AI evidence extraction

Academic Cheating with Generative AI in Higher Education: An Extended Model of the Theory of Planned Behavior with Motivational Antecedents

Muhammad Taslim, Riki Purnama Putra, Nurussakinah Daulay, Sefa Bulut · Jurnal Psikologi · 2025

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

8/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.22146/jpsi.107932

Methodology & findings

Study design

Quantitative survey with structural equation modeling.

Sample

N = 243, 1 group

Primary method

Confirmatory partial least squares structural equation modeling (PLS-SEM) with confirmatory factor analysis. Model fit was assessed using SRMR = .045 and NFI = .92.

Main result

The study found that "the proposed model can explain significant variance in cheating intentions and behavior" and that "perceived behavioral control proved to be the strongest predictor of cheating intentions." Additionally, "both behavioral intention and perceived behavioral control directly and strongly predicted self-reported academic cheating behavior."

Reports effect sizes.

Research paradigm

Positivist/Quantitative

Author conclusions

The authors concluded that "the extended TPB is a robust framework for this phenomenon, highlighting the dominant role of perceived behavioral control." They further emphasized that "practical implications emphasize the need for institutional interventions focused on reducing the perceived ease and increasing the perceived risk of GenAI misuse to maintain academic integrity."

Risk of bias

Self-reported behavior (social desirability bias); Cross-sectional design (cannot establish causality); Single geographic location (West Java, Indonesia); Undergraduate student sample only; Self-reported cheating behavior (social desirability bias); Cross-sectional survey design (cannot establish causality); Single geographic location (West Java) - limited generalizability; Undergraduate-only sample; Self-report bias: Study relies on self-reported academic cheating behavior, which may be subject to social desirability bias; Selection bias: Sample limited to 243 undergraduate students in West Java, potentially non-representative of broader population; Cross-sectional design: Cannot establish causal relationships, only associations; Single method bias: Reliance solely on survey methodology without triangulation

Open questions raised

Not explicitly stated in the abstract
The authors identify the need for institutional interventions targeting perceived behavioral control regarding GenAI misuse, suggesting future research should examine implementation and effectiveness of such interventions.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 60%

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