Academic Integrity in Teacher Education in the GenAI Era: Academic Coordinators’ Perspectives in Spain
Bibliographic record
Abstract
This study explores academic dishonesty in pre-service teacher training programmes from the perspective of academic managers in Spanish universities. Using a quantitative design, based on an online questionnaire and a sample of 198 academic coordinators, it examines perceptions of the prevalence, evolution, and severity of 28 dishonest behaviours, including those involving generative artificial intelligence (GenAI). Results reveal that GenAI-related misconduct is perceived as particularly severe and rapidly increasing, though traditional forms such as plagiarism and contract cheating remain common. Significant differences in perception were found across variables such as age, institutional type, and years of management experience. A composite index (DB-PES) was developed to categorise behaviours by perceived urgency. Findings suggest that academic dishonesty is a dynamic phenomenon requiring systemic and pedagogically grounded responses. Institutions must prioritise ethical training, develop clear policies on AI use, and adopt flexible, responsive mechanisms to address evolving examples of misconduct. This study offers new insights to guide integrity strategies in teacher education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".