Understanding PSE students’ reactions to the postplagiarism concept: a quantitative analysis
Why this work is in the frame
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Bibliographic record
Abstract
This study examines postsecondary education (PSE) students’ perspectives on postplagiarism—a framework that reconceptualizes academic integrity in response to generative artificial intelligence (GenAI). Through a quantitative survey of 581 PSE students across five English-speaking countries, the research investigated student responses to the six tenets of postplagiarism articulated by Eaton (Int J Educ Integr 19:23, 2023a). The findings reveal a complex pattern of acceptance and resistance: while students broadly embrace the integration of GenAI in academic work, with 93.1% acknowledging the normalization of hybrid human–AI writing, significant concerns persist. Notable resistance emerged regarding the distinction between human and AI-generated content (65.92%), the potential impact of AI on human creativity (60.76%), and the retention of human agency in writing (32.7%). The study also validates a novel instrument for measuring postplagiarism perspectives, achieving acceptable internal consistency (Cronbach’s alpha = 0.718) while identifying areas for refinement. These insights suggest that educational institutions must develop nuanced policies that address student concerns while facilitating ethical AI integration, particularly in areas of attribution, creative expression, and academic agency. The findings contribute to our understanding of how academic integrity frameworks can evolve to remain relevant in an AI-integrated educational landscape.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Research integrity Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Research integrity Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it