Cheating tendency significantly predicts AI-assisted academic dishonesty, and the association is stronger at higher levels of AI use¶
Claim · All claims
Evidence · 1 study · associationalr2·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? Cheating tendency significantly predicted AI-assisted academic dishonesty, and the interaction term indicated the association was stronger at higher levels of AI use. → Yilmaz A 2026
Evidence¶
Yilmaz A 2026¶
Yilmaz A (2026) The psychological mechanisms and behavioral determinants of academic integrity in the age of artificial intelligence. Front. Psychol. 17:1853790. https://doi.org/10.3389/fpsyg.2026.1853790
q2 · i? · associational · r2
Moderation tested with Hayes' PROCESS Model 14 in the survey of 1,045 undergraduates. The article reports the "interaction term indicated that the association between cheating tendency and AI-assisted academic dishonesty was stronger at higher levels of AI use." No effect size is printed in the available text.
"Cheating tendency significantly predicted AI-assisted academic dishonesty, and the interaction term indicated that the association between cheating tendency and AI-assisted academic dishonesty was stronger at higher levels of AI use."
Discussion¶
Related Claims¶
- Cheating tendency mediates the effects of procrastination, learned helplessness, and self-efficacy on AI-assisted dishonesty, with indirect effects stronger at higher AI use — a broader claim this one bears on
- Social norms, peer behaviors, family attitudes, insufficient sanctions, teacher attitude, high expectations, and adverse conditions positively predict AI-assisted dishonesty, while ethical and moral education negatively predicts it — related
- Early-semester LMS interaction data predicts AI-assisted cheating risk in the final exam, with Logistic Regression achieving AUC = 0.763 under LOOCV — related
- Academic procrastination and learned helplessness positively predict cheating tendency, while academic self-efficacy negatively predicts it among university students — related
- Self-efficacy negatively correlates with cheating, but actual ability does not inversely correlate with dishonesty — related
- Students with higher perceived academic dishonesty perceive greater risk in using ChatGPT — related
- Course-module views, assignment submissions, and video-access days are the most stable predictors, all lower in the high-risk group (Cliff's delta values from 0.50 to 0.60) — related
- Perceived academic dishonesty is not directly related to ChatGPT use frequency or intention to use — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
cheating-tendency-predicts-ai-dishonesty-moderated-by-ai-use |
title |
Cheating tendency significantly predicts AI-assisted academic dishonesty, and the association is stronger at higher levels of AI use |
description |
Cheating tendency significantly predicts AI-assisted academic dishonesty, and the association is stronger at higher levels of AI use |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
generated |
by process:wiki-ingest on 2026-10-11 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — yilmaz-a-2026 |
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