Reducing undergraduate students’ trust of commercial contract cheating websites with an academic support literacy intervention
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The acquisition of products and services from the commercial contract cheating industry has an extensive history, with the industry experiencing significant growth during the COVID-19 pandemic. Since then, these commercial entities have added generative artificial intelligence (genAI) to their websites to ensure continued use of their services by postsecondary students. Cheating providers use various other persuasive features (e.g., assurance of quality work, use of the words ‘guarantee’ and ‘secure’) to convince students to trust them and become customers. To counter the efforts of commercial cheating services, education about the cheating industry and academic integrity should reduce any trust that students have in them. We developed an academic support literacy module about appropriate (e.g., university assistance, legitimate tutors) and inappropriate (e.g., contract cheating services) academic support. Before and after the module, 39 introductory psychology students rated how much they trusted various websites using a 12-item consumer trust scale. Although a drop in trust after viewing the module was significant for all three types of academic support websites, it was greatest for contract cheating websites. Significant correlations were also found between the non-planning aspects of impulsiveness (as measured by the Barratt Impulsiveness Scale [BIS-11]; Patton et al. J Clin Psychol 51(6):768–774, 1995) and reputation ratings given for the contract cheating websites. Further study of perceptions (using objective and subjective measures) of contract cheating websites and how aspects of impulsiveness on website perceptions is necessary for the continued development of educational interventions to reduce temptations to engage with the industry. Our study findings contribute to the literature on the promotion of academic integrity and prevention of academic misconduct, particularly contract cheating, through education.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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