The structural model explains about half the variance in critical thinking and technical problem-solving and about a third in metacognitive self-regulation¶
Claim · All claims
Evidence · 1 study · associationalr1·q2
Currency · newest study 2026 · not yet checked against its sources · 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? The model accounted for 51.7% of variance in critical thinking, 50.8% in technical problem-solving, and 30.7% in metacognitive self-regulation. → Davor I 2026
Evidence¶
Davor I 2026¶
Davor I, Larbi E and Boateng FO (2026). Artificial intelligence-supported learning and higher-order cognitive outcomes: the mediating role of metacognitive self-regulation. Front. Educ. 11:1855869. https://doi.org/10.3389/feduc.2026.1855869
q2 · i? · associational · r1
R² values from the structural model of 533 respondents; the article states the MSR variance explained is "moderate but acceptable for behavioural research." The measurement model fit well (CFI = 0.983, RMSEA = 0.026).
"The model demonstrated substantial explanatory power, accounting for 51.7% of the variance in critical thinking, 50.8% in technical problem-solving, and 30.7% in metacognitive self-regulation."
Discussion¶
Learner Variables¶
- Self-Regulation — outcome: instruction changes it
Related Claims¶
- Cognitive offloading tendency is negatively associated with critical thinking, technical problem-solving, and metacognitive self-regulation — related
- AI task scaffolding is positively associated with critical thinking and technical problem-solving among AI-using university students — related
- AI verification literacy does not directly predict critical thinking or technical problem-solving — related
- Metacognitive self-regulation mediates the relationships between all three AI interaction mechanisms and both higher-order outcomes — related
- Adding time-varying task features roughly halves unexplained variance in disciplinary engagement ratings — related
- Metacognitive strategies correlate strongly with problem-solving skills, and behavioural metrics correlate with task performance — related
- Epistemic orientation and intellectual values jointly predict students’ critical use of genAI, accounting for about one-third of its variance — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
sem-variance-explained-outcomes |
title |
The structural model explains about half the variance in critical thinking and technical problem-solving and about a third in metacognitive self-regulation |
description |
The structural model explains about half the variance in critical thinking and technical problem-solving and about a third in metacognitive self-regulation |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
generated |
by process:wiki-ingest on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — davor-i-2026 |
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