A blinded RCT of 258 first-year UBC medical students found AI-generated exam questions were made 5.6x faster than student-written ones, with similar scores and acceptability, though human items discriminated slightly better and a gender gap emerged only on the AI exam.
258 students
Students randomized (Dec 2024)
112 items
Exam items (Dec 2024)
4.2 minutes
Time per item: AI-generated
19.6 minutes
Time per item: human-written
5.6 x faster
Item-writing efficiency gain (AI vs human)
73.4 %
Exam score, human-generated exam
71.0 %
Exam score, AI-generated exam
Details
Maturity
Pilot
Promoter
University of British Columbia Faculty of Medicine
Period
December 2024 (published June 2026)
Keywords
higher education, medical education, assessment design, academic integrity
Context
Writing a single high-quality multiple-choice exam question for medical education can take instructors or item-writers well over an hour and cost an estimated US$200 to over US$2,000 per usable item, making large question banks expensive to build and maintain.
Objectives
Test whether AI-generated exam questions can match human-written ones on exam performance, item quality and student acceptability, while quantifying the time savings.
Activities
The University of British Columbia Faculty of Medicine ran the AHEAD Trial on 8-9 December 2024, randomizing 258 first-year MD students 1:1 to a blinded 112-item mock exam built either by senior medical students (human-generated) or by a ChatGPT-4-plus-Google-Gemini validation pipeline (AI-generated), with all authorship indicators stripped before delivery.
Results
AI-generated items took 4.2 minutes each to produce versus 19.6 minutes for human-written items, a 5.6-fold efficiency gain (p<0.0001). Overall exam scores were statistically similar (73.4% human-generated vs 71.0% AI-generated, p=0.083). Human-written items had modestly but significantly higher item-discrimination scores (small effect), and student-rated acceptability favoured the human exam on four of nine measures with small effect sizes (Cohen's d <=0.32). Neither exam meaningfully changed students' self-rated exam readiness. An exploratory subgroup analysis found male students outperforming female students on the AI-generated exam (rrb=0.26) but not on the human-generated exam (rrb=0.07).
Conclusions
AI-generated items matched human-written ones on most measures at a fraction of the production time, but the trial was single-center, first-year-only, used senior students rather than faculty as question-writers, was registered retrospectively, and flags the gender-gap signal as requiring demographic monitoring rather than a confirmed effect.
Implementation
Indicative cost
Low (< €50k) — AI-generated items required far less staff time to produce (4.2 vs 19.6 minutes each) than human-written items, implying lower marginal item-writing cost; no absolute dollar figure was reported for this trial.
Time to results
Short (< 1 year) — A single 2-day mock-exam event (8-9 December 2024), with the AI validation pipeline built beforehand.
Staffing & skills
University of British Columbia Faculty of Medicine research team, Senior medical students as human item-writers, ChatGPT-4 + Google Gemini validation pipeline for AI-generated items
Conditions for success
Blinded delivery with authorship indicators stripped from all items
Pre-specified, CONSORT-compliant analysis plan with IRB approval
Validation pipeline combining two AI models for item generation