Academic Cheating with Generative AI in Higher Education: An Extended Model of the Theory of Planned Behavior with Motivational Antecedents
Bibliographic record
Abstract
This study proposed and tested an extended model of the theory of planned behavior (TPB) to understand the determinants of academic cheating using generative AI (GenAI). This model integrates intrinsic and extrinsic motivation as antecedents of the core constructs of TPB, namely attitude, subjective norms, and perceived behavioral control, to predict cheating intentions and behavior. Quantitative data were collected from 243 undergraduate students in West Java through a survey and analyzed using confirmatory partial least squares structural equation modeling (PLS-SEM). The model demonstrated satisfactory global fit (SRMR = .045; NFI = .92), supporting the hypothesized structure. The results indicate that the proposed model can explain significant variance in cheating intentions and behavior. Perceived behavioral control proved to be the strongest predictor of cheating intentions. More importantly, both behavioral intention and perceived behavioral control directly and strongly predicted self-reported academic cheating behavior. This study concluded that the extended TPB is a robust framework for this phenomenon, highlighting the dominant role of perceived behavioral control. Its 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".