Governance-Aware Generative AI Literacy for Adult Higher Education

Abstract

Generative artificial intelligence (GenAI) is now embedded in the everyday work of university students, yet many higher education responses remain divided between permissive innovation, prohibition, and academic-integrity compliance. This conceptual paper develops a governance-aware model of GenAI literacy for adult higher education. It asks how universities can teach adult learners to collaborate with AI systems while preserving human agency, epistemic responsibility, disciplinary judgement, and ethical accountability. Drawing on AI competency frameworks, adult learning theory, critical thinking scholarship, and emerging work on human-AI governance, the paper proposes a five-domain framework: epistemic verification, prompt negotiation, dialogic reflection, ethical governance, and transfer to professional action. The framework is translated into an eight-week instructional architecture, assessment matrix, and institutional implementation principles. The paper argues that GenAI literacy should not be reduced to prompt engineering or tool familiarity. Instead, it should be taught as a disciplined form of reflective practice in which learners disclose, test, revise, and justify AI-supported reasoning. The contribution of the paper is a practical and theoretically grounded model that can guide curriculum design, staff development, assessment redesign, and institutional AI policy in adult and professional education contexts.

Keywords

• generative artificial intelligence
• AI literacy
• adult higher education
• critical thinking
• governance-aware pedagogy

Yetişkin Yükseköğretimi İçin Yönetişim Bilinçli Üretken Yapay Zekâ Okuryazarlığı

Abstract

Üretken yapay zekâ (GenAI), üniversite öğrencilerinin gündelik çalışmalarına artık yerleşmiş durumdadır; ancak yükseköğretimin buna verdiği birçok yanıt hâlâ serbest yenilikçilik, yasaklama ve akademik dürüstlük uyumu arasında bölünmüş görünmektedir. Bu kavramsal makale, yetişkin yükseköğretimi için yönetişim bilinçli bir GenAI okuryazarlığı modeli geliştirmektedir. Makale, üniversitelerin yetişkin öğrenenlere, insan özerkliğini, epistemik sorumluluğu, disipliner yargıyı ve etik hesap verebilirliği koruyarak yapay zekâ sistemleriyle nasıl iş birliği yapmayı öğretebileceğini sorgulamaktadır. Yapay zekâ yeterlik çerçeveleri, yetişkin öğrenme kuramı, eleştirel düşünme literatürü ve insan-yapay zekâ yönetişimi üzerine gelişmekte olan çalışmalardan hareketle makale beş alanlı bir çerçeve önermektedir: epistemik doğrulama, istem müzakeresi, diyalojik yansıtma, etik yönetişim ve mesleki eyleme aktarım. Bu çerçeve sekiz haftalık bir öğretim mimarisine, değerlendirme matrisine ve kurumsal uygulama ilkelerine dönüştürülmektedir. Makale, GenAI okuryazarlığının istem mühendisliğine ya da araç aşinalığına indirgenmemesi gerektiğini savunmaktadır. Bunun yerine, öğrenenlerin yapay zekâ destekli akıl yürütmeyi açıkladığı, test ettiği, gözden geçirdiği ve gerekçelendirdiği disiplinli bir yansıtıcı uygulama biçimi olarak öğretilmelidir. Makalenin katkısı, yetişkin ve mesleki eğitim bağlamlarında müfredat tasarımına, akademik personel gelişimine, değerlendirme tasarımının yenilenmesine ve kurumsal yapay zekâ politikalarına rehberlik edebilecek pratik ve kuramsal olarak temellendirilmiş bir model sunmasıdır.

Keywords

• üretken yapay zekâ
• yapay zekâ okuryazarlığı
• yetişkin yükseköğretimi
• eleştirel düşünme
• yönetişim bilinçli pedagoji

References

1. Annapureddy, R., Fornaroli, A., & Gatica-Perez, D. (2024). Generative AI literacy: Twelve defining competencies. arXiv. Retrieved from https://arxiv.org/abs/2412.12107
2. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610-623). doi:10.1145/3442188.3445922
3. Brookfield, S. D. (2012). Teaching for critical thinking: Tools and techniques to help students question their assumptions. San Francisco, CA: Jossey-Bass.
4. 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, 38. doi:10.1186/s41239-023-00408-3
5. Chan, C. K. Y., & Hu, W. (2023). Students' voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20, 43. doi:10.1186/s41239-023-00411-8
6. Chen, K., Tallant, A. C., & Selig, I. (2025). Exploring generative AI literacy in higher education: Student adoption, interaction, evaluation and ethical perceptions. Information and Learning Sciences, 126(1/2), 132-148. doi:10.1108/ILS-10-2023-0160
7. Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20, 22. doi:10.1186/s41239-023-00392-8
8. European Commission. (2022). Ethical guidelines on the use of artificial intelligence (AI) and data in teaching and learning for educators. Luxembourg: Publications Office of the European Union. doi:10.2766/153756

9. Facione, P. A. (1990). Critical thinking: A statement of expert consensus for purposes of educational assessment and instruction. Millbrae, CA: California Academic Press.
10. Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X., & Gašević, D. (2025). Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance. British Journal of Educational Technology, 56(2), 489-530. doi:10.1111/bjet.13544
11. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Boston, MA: Center for Curriculum Redesign.
12. Johnston, H., Wells, R. F., Shanks, E. M., Boey, T., & Parsons, B. N. (2024). Student perspectives on the use of generative artificial intelligence technologies in higher education. International Journal for Educational Integrity, 20, 2. doi:10.1007/s40979-024-00149-4
13. Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. doi:10.1016/j.lindif.2023.102274
14. Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Englewood Cliffs, NJ: Prentice Hall.
15. Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022). Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence, 3, 100101. doi:10.1016/j.caeai.2022.100101
16. Lodge, J. M., Howard, S., Bearman, M., Dawson, P., & Associates. (2023). Assessment reform for the age of artificial intelligence. Melbourne, Australia: Tertiary Education Quality and Standards Agency.
17. Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-16). doi:10.1145/3313831.3376727
18. McDonald, N., Johri, A., Ali, A., & Collier, A. H. (2025). Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines. Computers in Human Behavior: Artificial Humans, 3, 100121. doi:10.1016/j.chbah.2025.100121
19. Mezirow, J. (1997). Transformative learning: Theory to practice. New Directions for Adult and Continuing Education, 1997(74), 5-12. doi:10.1002/ace.7401
20. Miao, F., & Cukurova, M. (2024). AI competency framework for teachers. Paris, France: UNESCO.
21. Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. Paris, France: UNESCO.
22. Miao, F., Shiohira, K., & Lao, N. (2024). AI competency framework for students. Paris, France: UNESCO.
23. Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. doi:10.1016/j.caeai.2021.100041
24. OECD. (2025). What should teachers teach and students learn in a future of powerful AI? Paris, France: OECD Publishing.
25. Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Cambridge, England: Polity Press.
26. Tan, K. H. (2026). Improving critical thinking skills among adult learners through structured reflective dialogue: An action research study in a university social sciences course in Singapore. International Journal of Research Publication and Reviews, 7(6), 5322-5329. doi:10.55248/gengpi.07.0626.17a03
27. UNESCO. (2021). AI and education: Guidance for policy-makers. Paris, France: UNESCO.
28. Zawacki-Richter, O., Marín, V. I., 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, 16, 39. doi:10.1186/s41239-019-0171-0