Adding a cross-factor composite from a teacher study yields r = .079, still with an interval including zero, and latent profiles reveal over- and underestimation of competence¶

Claim · All claims
Evidence · 1 study · quant-synthesis r1 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? In an expanded four-effect pool adding a Zhang et al. cross-factor composite, the pooled correlation was r = .079 with a Hartung-Knapp 95% CI of [-.025, .181], which includes zero. → Daniele Verí 2026

Evidence¶

Daniele Verí 2026¶

Daniele Verí. (2026). Beyond AI Literacy: A Structured Review and Exploratory Meta-Analysis of Measures for Competent Generative-AI Use. arXiv. https://arxiv.org/abs/2609.15624 doi:10.48550/arxiv.2609.15624

q2 · i? · quant-synthesis · r1

Sensitivity analysis adding a composite mean of 12 dependent cross-factor correlations from a teacher study (N = 288) to the three direct effects. The composite measures a different comparison than total-score correlations; its interval also includes zero.

"Adding the Zhang cross-factor composite yields four effects and a combined reportedN = 3,053: r = .079, Hartung-Knapp 95% CI [-.025, .181], normal-theory CI [.021, .136], and model-based prediction interval [-.070, .224]."

Discussion¶

Related Claims¶

• Perceived and objectively demonstrated AI literacy show only limited alignment in the same samples (pooled r = .055, interval including zero) — related
• Teachers' self-reported AI literacy correlates only weakly with their objectively measured performance (r = 0.07–0.24) — a broader claim this one bears on
• Latent profile analysis of combined SR and OB factors identifies six distinct teacher profiles spanning overestimation, underestimation, and alignment — related
• SR–OB profile patterns differ by prior AI literacy experience: overestimation appears only among experienced teachers and a low–low profile only among inexperienced teachers — related
• Students' self-reported AI literacy was moderately negatively correlated with objectively measured AI competency, suggesting potential overestimation of AI abilities — related
• Experienced teachers diagnose underestimating, overestimating, and uninterested student profiles more accurately than novices, with partial eye-tracking support — related
• Teachers report very high self-perceived competence and efficacy that are virtually unrelated to their schools' prior achievement levels — possibly the same claim (merge candidate)
• The induction did not increase teachers' perceived similarity with focal students — related