Impact of Generative AI on Self-Regulated Learning and Cognitive Offloading
Open accessAbstract
Generative artificial intelligence (AI) is rapidly reshaping how senior high school students study, yet its implications for self-regulated learning and learning durability remain poorly resolved in authentic classrooms. This study examined whether a structured generative AI approach strengthens self-regulated learning while increasing cognitive offloading, and whether offloading predicts weaker delayed retention. Using a cluster randomised pretest and posttest design, intact class sections from one public and one private senior high school in Davao City, Philippines were assigned either to structured generative AI use guided by plan, monitor, and evaluate prompts or to non-AI study support. Self-regulated learning was measured through adapted MSLQ strategy scales, cognitive offloading through a brief offloading tendency scale, and achievement through unit quizzes including a two-week delayed retention assessment. Mixed-effects models accounted for section-level clustering and adjusted for baseline scores and access indicators. Structured generative AI use was associated with higher posttest self-regulated learning and substantially higher cognitive offloading. Offloading was negatively associated with delayed retention after adjustment, while the direct condition effect on delayed retention was not reliable. These findings underscore the need to integrate generative AI as a scaffold for verification and metacognitive control rather than as a substitute for reasoning.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Preliminary use of generative AI modifies study workload by dividing mental tasks rather than changing or affecting core cognitive processing.
“Preliminary use of generative AI modify study workload by dividing mental tasks rather than changing or affecting core cognitive processing ( Lindo & Cutad, 2024)”
From The Double-Edged Sword of Automation: Impact of Generative AI Adoption on Student's Critical Thinking Skills in Higher Education Considering Cognitive Offloading as a Mediator (2026) · cites this paper as Lindo & Cutad, 2024
The Introduction (page not provided) says generative AI shifts not only what students remember but what they practise. The Discussion (page not provided) says students may adopt AI-generated reasoning with minimal internal construction. Although the paper describes a redistribution of cognitive work, it contradicts the claim that core cognitive processing is unaffected. (LLM, conf=0.89)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The publisher hasn’t deposited this paper’s reference list.
Research works printed but not linked: 2
- Winne, P. H.; Hadwin, A. F. Studying as self regulated learning. In Metacognition in Educational Theory and Practice; Hacker, D. J., Dunlosky, J., Graesser, A. C., Eds.; Lawrence Erlbaum Associates: Mahwah, NJ, USA, 1998; pp. 277-304.
- Gernsbacher, M. A., Pew, R. W., Hough, L. M., Pomerantz, J. R., Eds.; Worth Publishers: New York, NY, USA, 2011; pp. 56-64.
Cited by
Sorted by citations
Jaffar, Z. F., Alvi, A., Rahim, A., Ambreen, S., & Jaffar, M. M. (2026) · Journal of Language, Literature & Social Affairs · doi:10.63056/jllsa.2.5.2026.253
“Preliminary use of generative AI modify study workload by dividing mental tasks rather than changing or affecting core cognitive processing ( Lindo & Cutad, 2024), Generative AI tools assists higher education students in managing workload and in achieving their study goals more efficiently.”
Silatan, H. J., & Baguio, J. B. (2026) · International Journal of Transformative Multidisciplinary Studies · doi:10.66074/j1k2l3p4o
Pelipas, H. J., & Baguio, J. B. (2026) · International Journal of Transformative Multidisciplinary Studies · doi:10.66074/s5d4f3g2h
Uncovering the Career Development Experiences in Remote Areas: Elementary School Teachers in Focus
Maglente, J. A. R., & Baguio, J. B. (2026) · International Journal of Transformative Multidisciplinary Studies · doi:10.66074/y5t4r3e2w
Dicdican, M., & Baguio, J. B. (2026) · International Journal of Transformative Multidisciplinary Studies · doi:10.66074/a9s8d7f6g
Uncovering the Experiences in Makeshift Classrooms: Elementary School Teachers in Focus
Silatan, P. G., & Baguio, J. B. (2026) · International Journal of Transformative Multidisciplinary Studies · doi:10.66074/c5v4b3n2m
In the Spotlight: Success Stories of Award-Winning Elementary School Paper Advisers
Tagod, R., & Baguio, J. B. (2026) · International Journal of Transformative Multidisciplinary Studies · doi:10.66074/q9w8e7r6t
Classroom Intendance Comportment and Teacher Adeptness in Public Elementary Schools
Vidal, J., & Baguio, J. B. (2026) · International Journal of Transformative Multidisciplinary Studies · doi:10.66074/z1x2c3v4b
Your brain on ChatGPT, but whose brain? The missing adolescent in AI-cognition research
Campos, J. P., & Koff, T. (2026) · Frontiers in Developmental Psychology · doi:10.3389/fdpys.2026.1885225
Flores, G. G. J., & Lelis, C. P. (2026) · International Journal of Research and Innovation in Social Science · doi:10.47772/ijriss.2026.100801062
Fei, C., Zhang, Y., Wang, Y., Xu, J., Fu, Y., Wang, H., Tlili, A., Yang, D., & 黄荣怀 (2026) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt68022.2026.00050
Gozum, I. E. A. (2026) · Journal of Religion and Health · doi:10.1007/s10943-026-02818-9
- Authors
- Lindo, M. R., & Cutad, C. A.
- Affiliations
- University of the Immaculate Conception (PH)
- University of Southeastern Philippines (PH)
- ORCID
- Lindo, Maria Rizalie
- Venue
- IMCC Journal of Science
- Publisher
- Iligan Medical Center College
- Published
- 2024-12
- Volume
- 4
- Issue
- 2
- Pages
- 18-22
- ISSN
- 2783-0357 (ISSN-L)
- 2783-0365
- OpenAlex
- W7125985683
- Cited by
- 3
- References
- 0
- Citations by year
- 2026: 3
- FWCI
- 2.34
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- IMCC Journal of Science (publishedVersion, cc-by)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Psychology › Developmental and Educational Psychology › Educational Strategies and Epistemologies
- Keywords
- generative AI in education, student self-regulation, cognitive offloading, delayed retention, metacognition, high school students, metacognitive regulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
