The eight-predictor model explains 27.3% of variance in AI dependency, and multidimensional AI literacy adds explanatory value over a single aggregate score¶
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Evidence · 1 study · associationalr2·q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person
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q2 i? The full regression model was significant and accounted for 27.3% of variance, exceeding the 22.1% obtained when AI literacy was modeled as one aggregate score. → Hilit Maizel 2026
Evidence¶
Hilit Maizel 2026¶
Hilit Maizel, Maya Kalman Halevi, Miri Sarid, and Rony Tutian. (2026). Understanding Student Dependency on AI: The Role of AI Literacy, Academic Self-Efficacy, and Resource Management Strategies. Educ. Sci. 2026, 16, 1123. https://doi.org/10.3390/educsci16071123
q2 · i? · associational · r2
Multiple linear regression on the 478-student analytic sample with AI dependency as criterion. Multicollinearity diagnostics showed no concern (all VIF ≤ 1.87). The article reports "a meaningful increase over the 22.1%" for the multidimensional treatment.
"The overall model was statistically significant, F(8, 469) = 22.03, p < 0.001, and accounted for 27.3% of the variance in AI dependency (R2 = 0.273; adjusted R2 = 0.261)—a meaningful increase over the 22.1% obtained when AI literacy was modeled as a single aggregate score"
Discussion¶
Related Claims¶
- Effort regulation negatively predicts, and general academic help seeking positively predicts, reported AI dependency; time management is non-significant
- Both academic self-efficacy and AI self-efficacy are negatively associated with reported AI dependency, supporting a unified compensatory mechanism
- Skill-based AI literacy (using/understanding and detecting AI) is positively associated with reported AI dependency among higher education students — related
- Predictors operate selectively across AI literacy subdimensions: digital competence fails to predict technical proficiency or communication proficiency, and perceived resources predict only communication proficiency and creative application — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
multidimensional-ai-literacy-model-explains-27-percent-dependency-variance |
title |
The eight-predictor model explains 27.3% of variance in AI dependency, and multidimensional AI literacy adds explanatory value over a single aggregate score |
description |
The eight-predictor model explains 27.3% of variance in AI dependency, and multidimensional AI literacy adds explanatory value over a single aggregate score |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
generated |
by process:wiki-ingest on 2026-10-12 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — hilit-maizel-2026 |
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