Learners perceive AI applications as useful for regulating their learning but not for regulating motivation¶
Claim · All claims
Evidence · 1 study · reviewr2·q2
Currency · newest study 2025 · checked against its sources 2026-10-20 (automated, 1 of 1 entry) · not reviewed by a person
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? Learners perceived AI applications as useful for supporting metacognitive, cognitive, and behavioral regulation across SRL areas, but not for regulating motivation. → Jennifer Lock 2025
Evidence¶
Jennifer Lock 2025¶
Jennifer Lock, Soraya Arteaga, Carol Johnson. (2025). Integrating AI in Online Learning in Higher Education: A Literature Review. International Journal on Innovations in Online Education, 9(1). https://doi.org/10.1615/IntJInnovOnlineEdu.2026060433
q2 · i? · review · r2
The review reports, citing Jin et al. (2023), that "learners perceived AI applications as useful for supporting metacognitive, cognitive, and behavioral regulation" across SRL areas but not motivation. The review also reports Jin et al. found learner identity, activeness, and position necessary for AI-supported SRL. No effect size is printed.
"learners perceived AI applications as useful for supporting metacognitive, cognitive, and behavioral regulation across different SRL areas, but not for regulating motivation."
Discussion¶
Learner Variables¶
- Motivation — outcome: instruction changes it
- Self-Regulation — outcome: instruction changes it
Related Claims¶
- Evidence indicates socially shared regulation of learning occurs across SRL areas and may promote learning and performance — related
- AI's direct impact on SRL concentrates on metacognitive and cognitive aspects while motivation remains underexplored — related
- Self-efficacy and learning motivation serially mediate the link from AIGC affordance to self-regulated learning — related
- The pooled effect of self-regulated learning does not differ significantly by strategy type (cognitive, metacognitive, resource management, motivational) — related
- Metacognitive self-regulation mediates the relationships between all three AI interaction mechanisms and both higher-order outcomes — related
- Frequent generative AI use for essay writing can foster metacognitive laziness, reducing intrinsic motivation and learning autonomy — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
ai-supports-srl-not-motivation-regulation |
title |
Learners perceive AI applications as useful for regulating their learning but not for regulating motivation |
description |
Learners perceive AI applications as useful for regulating their learning but not for regulating motivation |
status |
review |
evidence_strength |
weak |
newest_evidence |
2025 |
source_checked |
2026-10-20 |
generated |
by process:wiki-ingest on 2026-10-11 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — jennifer-lock-2025 |
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