Review Article
BibTex RIS Cite

A Framework for Responsible AI in Higher Education

Year 2025, Volume: 6 Issue: 2, 155 - 174, 31.12.2025
https://doi.org/10.55993/hegp.1788343

Abstract

The rapid integration of Artificial Intelligence (AI) into higher education presents transformative opportunities alongside significant ethical challenges. While AI promises to enhance personalized learning, streamline administration, and improve evaluations, its implementation necessitates critical examination of its ethical implications. This article delves into the multifaceted ethical dimensions of AI use in higher education, exploring key concerns related to academic integrity, data privacy in personalized learning systems, equity and access, fairness in AI-driven evaluation, and the impact on both faculty employment and student employability. Drawing upon ethical theories (deontology, utilitarianism, virtue ethics) and pedagogical considerations, it identifies risks such as the blurring lines of cheating, algorithmic bias, data misuse, and the potential devaluation of human interaction and critical skills. To navigate these complexities, the article proposes the Ethical and Pedagogical Framework for Responsible AI in Higher Education (EPF-AI), offering principles grounded in (1) transparency and accountability, (2) equity and inclusion, (3) human-centered learning, and (4) continuous ethical reflection. Ultimately, this article aims to provide higher education stakeholders with a nuanced understanding and actionable guidance for the responsible, equitable, and human-centered integration of AI into academic practice.

Ethical Statement

Ethics approval was not required for this study as it relied exclusively on existing published materials and did not involve primary data collection from human subjects.

Supporting Institution

No funding was received from any institution or organization for this study.

Thanks

Acknowledgement: During manuscript preparation, the author employed free-tier versions of AI-powered language tools, including ChatGPT, Gemini, and Claude, to enhance readability and linguistic clarity. All AI-generated suggestions were critically reviewed and revised as necessary, with the author retaining full responsibility for the final content.

References

  • Agarwal, A., & Agarwal, H. (2024). A seven-layer model with checklists for standardising fairness assessment throughout the AI lifecycle. AI and Ethics, 4(2), 299-314. https://doi.org/10.1007/s43681-023-00266-9
  • Akiba, D., & Garte, R. (2024). Leveraging AI tools in university writing instruction: Enhancing student success while upholding academic integrity. Journal of Interactive Learning Research, 35(4), 467-480. https://doi.org/10.70725/355152wkijve
  • Arslan, A., Cooper, C., Khan, Z., Golgeci, I., & Ali, I. (2022). Artificial intelligence and human workers interaction at team level: a conceptual assessment of the challenges and potential HRM strategies. International Journal of Manpower, 43(1), 75-88. https://doi.org/10.1108/IJM-01-2021-0052
  • Bains, R. (2023). Artificial intelligence assisted medical writing: With greater power comes greater responsibility. Asian Journal of Oral Health and Allied Sciences, 13(2), 1-2. https://doi.org/10.25259/ajohas_1_2023
  • Baker, R. S., & Hawn, A. (2022). Algorithmic bias in education. International Journal of Artificial Intelligence in Education, 32, 1052–1092. https://doi.org/10.1007/s40593-021-00285-9
  • Balasubramaniam, N., Kauppinen, M., Hiekkanen, K., & Kujala, S. (2022, March). Transparency and explainability of AI systems: Ethical guidelines in practice. In V. Gervasi & A. Vogelsang (Eds.), International Working Conference on Requirements Engineering: Foundation for Software Quality (pp. 3-18). Springer International Publishing. https://doi.org/10.1007/978-3-030-98464-9_1
  • Bateni, A., Chan, M. C., & Eitel-Porter, R. (2022). AI fairness: from principles to practice. arXiv preprint arXiv:2207.09833. https://doi.org/10.48550/arXiv.2207.09833
  • Bergamaschi, A., Giambruno, C., & Morales, P. (2025). Empowering schools with data: How can we achieve effective use of educational dashboards for teachers and principals? Inter-American Development Bank. https://doi.org/10.18235/0013561
  • Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., Pham, P., Chong, S. W., & Siemens, G. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21(1), 4. https://doi.org/10.1186/s41239-023-00436-z
  • Boone, B. (2017). Ethics 101: From altruism and utilitarianism to bioethics and political ethics, an exploration of the concepts of right and wrong. Adams Media Simon and Schuster.
  • Brossi, L., Castillo, A. M., & Cortesi, S. (2022). Student-centred requirements for the ethics of AI in education. In W. Holmes & K. Porayska-Pomsta (Eds.), The Ethics of Artificial Intelligence in Education (pp. 91-112). Routledge. https://doi.org/10.4324/9780429329067-6
  • Capuano, N., & Caballé, S. (2020). Adaptive learning technologies. AI Magazine, 41(2), 96-98. https://doi.org/10.1609/aimag.v41i2.5317
  • Cesaro, A., & de la Fuente-Nunez, C. (2023). Antibiotic identified by AI. Nature Chemical Biology, 19(11), 1296-1298. https://doi.org/10.1038/s41589-023-01448-6
  • Cevizci, A. (2021). Etik ahlak felsefesi (4th ed.). Say Yayınları.
  • Chaika, O. (2023). The role of artificial intelligence in higher education. Молодь і ринок, 6–7(214–215), 69-74. https://doi.org/10.24919/2308-4634.2023.287898
  • Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20(1), 38. https://doi.org/10.1186/s41239-023-00408-3
  • Conijn, R., Kahr, P., & Snijders, C. C. (2023). The effects of explanations in automated essay scoring systems on student trust and motivation. Journal of Learning Analytics, 10(1), 37-53. https://doi.org/10.18608/jla.2023.7801
  • 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, 61(2), 228-239. https://doi.org/10.1080/14703297.2023.2190148
  • Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: the state of the field. International Journal of Educational Technology in Higher Education, 20(1), 22. https://doi.org/10.1186/s41239-023-00392-8
  • Dell'Acqua, F., McFowland, E., Mollick, E. R., Lifshitz-Assaf, H., Kellogg, K., Rajendran, S., Krayer, L., Candelon, F., & Lakhani, K. R. (2023). Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity and quality. Harvard Business School Technology & Operations Mgt. Unit Working Paper, (24-013). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4573321
  • Dilmaghani, S., Brust, M. R., Danoy, G., Cassagnes, N., Pecero, J., & Bouvry, P. (2019, December). Privacy and security of big data in AI systems: A research and standards perspective. In 2019 IEEE International Conference on Big Data (Big Data) (pp. 5737-5743). IEEE. https://doi.org/10.1109/BigData47090.2019.9006283
  • Doshi-Velez, F., Kortz, M., Budish, R., Bavitz, C., Gershman, S., O'Brien, D., Scott, K., Schieber, S., Waldo, J., Weinberger, D., Weller, A., & Wood, A. (2017). Accountability of AI under the law: The role of explanation. arXiv preprint arXiv:1711.01134. https://doi.org/10.48550/arXiv.1711.01134
  • du Boulay, B. (2022). The overlapping ethical imperatives of human teachers and their Artificially Intelligent assistants. In W. Holmes & K. Porayska-Pomsta (Eds.), The Ethics of Artificial Intelligence in Education (pp. 240-254). Routledge. https://doi.org/10.1007/978-981-19-2080-6_6
  • Duan, Y., Edwards, J., & Dwivedi, Y. (2019). Artificial intelligence for decision making in the era of big data –evolution, challenges and research agenda. International Journal of Information Management, 48, 63-71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
  • Emdad, F. B., Ho, S. M., Ravuri, B., & Hussain, S. (2023). Towards a unified utilitarian ethics framework for healthcare artificial intelligence. arXiv. https://arxiv.org/abs/2309.14617
  • European Commission. (2019, April 8). A definition of artificial intelligence: Main capabilities and scientific disciplines. Shaping Europe’s Digital Future. https://digital-strategy.ec.europa.eu/en/library/definition-artificial-intelligence-main-capabilities-and-scientific-disciplines
  • European Commission. (2024). AI Act. https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai
  • Farina, M., Zhdanov, P., Karimov, A., & Lavazza, A. (2022). AI and society: A virtue ethics approach. AI & SOCIETY, 1-14. https://doi.org/10.1007/s00146-022-01545-5
  • Feng, L. (2024). Investigating the effects of artificial intelligence-assisted language learning strategies on cognitive load and learning outcomes: A comparative study. Journal of Educational Computing Research, 62(8), 1741-1774. https://doi.org/10.1177/07356331241268349
  • Florea, M., & Esteves, B. (2023). Is automated consent in solid GDPR-compliant? An approach for obtaining valid consent with the solid protocol. Information, 14(12), 631. https://doi.org/10.1109/EuroSPW54576.2021.00038
  • Fox, A. (2022). Educational research and AIED: Identifying ethical challenges. In W. Holmes & K. Porayska-Pomsta (Eds.), The Ethics of Artificial Intelligence in Education (pp. 47-73). Routledge. https://doi.org/10.4324/9780429329067-4
  • Garg, M., & Goel, A. (2022). A systematic literature review on online assessment security: Current challenges and integrity strategies. Computers & Security, 113, 102544. https://doi.org/10.1016/j.cose.2021.102544
  • General Data Protection Regulation. (2016). Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32016R0679
  • George, B., & Wooden, O. (2023). Managing the strategic transformation of higher education through artificial intelligence. Administrative Sciences, 13(9), 196. https://doi.org/10.3390/admsci13090196
  • Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627-660. https://doi.org/10.5465/annals.2018.0057
  • Google. (n.d.). Responsibility: Our principles. https://ai.google/responsibility/principles/
  • Grace, K., Stewart, H., Sandkühler, J. F., Thomas, S., Weinstein-Raun, B., & Brauner, J. (2024). Thousands of AI authors on the future of AI. arXiv. https://arxiv.org/abs/2401.02843
  • Grubaugh, S., Levitt, G., & Deever, D. (2023). Harnessing AI to power constructivist learning: An evolution in educational methodologies. EIKI Journal of Effective Teaching Methods, 1(3), 81-83. https://doi.org/10.59652/jetm.v1i3.43
  • Gültekin Varkonyi, G. (2020). Yapay zekâ teknolojisinin kişisel verilerin korunması açısından risk değerlendirmesi. In Ş. Sarıoğlu & M. U. Demirezen (Eds.), Yapay Zeka ve Büyük Veri Teknolojiler Yaklaşım ve Uygulamalar (pp. 343-364). Nobel.
  • Guo, H., Yi, W., & Liu, K. (2024). Enhancing constructivist learning: The role of generative AI in personalised learning experiences. In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024) (Vol. 1, pp. 767-770). https://doi.org/10.5220/0012688700003690
  • Hagendorff, T. (2022). A virtue-based framework to support putting AI ethics into practice. Philosophy & Technology, 35(3), 55. https://doi.org/10.1007/s13347-022-00553-z
  • Herm, L. V. (2023). Impact of explainable ai on cognitive load: Insights from an empirical study. arXiv. https://arxiv.org/abs/2304.08861
  • Hof, B. (2021). The turtle and the mouse: how constructivist learning theory shaped artificial intelligence and educational technology in the 1960s. History of education, 50(1), 93-111. https://doi.org/10.1080/0046760X.2020.1826053
  • Hofmann, V., Kalluri, P. R., Jurafsky, D., & King, S. (2024). Dialect prejudice predicts AI decisions about people's character, employability, and criminality. arXiv. https://doi.org/10.48550/ arXiv.2403.00742
  • Holmes, W., & Porayska-Pomsta, K. (Eds.). (2023). The ethics of artificial intelligence in education: Practices, challenges, and debates. Taylor & Francis.
  • Holstein, K., & Doroudi, S. (2022). Equity and artificial intelligence in education. In W. Holmes & K. Porayska-Pomsta (Eds.), The Ethics of Artificial Intelligence in Education (pp. 151-173). Routledge. https://doi.org/10.4324/9780429329067-9
  • Huang, L. (2023). Ethics of artificial intelligence in education: Student privacy and data protection. Science Insights Education Frontiers, 16(2), 2577-2587. https://doi.org/10.15354/sief.23.re202
  • Hugenholtz, P. B., & Quintais, J. P. (2021). Copyright and artificial creation: does EU copyright law protect AI-assisted output? IIC-International Review of Intellectual Property and Competition Law, 52(9), 1190-1216. https://doi.org/10.1007/s40319-021-01115-0
  • Hunkenschroer, A. L., & Kriebitz, A. (2023). Is AI recruiting (un) ethical? A human rights perspective on the use of AI for hiring. AI and Ethics, 3(1), 199-213. https://doi.org/10.1007/s43681-022-00166-4
  • IBM. (2023). IBM artificial intelligence pillars. https://www.ibm.com/policy/ibm-artificial-intelligence-pillars/
  • IBM. (n.d.). IBM’s principles for trust and transparency. https://www.ibm.com/policy/wp-content/uploads/2018/06/IBM_Principles_SHORT.V4.3.pdf
  • Institute of Business Analytics. (2023). Basics of bias & fairness in AI systems. https://bias-and-fairness-in-ai-systems.de/en/basics/
  • Institute of Electrical and Electronics Engineers. (2019). Ethically aligned design. https://sagroups.ieee.org/global-initiative/wp-content/uploads/sites/542/2023/01/ead1e.pdf
  • Irfan, M., Aldulaylan, F., & Alqahtani, Y. (2023). Ethics and privacy in Irish higher education: A comprehensive study of artificial intelligence (AI) tools implementation at University of Limerick. Global Social Sciences Review (GSSR), 3(2), 201-210. https://doi.org/10.34961/researchrepository-ul.23181584.v1.
  • Ivanov, S. (2023). The dark side of artificial intelligence in higher education. The Service Industries Journal, 43(15-16), 1055-1082. https://doi.org/10.1080/02642069.2023.2258799
  • Jago, A. S., & Carroll, G. R. (2023). Who made this? Algorithms and authorship credit. Personality and Social Psychology Bulletin, 50(5), 793-806. https://doi.org/10.1177/01461672221149815
  • Jian-hua, H., Zhao, W., Jiang, Q., Oubibi, M., & Hu, X. (2019). Intelligent tutoring system trends 2006-2018: A literature review. In 2019 Eighth International Conference on Educational Innovation Through Technology (EITT). https://doi.org/10.1109/eitt.2019.00037
  • Jones, K. M., Asher, A., Goben, A., Perry, M. R., Salo, D., Briney, K. A., & Robertshaw, M. B. (2020). “We're being tracked at all times”: Student perspectives of their privacy in relation to learning analytics in higher education. Journal of the Association for Information Science and Technology, 71(9), 1044-1059. https://doi.org/10.1002/asi.24358
  • Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., & Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583-589. https://doi.org/10.1038/s41586-021-03819-2
  • Kant, I. (2020). Ahlak metafiziğinin temellendirilmesi (İ. Kuçuradi, Trans.). Türk Felsefe Kurumu. (Original work published 1786).
  • Khan, K., Aziz, M. U., Minhas, M., & Khan, A. I. (2025). The psychological effects of AI-based learning on student motivation, anxiety, and cognitive load. The Critical Review of Social Sciences Studies, 3(1), 3527-3540. https://doi.org/10.59075/1fvs0v71
  • Khlaif, Z. N., Alkouk, W. A., Salama, N., & Abu Eideh, B. (2025). Redesigning assessments for ai-enhanced learning: A framework for educators in the generative AI era. Education Sciences, 15(2), 174. https://doi.org/10.3390/educsci15020174
  • Kılıç, R. (2022). Ahlak felsefesi (etik). Nobel Akademik Yayıncılık.
  • Kizilcec, R. F., & Lee, H. (2022). Algorithmic fairness in education. In W. Holmes & K. Porayska-Pomsta (Eds.), The Ethics of Artificial Intelligence in Education (pp. 174-202). Routledge. https://doi.org/10.4324/9780429329067-10
  • Kosov, M. E., Malashenko, G. T., Frumina, S. V., Grishina, O. A., Polyakova, O. A., Alandarov, R. A., Ponkratov, V. V., Shmigol, N. S., Dzusova, S. S., & Abbood, A. A. A. (2023). Increasing the effectiveness of pedagogical technologies in education: Psychological experience of technological change management. Emerging Science Journal, 7, 49-63. https://doi.org/10.28991/esj-2023-sied2-05
  • Krafft, P. M., Young, M., Katell, M., Huang, K., & Bugingo, G. (2020). Defining AI in policy versus practice. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (pp. 72-78). https://doi.org/10.1145/3375627.3375835
  • Lin, Z. (2023). Responsible integration of ai in academic research: detection, attribution, and documentation. PsyArXiv Preprints. https://doi.org/10.31234/osf.io/w75gs
  • Madaio, M., Blodgett, S. L., Mayfield, E., & Dixon-Román, E. (2022). Beyond “fairness”: Structural (in) justice lenses on AI for education. In W. Holmes & K. Porayska-Pomsta (Eds.), The Ethics of Artificial Intelligence in Education (pp. 203-239). Routledge. https://doi.org/10.4324/9780429329067-11
  • Maslej, N., Fattorini, L., Perrault, R., Parli, V., Reuel, A., Brynjolfsson, E., Etchemendy, J., Ligett, K., Lyons, T., Manyika, J., Niebles, J. C., Shoham, Y., Wald, R., & Clark, J. (2024). The AI Index 2024 annual report. AI Index Steering Committee, Institute for Human-Centered AI, Stanford University. https://hai.stanford.edu/assets/files/hai_ai-index-report-2024-smaller2.pdf
  • McCarthy, J. (2007). From here to human-level AI. Artificial Intelligence, 171(18), 1174-1182. https://doi.org/10.1016/j.artint.2007.10.009
  • Microsoft. (2022). Microsoft responsible AI standard, v2 general requirements. https://query.prod.cms.rt.microsoft.com/cms/api/am/binary/RE5cmFl?culture=en-us&country=us
  • Mougan, C., & Brand, J. (2023). Kantian deontology meets AI alignment: Towards morally robust fairness metrics. arXiv. https://doi.org/10.48550/arXiv.2311.05227
  • National Institute of Standards and Technology. (2024). Artificial intelligence risk management framework: Generative artificial intelligence profile [NIST AI 600-1]. https://airc.nist.gov/docs/NIST.AI.600-1.GenAI-Profile.ipd.pdf
  • Neha, K., Kumar, R., & Sankat, M. (2024). AI wizards: Pioneering assistive technologies for higher education inclusion of students with learning disabilities. In R. Kaluri, M. Mahmud, T. R. Gadekallu, D. S. Rajput, & K. Lakshmanna (Eds.), Applied Assistive Technologies and Informatics for Students with Disabilities. Applied Intelligence and Informatics. Springer, Singapore. https://doi.org/10.1007/978-981-97-0914-4_4
  • Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B. T. (2022). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28(4), 4221-4241. https://doi.org/10.1007/s10639-022-11316-w
  • Novelli, C., Taddeo, M., & Floridi, L. (2024). Accountability in artificial intelligence: What it is and how it works. AI & SOCIETY, 39(4), 1871-1882. https://doi.org/10.1007/s00146-023-01635-y
  • OECD. (2019). Recommendation of the council on artificial intelligence. OECD/LEGAL/0449. https://oecd.ai/en/assets/files/OECD-LEGAL-0449-en.pdf
  • OECD. (2023). Transparency and explainability (Principle 1.3). OECD.AI. https://oecd.ai/en/dashboards/ai-principles/P7
  • OECD. (2024). Recommendation of the council on artificial intelligence. OECD/LEGAL/0449. https://legalinstruments.oecd.org/en/instruments/oecd-legal-0449
  • Omay, M. (2023). Çağdaş etik tartışmaları. Vakıfbank Kültür Yayınları.
  • OpenAI. (2018). OpenAI charter. https://openai.com/charter
  • Orrell, B., & Veldran, D. (2024). The age of uncertainty—and opportunity: Work in the age of AI. American Enterprise Institute Perspectives on Opportunity: Center of Opportunity and Social Mobility. https://www.aei.org/research-products/report/the-age-of-uncertainty-and-opportunity-work-in-the-age-of-ai/
  • Parker, S. K., & Grote, G. (2022). Automation, algorithms, and beyond: Why work design matters more than ever in a digital world. Applied Psychology, 71(4), 1171-1204. https://doi.org/10.1111/apps.12241
  • Patibandla, R. L., Rao, B. T., Rao, D. M., & Ramakrishna Murthy, M. (2024). Reshaping the Future of Learning Disabilities in Higher Education with AI. In Applied Assistive Technologies and Informatics for Students with Disabilities (pp. 17-33). Springer Nature Singapore. https://doi.org/10.1007/978-981-97-0914-4_2
  • Pi, Y., & Proctor, M. (2025, January). Toward empowering AI governance with redress mechanisms. In Cambridge Forum on AI: Law and Governance (Vol. 1, p. e24). Cambridge University Press. https://doi.org/10.1017/cfl.2025.9
  • Popkhadze, N. (2021). The good, the bad and the ugly: AI in the higher education. Hallinnon Tutkimus, 40(4), 254-263. https://doi.org/10.37450/ht.107896
  • Qian, Z. (2021, June). Applications, risks and countermeasures of artificial intelligence in education. In 2021 2nd International Conference on Artificial Intelligence and Education (ICAIE) (pp. 89-92). IEEE. https://doi.org/10.1109/ICAIE53562.2021.00026
  • Roshanaei, M. (2024). Towards best practices for mitigating artificial intelligence implicit bias in shaping diversity, inclusion and equity in higher education. Education and Information Technologies, 1-26. https://doi.org/10.1007/s10639-024-12605-2
  • Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning and Teaching, 6(1). https://doi.org/10.37074/jalt.2023.6.1.9
  • Russell, S. J. & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson Education Limited.
  • Ryan, M., Antoniou, J., Brooks, L., Jiya, T., Macnish, K., & Stahl, B. (2021). Research and practice of AI ethics: A case study approach juxtaposing academic discourse with organisational reality. Science and Engineering Ethics, 27, 1-29. https://doi.org/10.1007/s11948-021-00293-x
  • Sajja, R., Sermet, Y., Cikmaz, M., Cwiertny, D., & Demir, I. (2024). Artificial intelligence-enabled intelligent assistant for personalized and adaptive learning in higher education. Information, 15(10), 596. https://doi.org/10.3390/info15100596
  • Samala, A. D., Rawas, S., Wang, T., Reed, J. M., Kim, J., Howard, N. J., & Ertz, M. (2025). Unveiling the landscape of generative artificial intelligence in education: A comprehensive taxonomy of applications, challenges, and future prospects. Education and Information Technologies, 30(3), 3239-3278. https://doi.org/10.1007/s10639-024-12936-0
  • Scherer, M. U. (2016). Regulating artificial intelligence systems: Risks, challenges, competencies, and strategies. Harvard Journal of Law & Technology, 29(2), 354-400. https://doi.org/10.2139/ssrn.2609777
  • Schuett, J. (2021). Defining the scope of AI regulations. Law, Innovation and Technology, 15(1), 60-82. http://dx.doi.org/10.2139/ssrn.3453632
  • Schwartz, R., Vassilev, A., Greene, K., Perine, L., Burt, A., & Hall, P. (2022). Towards a Standard for Identifying and Managing Bias in Artificial Intelligence (NIST Special Publication 1270). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.SP.1270
  • Selvadurai, N., & Matulionyte, R. (2020). Reconsidering creativity: copyright protection for works generated using artificial intelligence. Journal of Intellectual Property Law & Practice, 15(7), 536-543. https://doi.org/10.1093/jiplp/jpaa062
  • Smuha, N. A. (2022). Pitfalls and pathways for trustworthy artificial intelligence in education. In W. Holmes & K. Porayska-Pomsta (Eds.), The Ethics of Artificial Intelligence in Education (pp. 113-145). Routledge. https://doi.org/10.4324/9780429329067-7
  • Soleimani, M., Intezari, A., & Pauleen, D. J. (2022). Mitigating cognitive biases in developing AI-assisted recruitment systems: A knowledge-sharing approach. International Journal of Knowledge Management, 18(1), 1-18. https://doi.org/10.4018/IJKM.290022
  • Stenseke, J. (2023). Artificial virtuous agents: From theory to machine implementation. AI & SOCIETY, 38(4), 1301-1320. https://doi.org/10.1007/s00146-021-01325-7
  • Stokel-Walker, C. (2023). ChatGPT listed as author on research papers: Many scientists disapprove. Nature, 613(7945), 620-621. https://doi.org/10.1038/d41586-023-00107-z
  • Sumakul, D. T. Y. G., Hamied, F. A., & Sukyadi, D. (2022). Students’ perceptions of the use of AI in a writing class. Proceedings of the 67th TEFLIN International Virtual Conference; The 9th ICOELT 2021 (TEFLIN ICOELT 2021). https://doi.org/10.2991/assehr.k.220201.009
  • Sweeney, L. (2003). That's AI: A history and critique of the field (CMU-CS-03-106). Carnegie Mellon University, School of Computer Science. https://kilthub.cmu.edu/ndownloader/files/12102473
  • Talamo, A., Marocco, S., & Tricol, C. (2021). “The flow in the funnel”: Modeling organizational and individual decision-making for designing financial AI-based systems. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.697101
  • The Institute for Ethical AI in Education. (2020). Interim report: towards a shared vision of ethical AI in education. https://www.buckingham.ac.uk/wp-content/uploads/2020/02/The-Institute-for-Ethical-AI-in-Educations-Interim-Report-Towards-a-Shared-Vision-of-Ethical-AI-in-Education.pdf
  • Treviranus, J. (2022). Learning to learn differently. In W. Holmes & K. Porayska-Pomsta (Eds.), The Ethics of Artificial Intelligence in Education (pp. 25-46). Routledge. https://doi.org/10.4324/9780429329067-3
  • United States Copyright Office. (n.d.). Copyright and artificial intelligence. https://www.copyright.gov/ai/
  • Ukeje, I. O., Elom, C. O., Ayanwale, M. A., Umoke, C. C., & Nwangbo, S. O. (2024). Exploring an innovative educational governance framework: Leveraging artificial intelligence in a stakeholder-driven 'Open Campus Model'in South East Nigerian Universities. International Journal of Learning, Teaching and Educational Research, 23(6), 416-440. https://doi.org/10.26803/ijlter.23.6.19
  • UNESCO. (2021). UNESCO recommendation on the ethics of artificial intelligence. https://www.unesco.org/en/articles/recommendation-ethics-artificial-intelligence
  • Vidal, Q., Vincent-Lancrin, S., & Yun, H. (2023). Emerging governance of generative AI in education. In OECD Digital Education Outlook 2023: Towards an Effective Digital Education Ecosystem. OECD Publishing. https://doi.org/10.1787/c74f03de-en
  • Williamson, B., Bayne, S., & Shay, S. (2020). The datafication of teaching in higher education: critical issues and perspectives. Teaching in Higher Education, 25(4), 351-365. https://doi.org/10.1080/13562517.2020.1748811
  • Yang, H. & Kyun, S. (2022). The current research trend of artificial intelligence in language learning: a systematic empirical literature review from an activity theory perspective. Australasian Journal of Educational Technology, 38(5), 180-210. https://doi.org/10.14742/ajet.7492
  • Yeo, M. A. (2023). Academic integrity in the age of artificial intelligence (AI) authoring apps. Tesol Journal, 14(3), e716. https://doi.org/10.1002/tesj.716
  • Zafar, N., & Afzal, S. (2025). AI-powered reading support for multilingual learners in higher education: A critical review. Journal for Social Science Archives, 3(1), 776-786. https://doi.org/10.59075/jssa.v3i1.158
  • Zirar, A., Ali, S. I., & Islam, N. (2023). Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda. Technovation, 124, 102747. https://doi.org/10.1016/j.technovation.2023.102747
There are 115 citations in total.

Details

Primary Language English
Subjects Higher Education Policies, Higher Education Management
Journal Section Review Article
Authors

Hatice Bilgin 0000-0003-0020-2051

Submission Date September 21, 2025
Acceptance Date December 7, 2025
Publication Date December 31, 2025
Published in Issue Year 2025 Volume: 6 Issue: 2

Cite

APA Bilgin, H. (2025). A Framework for Responsible AI in Higher Education. Higher Education Governance and Policy, 6(2), 155-174. https://doi.org/10.55993/hegp.1788343