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¶
Claim · All claims
Evidence · 1 study · associationalr1·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? Social and contextual factors significantly predicted AI-assisted academic dishonesty, with ethical and moral education emerging as a negative predictor. → 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 · r1
Multiple regression analyses in the survey of 1,045 undergraduates tested social and contextual factors measured with a researcher-developed instrument. The article reports "ethical and moral education emerged as a negative predictor" of AI-assisted academic dishonesty. No effect sizes are printed in the available text.
"In addition, social norms, peer behaviors, family attitudes, insufficient sanctions, teacher attitude, high expectations, and adverse conditions significantly predicted AI-assisted academic dishonesty, whereas ethical and moral education emerged as a negative predictor."
Discussion¶
Related Claims¶
- Cheating tendency significantly predicts AI-assisted academic dishonesty, and the association is stronger at higher levels of AI use — related
- Student perceptions of AI use in academic work are neutral and range from viewing AI as valid learning assistance to viewing it as cheating, depending on task type, transparency, and assessment design — related
- Human guidance remains essential for ethical grounding when integrating AI with situated learning, including resisting complacency toward generative AI outputs — related
- Students rate their ethical awareness of AI higher than instructors, while instructors report stronger willingness to experiment with AI tools and higher behavioural intention — related
- Cheating tendency mediates the effects of procrastination, learned helplessness, and self-efficacy on AI-assisted dishonesty, with indirect effects stronger at higher AI use — related
- GenAI use is positively associated with attitude, subjective norm, perceived behavioral control, and behavioral intention toward GenAI-assisted learning — related
- GenAI undermines academic integrity systemically rather than as individual dishonesty, because it optimises plausibility over truth and erodes the conditions for integrity — related
- Instructors' moral awareness and intention grow parallel with institutional and social support — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
social-contextual-predictors-ai-assisted-dishonesty |
title |
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 |
description |
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 negative... |
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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