The impact of GenAI on applicant behaviour, performance, and interview reliability during virtual interviews for medical school admissions
Bibliographic record
Abstract
As artificial intelligence becomes increasingly powerful and accessible, education programs must guard against risks during student selection. Chief among those is avoidance of rewarding applicants who use prohibited tools. The trend towards increased use of virtual interviews makes programs particularly susceptible. To empirically study the risk and mitigation strategies, we conducted a randomized experiment comparing preparatory behaviors, performance, reliability and acceptability for candidates encouraged to surreptitiously use ChatGPT relative to two control groups. No advantages were observed (ChatGPT group mean = 3.67 (sd = 0.69) vs 3.74 (sd = 0.61) for 'usual practice' controls and 3.73 (sd = 0.80) for participants who were foretold station content). Reducing the time interviewees had to engage with ChatGPT off camera did not harm performance (3.73 (sd = 0.58) vs 3.70 (sd = 0.81) for controls); precision of test scores (SEMeas = 0.34 vs 0.39); or acceptability ratings (mean = 4.0 vs 4.1). These findings suggest an easy way to increase confidence in the fairness of virtual interviews.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".