The Artificial Intelligence (AI) vs Human Exam Assessment and Development (AHEAD) Trial is a participant-blinded randomized controlled trial conducted among first-year medical students at the University of British Columbia. The study evaluates whether multiple-choice examination questions generated using large language models (LLMs) perform comparably to traditionally human-written questions in medical education.

Participants were randomized to complete one of two versions of a formative mock final examination consisting of 112 case-based single-best-answer multiple-choice questions (MCQs) aligned with the same course learning objectives. One exam version contained AI-generated questions produced using a structured LLM workflow with independent AI verification, while the other contained questions authored by senior medical students using conventional methods.

The study evaluates exam feasibility, psychometric reliability, validity, student acceptability, and educational impact. Outcomes include exam performance, item discrimination indices, distractor efficiency, student perceptions of exam quality and difficulty, and changes in perceived preparedness for the upcoming summative examination.

The AHEAD Trial (AI vs Human Exam Assessment and Development) is a single-center, participant-blinded randomized controlled trial conducted among first-year Doctor of Medicine (MD) students enrolled in the Foundations of Medical Practice I (MEDD 411) course at the University of British Columbia.

Participants were randomized in a 1:1 ratio to complete either an AI-generated or a human-generated mock final examination. Both exams consisted of 112 case-based single-best-answer multiple-choice questions (MCQs) aligned with the same MEDD 411 curricular objectives.

AI-generated questions were produced using a structured workflow involving ChatGPT for question generation and Google Gemini for independent verification. Human-generated questions were authored by senior medical students without AI assistance and underwent independent peer review. Both exams followed identical formatting guidelines and assessed the same learning objectives.

All participants completed identical pre-exam and post-exam surveys assessing demographic characteristics, familiarity with artificial intelligence in education, and perceptions of the examination experience. The study evaluates the utility of AI-generated assessments using van der Vleuten's Assessment Utility Framework, including feasibility, reliability, validity, acceptability, and educational impact.

The trial aims to determine whether large language models can accelerate the development of formative medical examinations while maintaining comparable psychometric quality and educational value relative to traditional human-authored questions.

Inclusion Criteria:

• Enrolled first-year medical students in the University of British Columbia MD undergraduate program.
• Can voluntarily consent to participate in the formative mock examination study.

Exclusion Criteria:

• Students who declined participation.
• Students who did not complete the mock examination or required surveys.

Deidentified participant-level examination scores and survey responses will be available upon reasonable request.

Beginning 6 months after publication and ending 5 years after publication.

Data will be shared with researchers who provide a methodologically sound proposal. Requests should be directed to the corresponding author.

Participants were blinded to the source of the examination questions (AI-generated vs human-generated). All items were reviewed to remove indicators of authorship before distribution.