Claims

Self-efficacy is positively associated with acceptance outcomes for AI-assisted English learning tools in adjusted regression models

Claim · All claims
Evidence · 1 study · associational r2 · q2 · i3 large
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 i3 In full-sample adjusted OLS models, self-efficacy showed statistically significant positive associations with all four acceptance outcomes (all p < 0.001). → Wu J 2026

Evidence

Wu J 2026

Wu J, Wang Y, He Y, Yin X, Chen F and Wan B (2026) Acceptance of AI-assisted English language learning tools in higher education: psychological correlates across disciplinary and proficiency groups. Front. Psychol. 17:1806457. https://doi.org/10.3389/fpsyg.2026.1806457

q2 · i3 · associational · r2

Full-sample adjusted OLS regression (Table 4) predicting satisfaction from self-efficacy with robust standard errors and covariate adjustment; the row reports a standardized coefficient of β = 0.787, adjusted R2 of 0.656, and N = 210. Satisfaction models had the highest adjusted R2 for self-efficacy and motivation.

"Satisfaction Self-efficacy 0.781 0.043 0.787 [0.700, 0.866] <0.001 0.666 0.656 1.995 210"

Discussion

Page metadata
Field Value
type claim
id self-efficacy-positive-ai-english-acceptance
title Self-efficacy is positively associated with acceptance outcomes for AI-assisted English learning tools in adjusted regression models
description Self-efficacy is positively associated with acceptance outcomes for AI-assisted English learning tools in adjusted regression models
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 — wu-j-2026

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