives on opportunities and concerns. The American Journal of Social Science and Education Innovations. 6(9), 140-150. https://doi.org/10.37547/tajssei/volume06issue09-15
