Artificial Intelligence as a Discriminator of Competence in Urological Training: Are We There?
Bibliographic record
Abstract
PURPOSE: Assessments in medical education play a central role in evaluating trainees' progress and eventual competence. Generative artificial intelligence is finding an increasing role in clinical care and medical education. The objective of this study was to evaluate the ability of the large language model ChatGPT to generate examination questions that are discriminating in the evaluation of graduating urology residents. MATERIALS AND METHODS: Graduating urology residents representing all Canadian training programs gather yearly for a mock examination that simulates their upcoming board certification examination. The examination consists of a written multiple-choice question (MCQ) examination and an oral objective structured clinical examination. In 2023, ChatGPT Version 4 was used to generate 20 MCQs that were added to the written component. ChatGPT was asked to use Campbell-Walsh Urology, AUA, and Canadian Urological Association guidelines as resources. Psychometric analysis of the ChatGPT MCQs was conducted. The MCQs were also researched by 3 faculty for face validity and to ascertain whether they came from a valid source. RESULTS: The mean score of the 35 examination takers on the ChatGPT MCQs was 60.7% vs 61.1% for the overall examination. Twenty-five of ChatGPT MCQs showed a discrimination index > 0.3, the threshold for questions that properly discriminate between high and low examination performers. Twenty-five percent of ChatGPT MCQs showed a point biserial > 0.2, which is considered a high correlation with overall performance on the examination. The assessment by faculty found that ChatGPT MCQs often provided incomplete information in the stem, provided multiple potentially correct answers, and were sometimes not rooted in the literature. Thirty-five percent of the MCQs generated by ChatGPT provided wrong answers to stems. CONCLUSIONS: Despite what seems to be similar performance on ChatGPT MCQs and the overall examination, ChatGPT MCQs tend not to be highly discriminating. Poorly phrased questions with potential for artificial intelligence hallucinations are ever present. Careful vetting for quality of ChatGPT questions should be undertaken before their use on assessments in urology training examinations.
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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 | 0.014 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".