Keywords
higher-order thinking;
human–AI collaboration;
scaffolding;
self-regulated learning;
transfer of learning
Abstract
In an AI-supported task, a student may explain a weak comparison once it has been identified, yet fail to notice a similar problem without a reminder. A decision about further help therefore needs evidence about what the student did without being prompted. We examine such decisions in English-medium university argumentation and evidence evaluation. The framework brings together English-language work on scaffolding, learner-generated reasoning, metacognitive calibration, and cognitive offloading. We distinguish help that supplies a reason from help that initiates a check. Readiness to work without the first does not establish readiness to work without the second. Coding examples and boundary cases make evidence about these functions interpretable, while six conditional propositions connect reconstruction and regulation to adaptation and transfer. A proposed experiment gives every group comparable diagnostic practice but varies whether teachers use its results to adjust assistance. This comparison isolates the value of using the evidence from learning through the diagnostic task itself. We relate the account to four GenAI approaches and illustrate it with a hypothetical sociology lesson. Help may be retained or restored when a new task requires it. The framework and coding rules need validation, particularly beyond the language and disciplinary settings considered here.
References
Barnett, S. M., & Ceci, S. J. (2002). When and where do we apply what we learn? A taxonomy for far transfer. Psychological Bulletin, 128(4), 612–637. https://doi.org/10.1037/0033-2909.128.4.612
Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö., & Mariman, R. (2025). Generative AI without guardrails can harm learning: Evidence from high school mathematics. Proceedings of the National Academy of Sciences, 122(26), Article e2422633122. https://doi.org/10.1073/pnas.2422633122
Bearman, M., Tai, J., Dawson, P., Boud, D., & Ajjawi, R. (2024). Developing evaluative judgement for a time of generative artificial intelligence. Assessment & Evaluation in Higher Education, 49(6), 893–905. https://doi.org/10.1080/02602938.2024.2335321
Cao, M., & Carvalho, P. F. (2026). Striking the balance: How variability shapes retrieval practice and worked examples for transfer learning. Educational Psychology Review, 38, Article 71. https://doi.org/10.1007/s10648-026-10169-w
Chi, M. T. H., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49(4), 219–243. https://doi.org/10.1080/00461520.2014.965823
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. https://doi.org/10.1111/bjet.13544
Fiorella, L. (2023). Making sense of generative learning. Educational Psychology Review, 35, Article 50. https://doi.org/10.1007/s10648-023-09769-7
Gonçalves, A. O., Muniz, B. F. B., & Jaeger, A. (2025). Retrieval practice versus elaborative encoding: A systematic and meta-analytic review. Educational Psychology Review, 37, Article 100. https://doi.org/10.1007/s10648-025-10076-6
Guan, R., Raković, M., Chen, G., & Gašević, D. (2025). How educational chatbots support self-regulated learning? A systematic review of the literature. Education and Information Technologies, 30(4), 4493–4518. https://doi.org/10.1007/s10639-024-12881-y
Jaakkola, E. (2020). Designing conceptual articles: Four approaches. AMS Review, 10(1–2), 18–26. https://doi.org/10.1007/s13162-020-00161-0
Janssen, N., & Lazonder, A. W. (2024). Meta-analysis of interventions for monitoring accuracy in problem solving. Educational Psychology Review, 36, Article 96. https://doi.org/10.1007/s10648-024-09936-4
Jin, S.-H., Im, K., Yoo, M., Roll, I., & Seo, K. (2023). Supporting students’ self-regulated learning in online learning using artificial intelligence applications. International Journal of Educational Technology in Higher Education, 20, Article 37. https://doi.org/10.1186/s41239-023-00406-5
Kane, M. T. (2013). Validating the interpretations and uses of test scores. Journal of Educational Measurement, 50(1), 1–73. https://doi.org/10.1111/jedm.12000
Kestin, G., Miller, K., Klales, A., Milbourne, T., & Ponti, G. (2025). AI tutoring outperforms in-class active learning: An RCT introducing a novel research-based design in an authentic educational setting. Scientific Reports, 15, Article 17458. https://doi.org/10.1038/s41598-025-97652-6
Koedinger, K. R., & Aleven, V. (2007). Exploring the assistance dilemma in experiments with Cognitive Tutors. Educational Psychology Review, 19(3), 239–264. https://doi.org/10.1007/s10648-007-9049-0
Lakens, D., Scheel, A. M., & Isager, P. M. (2018). Equivalence testing for psychological research: A tutorial. Advances in Methods and Practices in Psychological Science, 1(2), 259–269. https://doi.org/10.1177/2515245918770963
Molenaar, I. (2022). Towards hybrid human–AI learning technologies. European Journal of Education, 57(4), 632–645. https://doi.org/10.1111/ejed.12527
Ngai, C., & Gilbert, S. J. (2026). Metacognitive training facilitates optimal cognitive offloading. Cognitive Research: Principles and Implications, 11, Article 21. https://doi.org/10.1186/s41235-026-00714-0
Perkins, M., Roe, J., & Furze, L. (2025). Reimagining the Artificial Intelligence Assessment Scale (AIAS): A refined framework for educational assessment. Journal of University Teaching and Learning Practice, 22(7). https://doi.org/10.53761/rrm4y757
Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688. https://doi.org/10.1016/j.tics.2016.07.002
Roe, J., Perkins, M., & Giray, L. (2026). Assessment twins: An approach for strengthening assessment validity in the age of generative AI. Journal of Applied Learning & Teaching, 9(2). https://doi.org/10.37074/jalt.2026.9.2.3
Sinha, T., & Kapur, M. (2021). When problem solving followed by instruction works: Evidence for productive failure. Review of Educational Research, 91(5), 761–798. https://doi.org/10.3102/00346543211019105
Soderstrom, N. C., & Bjork, R. A. (2015). Learning versus performance: An integrative review. Perspectives on Psychological Science, 10(2), 176–199. https://doi.org/10.1177/1745691615569000
Steiss, J., Tate, T., Graham, S., Cruz, J., Hebert, M., Wang, J., Moon, Y., Tseng, W., Warschauer, M., & Olson, C. B. (2024). Comparing the quality of human and ChatGPT feedback of students’ writing. Learning and Instruction, 91, Article 101894. https://doi.org/10.1016/j.learninstruc.2024.101894
Tankelevitch, L., Kewenig, V., Simkute, A., Scott, A. E., Sarkar, A., Sellen, A., & Rintel, S. (2024). The metacognitive demands and opportunities of generative AI. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (Article 680, pp. 1–24). Association for Computing Machinery. https://doi.org/10.1145/3613904.3642902
Tetzlaff, L., Simonsmeier, B., Peters, T., & Brod, G. (2025). A cornerstone of adaptivity – A meta-analysis of the expertise reversal effect. Learning and Instruction, 98, Article 102142. https://doi.org/10.1016/j.learninstruc.2025.102142
Thurn, C. M., Edelsbrunner, P. A., Berkowitz, M., Deiglmayr, A., & Schalk, L. (2023). Questioning central assumptions of the ICAP framework. npj Science of Learning, 8, Article 49. https://doi.org/10.1038/s41539-023-00197-4
van de Pol, J., Volman, M., & Beishuizen, J. (2010). Scaffolding in teacher–student interaction: A decade of research. Educational Psychology Review, 22(3), 271–296. https://doi.org/10.1007/s10648-010-9127-6
Vendrell, M., & Johnston, S.-K. (2026). Scaffolding critical thinking with generative AI: Design principles for integrating large language models in higher education. Computers and Education: Artificial Intelligence, 10, Article 100572. https://doi.org/10.1016/j.caeai.2026.100572
Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry, 17(2), 89–100. https://doi.org/10.1111/j.1469-7610.1976.tb00381.x
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., & Gašević, 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
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2026 Chen Wang