Investigating the threat of AI to undergraduate medical school admissions: a study of its potential impact on the rating of applicant essays
Bibliographic record
Abstract
Background: Medical school applications often require short written essays or personal statements, which are purportedly used to assess professional qualities related to the practice of medicine. With generative artificial intelligence (AI) tools capable of supplementing or replacing inputs by human applicants, concerns about how these tools impact written assessments are growing. This study explores how AI influences the ratings of essays used for medical school admissions. Methods: A within-subject experimental design was employed. Eight participants (academic clinicians, faculty researchers, medical students, and a community member) rated essays written by 24 undergraduate students and recent graduates from McMaster University. The students were divided into four groups: medical school aspirants with AI assistance (ASP-AI), aspirants without AI assistance (ASP), non-aspirants with AI assistance (NASP-AI), and essays generated solely by ChatGPT 3.5 (AI-ONLY). Participants were provided training in the application of single Likert scale tool before rating. Differences in ratings by writer group were determined via one-way between group ANOVA. Results: = .358). The intraclass correlation coefficient was .147. Conclusion: The proliferation of AI adds to prevailing questions about the value personal statements and essays have in supporting applicant selection. We speculate that these assessments hold less value than ever in providing authentic insight into applicant attributes. In this context, we suggest that medical schools move away from the use of essays in their admissions processes.
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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 | 0.025 | 0.173 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".