OSCEai: personalized interactive learning for undergraduate medical education
Bibliographic record
Abstract
Background: This study aims to evaluate the effectiveness of the OSCEai, a large language model-based platform that simulates clinical encounters, in enhancing undergraduate medical education. Methods: A web-based application, OSCEai, was developed to bridge theoretical and practical learning. Following use, medical students from the University of Calgary Class of 2026 completed an anonymized survey on the usability, utility, and overall experience of OSCEai. Results: A total of 37 respondents answered the anonymized survey. The OSCEai platform was highly valued for its ability to provide data on demand (33/37), support self-paced learning (30/37), and offer realistic patient interactions (29/37). The ease of use and medical content quality were rated at 4.73 (95% CI: 4.58 to 4.88) and 4.70 (95% CI: 4.55 to 4.86) out of 5, respectively. Some participants (8/37) commented that few cases were not representative and needed clarification about app functionality. Despite these limitations, OSCEai was favorably compared to lecture-based teaching methods, with an overall reception rating of 4.62 (95% CI: 4.46 to 4.79) out of 5. Interpretation: The OSCEai platform fills a gap in medical training through its scalable, interactive, and personalized design. The findings suggest that integrating technologies, like OSCEai, into medical curricula can enhance the quality and efficacy of medical 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".