Acceptance scores are broadly held across learner characteristics, with frequency of use the only significant and weak predictor¶
Claim · All claims
Evidence · 1 study · associationalr1·q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2031: baselines and conditions change
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.
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q2 i? In an exploratory regression, learner characteristics explained little variance in acceptance (R2 = .034); only frequency of use was significant (b = .121, p = .002), while English level and length of study were non-significant. → Zhorabekova A 2026
Evidence¶
Zhorabekova A 2026¶
Zhorabekova A, Duisenova M, Kurmasheva M and Yesnazar A (2026). Acceptance of AI-gamified adaptive learning in EFL: bifactor evidence for essential unidimensionality and the limits of subscale scoring. Front. Educ. 11:1929123. https://doi.org/10.3389/feduc.2026.1929123
q2 · i? · associational · r1
Exploratory ordinal-coded regression of the composite acceptance score on frequency of use, English level, length of study, and year of study (N = 401). The article reports R2 = .034 and model F p = .009; English level (p = .80) and length of study (p = .47) were non-significant.
"The only significant predictor was frequency of use (b = .121, p = .002): more frequent users reported slightly higher acceptance."
Discussion¶
Related Claims¶
- Learning motivation is positively associated with acceptance outcomes for AI-assisted English learning tools in adjusted regression models — related
- Anxiety shows positive concurrent associations with acceptance outcomes for AI-assisted English learning, and should not be read as uniformly negative or beneficial — related
- Practice frequency, not cumulative usage time, was strongly associated with posttest presentation performance after controlling for baseline and demographics — related
- High-frequency LLM users show higher minimum course outcomes and reduced score variability, though interaction level is not a dominant outcome predictor — related
- Self-efficacy is positively associated with acceptance outcomes for AI-assisted English learning tools in adjusted regression models — reports the opposite
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
acceptance-frequency-of-use-weak-predictor |
title |
Acceptance scores are broadly held across learner characteristics, with frequency of use the only significant and weak predictor |
description |
Acceptance scores are broadly held across learner characteristics, with frequency of use the only significant and weak predictor |
status |
review |
evidence_strength |
weak |
newest_evidence |
2026 |
recheck_by |
2031 |
generated |
by process:wiki-ingest on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — zhorabekova-a-2026 |
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