ANN analysis identifies AI learning self-efficacy and perceived usefulness as the strongest and most stable predictors of AI learning anxiety, with only modest advantage over linear benchmarks¶

Claim · All claims
Evidence · 1 study · associational r2 · q1
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q1 i? In cross-validated ANN prediction, self-efficacy was the most important predictor of AI learning anxiety, followed by perceived usefulness. → Jiang H 2026 q1 i? The ANN achieved mean testing R2 of 0.5126 and showed only a small predictive advantage over linear and Ridge regression benchmarks. → Jiang H 2026

Evidence¶

Jiang H 2026¶

Jiang H, Chen B and Chen Q (2026). Multidimensional perceived school support and AI learning anxiety among university students: the mediating roles of control-value appraisals. Front. Psychol. 17:1951992. https://doi.org/10.3389/fpsyg.2026.1951992

q1 · i? · associational · r2

Supplementary ANN (5–3–1 network, repeated 10-fold cross-validation with 10 repetitions, 100 held-out evaluations) predicting anxiety from ES, IFS, IS, ASE, and APU. The article reports "AI learning self-efficacy was the most important predictor of AI learning anxiety in the ANNmodel,followedbyAIperceivedusefulness", with mean testing R2 of 0.5126 and support-dimension importance scores less stable.

"AI learning self-efficacy was the most important predictor of AI learning anxiety in the ANNmodel,followedbyAIperceivedusefulness."

Discussion¶

Related Claims¶

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