Reconceptualising Learner Autonomy in Generative AI-Supported Higher Education: A Technology-Mediated Framework
Keywords:
Learner autonomy; generative AI; human judgement; self-regulated learning; higher educationAbstract
Generative artificial intelligence (generative AI) creates an autonomy paradox in higher education: It expands access to feedback, explanation and learning support while also enabling learners to outsource parts of planning, reasoning, production, verification and evaluation. Recently, studies have started to examine learner autonomy in environments supported by generative AI, but they remain conceptually fragmented regarding how learner responsibility should be organised across AI-mediated academic work. This conceptual study addresses this problem with a conceptual analysis and a bounded integrative literature review drawing on 46 sources, of which 43 formed the substantive analytical corpus that spans foundational autonomy theory, self-regulated learning, metacognition, learner agency, AI literacy, feedback, assessment, academic integrity and generative AI. The synthesis identifies three mechanisms of reconfiguration—cognitive redistribution, authorship ambiguity and regulatory intensification – and proposes technology-mediated learner autonomy as a learner-level responsibility structure comprising five operational dimensions: goal-setting autonomy, prompting agency, critical verification, metacognitive regulation and ethical accountability. Human judgement preservation functions as the normative core that links these dimensions and provides a criterion for distinguishing between accountable learner-directed use and uncritical or dependent AI use. The framework offers a theoretically traceable basis for future empirical validation and for designing assessment, feedback and institutional guidance that makes learner responsibility visible.
https://doi.org/10.26803/ijlter.25.9.21
References
Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52(1), 1–26. https://doi.org/10.1146/annurev.psych.52.1.1
Bearman, M., & Ajjawi, R. (2023). Learning to work with the black box: Pedagogy for a world with artificial intelligence. British Journal of Educational Technology, 54(5), 1160–1173. https://doi.org/10.1111/bjet.13337
Benson, P. (2011). Teaching and researching: Autonomy in language learning (2nd ed.). Routledge. https://doi.org/10.4324/9781315833767
Boud, D., & Molloy, E. (2013). Rethinking models of feedback for learning: The challenge of design. Assessment & Evaluation in Higher Education, 38(6), 698–712. https://doi.org/10.1080/02602938.2012.691462
Carless, D., & Boud, D. (2018). The development of student feedback literacy: Enabling uptake of feedback. Assessment & Evaluation in Higher Education, 43(8), 1315–1325. https://doi.org/10.1080/02602938.2018.1463354
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), Article 38. https://doi.org/10.1186/s41239-023-00408-3
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(1), Article 43. https://doi.org/10.1186/s41239-023-00411-8
Cong-Lem, N., Nguyen, T. T., Nguyen, K. N. H., & Nguyen, Q. N. H. (2026). Critical thinking in Vietnamese EFL students’ use of generative AI tools for academic learning: Qualitative insights from a brief intervention. English Teaching & Learning, 50(2), 295–315. https://doi.org/10.1007/s42321-025-00217-z
Cotton, D. R. E., 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
Deci, E. L., & Ryan, R. M. (2000). The ‘what’ and ‘why’ of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01
Essel, H. B., Vlachopoulos, D., Johnson, E. E., & Quansah, E. K. (2026). Undergraduate students’ perceptions of generative artificial intelligence as a predictor of learning autonomy in Ghana. Discover Artificial Intelligence, 6(1), Article 85. https://doi.org/10.1007/s44163-025-00725-8
Farrelly, T., & Baker, N. (2023). Generative artificial intelligence: Implications and considerations for higher education practice. Education Sciences, 13(11), Article 1109. https://doi.org/10.3390/educsci13111109
Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, 34(10), 906–911. https://doi.org/10.1037/0003-066X.34.10.906
Gourlay, L. (2021). There is no ‘virtual learning’: The materiality of digital education. Journal of New Approaches in Educational Research, 10(1), 57–66. https://doi.org/10.7821/naer.2021.1.649
Guadu, A., Dibekulu, D., & Menberu, A. W. (2025). Academic integrity in the age of AI: Challenges and strategies for African higher education. Discover Education, 5, Article 21. https://doi.org/10.1007/s44217-025-01031-2
Hamamra, B. N., Khlaif, Z. N., & Mahamid, N. (2026). AI literacy and mediated learner autonomy in ChatGPT-supported EFL writing: A qualitative study at a Palestinian university. International Journal of Educational Technology in Higher Education, 23, Article 19. https://doi.org/10.1186/s41239-026-00597-7
Holec, H. (1981). Autonomy and foreign language learning. Pergamon.
Holmes, W., & Miao, F. (2023). Guidance for generative AI in education and research. Unesco Publishing.
Hu, Z. (2026). AI–human intelligence synergy and student voice in health sciences education: A conceptual framework. International Journal of Learning, Teaching and Educational Research, 25(4), 557–574. https://doi.org/10.26803/ijlter.25.4.26
Jaakkola, E. (2020). Designing conceptual articles: Four approaches. AMS Review, 10(1–2), 18–26. https://doi.org/10.1007/s13162-020-00161-0
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274
Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for language teaching and learning. RELC Journal, 54(2), 537–550. https://doi.org/10.1177/00336882231162868
Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21(2), Article 100790. https://doi.org/10.1016/j.ijme.2023.100790
Little, D. (1991). Learner autonomy 1: Definitions, issues and problems. Authentik Language Learning Resources.
Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), Article 410. https://doi.org/10.3390/educsci13040410
Mhindu, A., Ramoroka, B. T., Ntereke, B., & Conteh, B. B. (2026). Adoption of generative artificial intelligence in L2 graduate academic writing in higher education: A scoping review of current status and implications. International Journal of Learning, Teaching and Educational Research, 25(3), 968–987. https://doi.org/10.26803/ijlter.25.3.43
Moorhouse, B. L., Yeo, M. A., & Wan, Y. (2023). Generative AI tools and assessment: Guidelines of the world’s top-ranking universities. Computers and Education Open, 5, Article 100151. https://doi.org/10.1016/j.caeo.2023.100151
Muchuweni, T., & Jojo, Z. (2026). Advancing mathematics teaching with AI: A conceptual framework for instructional planning and delivery. International Journal of Learning, Teaching and Educational Research, 25(4), 251–270. https://doi.org/10.26803/ijlter.25.4.12
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, 20(2), Article 7. https://doi.org/10.53761/1.20.02.07
Perkins, M., Furze, L., Roe, J., & MacVaugh, J. (2024). The artificial intelligence assessment scale (AIAS): A framework for ethical integration of generative AI in educational assessment. Journal of University Teaching and Learning Practice, 21(6). https://doi.org/10.53761/q3azde36
Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 451–502). Academic Press. https://doi.org/10.1016/B978-012109890-2/50043-3
Psaros, H. (2022). Learning, digital technologies, and sociomaterial approaches: A critical reflection from the perspective of materialist dialectics. Theory & Psychology, 32(6), 827–847. https://doi.org/10.1177/09593543221129235
Qian, Y. (2025). Pedagogical applications of generative AI in higher education: A systematic review of the field. TechTrends, 69(5), 1105–1120. https://doi.org/10.1007/s11528-025-01100-1
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), 342–363. https://doi.org/10.37074/jalt.2023.6.1.9
Singh, R. K. V., Hashim, H., & Jamaludin, K. A. (2026). Reconceptualising fair assessment in English-medium instruction: Content–language separation as a validity principle in higher education. International Journal of Learning, Teaching and Educational Research, 25(4), 916–935. https://doi.org/10.26803/ijlter.25.4.41
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039
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, 10(1), Article 15. https://doi.org/10.1186/s40561-023-00237-x
Torraco, R. J. (2016). Writing integrative literature reviews: Using the past and present to explore the future. Human Resource Development Review, 15(4), 404–428. https://doi.org/10.1177/1534484316671606
Wang, C., Lim, S. P., Zhang, Y., Abdul Latif, I. N., Zhou, B., & Li, M. (2026). Integrating generative AI in language education: A systematic review of pedagogical, ethical, and technological themes. International Journal of Learning, Teaching and Educational Research, 25(2), 577–595. https://doi.org/10.26803/ijlter.25.2.26
Wang, F., Li, N., Cheung, A. C. K., & Wong, G. K. W. (2025). In GenAI we trust: An investigation of university students’ reliance on and resistance to generative AI in language learning. International Journal of Educational Technology in Higher Education, 22(1), Article 59. https://doi.org/10.1186/s41239-025-00547-9
Xu, X., Qiao, L., Cheng, N., Liu, H., & Zhao, W. (2025). Enhancing self-regulated learning and learning experience in generative AI environments: The critical role of metacognitive support. British Journal of Educational Technology, 56(5), 1842–1863. https://doi.org/10.1111/bjet.13599
Yan, L., Sha, L., Zhao, L., Li, Y., Martinez-Maldonado, R., Chen, G., Li, X., Jin, Y., & Gaševi?, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1), 90–112. https://doi.org/10.1111/bjet.13370
Yang, Y., Luo, J., Yang, M., Yang, R., & Chen, J. (2024). From surface to deep learning approaches with generative AI in higher education: An analytical framework of student agency. Studies in Higher Education, 49(5), 817–830. https://doi.org/10.1080/03075079.2024.2327003
Yu, H. (2023). Reflection on whether ChatGPT should be banned by academia from the perspective of education and teaching. Frontiers in Psychology, 14, Article 1181712. https://doi.org/10.3389/fpsyg.2023.1181712
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, Article 39. https://doi.org/10.1186/s41239-019-0171-0
Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). Academic Press. https://doi.org/10.1016/B978-012109890-2/50031-7
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