AI evidence extraction

Understanding AIgiarism in higher education: the lens of general AI attitudes and moral disengagement

Muhammad Waqas, Alishba Hania · Studies in Higher Education · 2025

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

0/4

Quality (LMQS)

Evidence

Citations

20.64

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1080/03075079.2025.2497479

Methodology & findings

Study design

Cross-sectional survey study using stratified random sampling of 373 undergraduate students.

Sample

N = 373, 1 group

Primary method

Stratified random sampling; validated instruments for measurement; moderated mediation analysis with path coefficients (β) and p-values reported. Specific software not mentioned in abstract.

Main result

The study reveals "a moderated mediation model where attitudes towards AI technology significantly mediate the relationship between academic support and AIgiarism (β = −0.384, p < .001), with moral disengagement moderating both paths (β = 0.152, p < .002; β = 0.178, p < .001)." These findings demonstrate that institutional support influences students' engagement with AI plagiarism through their attitudes toward AI, and this relationship is moderated by moral disengagement processes.

Reports effect sizes.

Research paradigm

Positivist/quantitative empiricism

Author conclusions

The authors conclude that "these findings challenge traditional deterrence-based approaches to academic integrity and introduce the 'Mediated Digital Integrity Model,' emphasizing the dynamic interplay between institutional, attitudinal, and moral factors" and that "our research contributes to the emerging literature on digital academic integrity and provides crucial insights for developing integrated AI ethics programs in higher education."

Risk of bias

Selection bias: stratified random sampling reduces but does not eliminate bias; Self-report bias: reliance on validated instruments measuring sensitive behavior (plagiarism); Geographic limitation: single province (Jiangsu) may limit generalizability; Cross-sectional design: cannot establish causality; Potential social desirability bias in responses about academic integrity violations; Selection bias: Study limited to government universities in Jiangsu Province, China, potentially limiting generalizability; Self-report bias: Survey-based methodology relies on self-reported attitudes and behaviors regarding plagiarism; Cross-sectional design: Inability to establish temporal causality; Selection bias: Stratified random sampling reduces but may not eliminate selection bias in self-reported academic integrity behaviors; Social desirability bias: Self-reported survey responses about plagiarism and moral disengagement are susceptible to social desirability bias; Geographic limitation: Sample from government universities in Jiangsu Province, China may not generalize to other regions or institution types; Cross-sectional design: Cannot establish causal directionality or account for temporal confounds

Open questions raised

The authors identify a gap in understanding AI-facilitated plagiarism (AIgiarism) and call for development of integrated AI ethics programs in higher education that move beyond deterrence-based approaches.
The authors identify a gap in understanding digital academic integrity in the context of AI integration, indicating the need for integrated AI ethics programs in higher education rather than traditional deterrence-based approaches.
The abstract identifies the need for integrated AI ethics programs in higher education to address AI-facilitated plagiarism, but does not explicitly detail specific future research directions.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 56%

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