Claims

AI literacy directly and positively predicts learning engagement among university students

Claim · All claims
Evidence · 1 study · associational r2 · 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? AI literacy has a statistically significant positive direct effect on learning engagement (β = 0.668, p < 0.001), with the model explaining R² = 0.446 of engagement variance. → Wang 2026

Evidence

Wang 2026

Wang, N. (2026). The impact of artificial intelligence literacy on learning engagement among university students: the mediating role of psychological capital and the moderating role of professional identity. Frontiers in Psychology, 17:1892204. https://doi.org/10.3389/fpsyg.2026.1892204

q2 · i? · associational · r2

PLS-SEM path analysis of cross-sectional questionnaire data from 1,198 undergraduates at four Zhengzhou universities. The direct path "was β = 0.668 ( p < 0.001), demonstrating that this effect was statistically significant", and the model's coefficient of determination was R2 = 0.446.

"The standardized path coefficient from artificial intelligence literacy to learning engagement was β = 0.668 ( p < 0.001), demonstrating that this effect was statistically significant. Thus, Hypothesis 1 was supported."

Discussion

Page metadata
Field Value
type claim
id ai-literacy-predicts-learning-engagement
title AI literacy directly and positively predicts learning engagement among university students
description AI literacy directly and positively predicts learning engagement among university students
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 — wang-2026

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