Abstract
We examined evaluative AI literacy, learner control, and cognitive offloading in higher education by comparing 13 full-text reports in this source-seeded evidence map. These reports were drawn from a 54-record working bibliography; four abstract/preview records were used solely as background. No core report empirically tests the full hypothesized chain: evaluative AI literacy → learner control → cognitive offloading. The course-relative offloading distinctions accompany two hypothetical assignments in which work assisted by generative artificial intelligence (GenAI), reasons for decisions, and learning without GenAI would be assessed separately. These proposals remain unvalidated. English-only selection, incomplete search records, and no formal risk-of-bias appraisal prevent comprehensive coverage and field-wide effect estimates.
References
Abbas, M., Jam, F. A., & Khan, T. I. (2024). Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students. International Journal of Educational Technology in Higher Education, 21, Article 10. https://doi.org/10.1186/s41239-024-00444-7
Anders, A. D., & Dux Speltz, E. (2025). Developing generative AI literacies through self-regulated learning: A human-centered approach. Computers and Education: Artificial Intelligence, 9, Article 100482. https://doi.org/10.1016/j.caeai.2025.100482
Biggs, J. (1996). Enhancing teaching through constructive alignment. Higher Education, 32(3), 347–364. https://doi.org/10.1007/BF00138871
Carolus, A., Koch, M. J., Straka, S., Latoschik, M. E., & Wienrich, C. (2023). MAILS—Meta AI literacy scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change- and meta-competencies. Computers in Human Behavior: Artificial Humans, 1(2), Article 100014. https://doi.org/10.1016/j.chbah.2023.100014
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
Edwards, J., Nguyen, A., Lämsä, J., Sobocinski, M., Whitehead, R., Dang, B., Roberts, A.-S., & Järvelä, S. (2025). Human-AI collaboration: Designing artificial agents to facilitate socially shared regulation among learners. British Journal of Educational Technology, 56(2), 712–733. https://doi.org/10.1111/bjet.13534
Efklides, A. (2006). Metacognition and affect: What can metacognitive experiences tell us about the learning process? Educational Research Review, 1(1), 3–14. https://doi.org/10.1016/j.edurev.2005.11.001
Elshall, A. S., & Badir, A. (2025). Balancing AI-assisted learning and traditional assessment: The FACT assessment in environmental data science education. Frontiers in Education, 10, Article 1596462. https://doi.org/10.3389/feduc.2025.1596462
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
Fitriani, A. N., Risaldy, R., Rauf, A., & Afidatunisa, S. (2026). Academic dependency, AI literacy, and cognitive offloading predict students’ cognitive ability in generative AI learning. Artificial Intelligence in Lifelong and Life-Course Education, 1(2), 53–66. https://doi.org/10.66053/aillce.v1i2.18
Fyfe, P. (2023). How to cheat on your final paper: Assigning AI for student writing. AI & SOCIETY, 38, 1395–1405. https://doi.org/10.1007/s00146-022-01397-z
Grant, M. J., & Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information & Libraries Journal, 26(2), 91–108. https://doi.org/10.1111/j.1471-1842.2009.00848.x
Jiang, Y., & Chen, H. (2026). AI-based cognitive offloading by students in higher education in China. Computers in Human Behavior: Artificial Humans, 8, Article 100327. https://doi.org/10.1016/j.chbah.2026.100327
Jin, Y., Martinez-Maldonado, R., Gašević, D., & Yan, L. (2025). GLAT: The generative AI literacy assessment test. Computers and Education: Artificial Intelligence, 9, Article 100436. https://doi.org/10.1016/j.caeai.2025.100436
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
Lan, M., & Zhou, X. (2025). A qualitative systematic review on AI empowered self-regulated learning in higher education. npj Science of Learning, 10, Article 21. https://doi.org/10.1038/s41539-025-00319-0
Lee, Y. E., & Kan, J. S. (2026). AI literacy and self-perceived cognitive learning outcomes among university students in AI-integrated courses: Associations with instructor feedback and AI use indicators. Education Sciences, 16(9), Article 1384. https://doi.org/10.3390/educsci16091384
Liu, X., Xiao, Y., & Li, D. (2025). Assessing strategic use of artificial intelligence in self-regulated learning: Instrument development and evidence from Chinese university students. International Journal of Educational Technology in Higher Education, 22, Article 69.
https://doi.org/10.1186/s41239-025-00567-5
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). Association for Computing Machinery. https://doi.org/10.1145/3313831.3376727
Misiejuk, K., López-Pernas, S., Kaliisa, R., & Saqr, M. (2026). Cognitive offloading in student–AI collaboration: A longitudinal analysis of prompting strategies. Computers in Human Behavior Reports, 22, Article 101130. https://doi.org/10.1016/j.chbr.2026.101130
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., . . . Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, Article n71. https://doi.org/10.1136/bmj.n71
Peters, M. D. J., Marnie, C., Tricco, A. C., Pollock, D., Munn, Z., Alexander, L., McInerney, P., Godfrey, C. M., & Khalil, H. (2020). Updated methodological guidance for the conduct of scoping reviews. JBI Evidence Synthesis, 18(10), 2119–2126. https://doi.org/10.11124/JBIES-20-00167
Qin, F., Hao, Z., Yu, J., Liu, Z., & Zhang, Y. (2025). AI instructional agent improves student’s perceived learner control and learning outcome: Empirical evidence from a randomized controlled trial [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2505.22526
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
Scheiter, K., & Gerjets, P. (2007). Learner control in hypermedia environments. Educational Psychology Review, 19(3), 285–307. https://doi.org/10.1007/s10648-007-9046-3
Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., . . . Straus, S. E. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7), 467–473. https://doi.org/10.7326/M18-0850
Tzirides, A. O., Zapata, G., Kastania, N. P., Saini, A. K., Castro, V., Ismael, S. A., You, Y., Santos, T. A. D., Searsmith, D., O’Brien, C., Cope, B., & Kalantzis, M. (2024). Combining human and artificial intelligence for enhanced AI literacy in higher education. Computers and Education Open, 6, Article 100184. https://doi.org/10.1016/j.caeo.2024.100184
Urban, M., Děchtěrenko, F., Lukavský, J., Hrabalová, V., Svacha, F., Brom, C., & Urban, K. (2024). ChatGPT improves creative problem-solving performance in university students: An experimental study. Computers & Education, 215, Article 105031. https://doi.org/10.1016/j.compedu.2024.105031
Wang, B., Rau, P.-L. P., & Yuan, T. (2023). Measuring user competence in using artificial intelligence: Validity and reliability of artificial intelligence literacy scale. Behaviour & Information Technology, 42(9), 1324–1337. https://doi.org/10.1080/0144929X.2022.2072768
Wang, W.-S., Lin, C.-J., Lee, H.-Y., Huang, Y.-M., & Wu, T.-T. (2025). Enhancing self-regulated learning and higher-order thinking skills in virtual reality: The impact of ChatGPT-integrated feedback aids. Education and Information Technologies, 30, 19419–19445. https://doi.org/10.1007/s10639-025-13557-x
Xia, Q., Liu, Q., Tlili, A., & Chiu, T. K. F. (2026). A systematic literature review on designing self-regulated learning using generative artificial intelligence and its future research directions. Computers & Education, 240, Article 105465. https://doi.org/10.1016/j.compedu.2025.105465
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
Zhang, L., & Chen, S. C. (2026). Towards human-AI collaborative learning: Synergizing self-regulation and artificial-intelligence literacy. Education and Information Technologies, 31, 1879–1907. https://doi.org/10.1007/s10639-025-13880-3
Zhang, Y., Meng, X., Feng, S., & Lin, J. (2027). Human–AI collaboration reconfigures group regulation from socially shared to hybrid co-regulation. In E. G. Blanchard, G. Chen, M. Chi, & S. Isotani (Eds.), Artificial intelligence in education (Lecture Notes in Computer Science, Vol. 16585, pp. 318–326). Springer. https://doi.org/10.1007/978-3-032-29770-9_35
First published online 27 June 2026.
Zhu, Q., Li, X., Dong, Y., Chang, P., & Fan, M. (2026). Not all cognitive offloading is equal: Distinguishing dependent and autonomous offloading to generative AI. Frontiers in Psychology, 17, Article 1878629. https://doi.org/10.3389/fpsyg.2026.1878629
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2

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