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TRUVACE RECORD VERSION
record: TRV-2026-2779
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:22:32.269283Z
status: published
lens: g_space
sector: education
headline: Can large language models generate exam questions comparable to humans? A systematic review and meta-analysis study in medical education
dek: Introduction Recently developed artificial intelligence (AI), particularly large language models (LLMs), e.g. GPT, streamline the development of multiple-choice questions (MCQs); however, concerns remain about psychometric quality, fairness across learner groups, and potential bias.Methods Following PRISMA 2020, we searched PubMed, Google Scholar, Web of Science, and the Cochrane Library for studies comparing both approaches in health education. We extracted difficulty and discrimination outcomes and assessed ri…
gain_title: In a meta-analysis of 12 studies in health education, AI-generated multiple-choice questions showed no difference from human-written ones on difficulty (SMD 0.05) or discrimination (SMD −0.10).
problem_title: (none)
trace_subject: (none)
gain_reading: In a meta-analysis of 12 studies in health education, AI-generated multiple-choice questions showed no difference from human-written ones on difficulty (SMD 0.05) or discrimination (SMD −0.10).
gain_evidence: There was no evidence of a difference between AI-generated and human-authored items for difficulty (SMD 0.05, 95% CI −0.30 to 0.40; p = 0.77; I2 = 82%)
problem_reading: (none)
problem_evidence: (none)
quick_read: A systematic review and meta-analysis pooled 12 studies comparing multiple-choice questions written by large language models with those written by humans in health education. It found no difference in difficulty (SMD 0.05, 95% CI −0.30 to 0.40) or in discrimination (SMD −0.10, 95% CI −0.35 to 0.15).

The authors say this makes AI-generated items best suited to supplementing expert item writing in lower-stakes settings. They propose staged, auditable safety checkpoints, including provenance tracking, continuous psychometric monitoring and human gatekeeping.

Results varied widely between studies, and the evidence base is limited, with little attention to equity. Human-written questions were more difficult in medical licensing examinations, and dropping a single study changed the discrimination result to favour AI.
limitation: The evidence base is limited, with little attention to equity, and between-study heterogeneity was high; excluding one study altered the discrimination result.
tag: Evidence-backed gain
key_points: Twelve studies were included: two randomised trials and ten non-randomised studies. | Eleven studies supplied learner-derived difficulty data and twelve supplied discrimination data. | Difficulty showed no pooled difference (SMD 0.05, p = 0.77, I2 = 82%). | Discrimination showed no pooled difference (SMD −0.10, p = 0.42, I2 = 70%). | Human-generated questions were more difficult in medical licensing examinations. | Removing one study in sensitivity analysis showed a significant benefit for AI-generated items on discrimination. | The authors describe this as the first review comparing AI and human-generated MCQs.
rundown: The authors followed PRISMA 2020 and searched PubMed, Google Scholar, Web of Science and the Cochrane Library for studies comparing AI and human question writing in health education. Risk of bias was assessed with the original Cochrane tool for randomised trials and ROBINS-I for non-randomised studies, and random-effects meta-analyses were run.

Heterogeneity was high for both outcomes (I2 of 82% for difficulty and 70% for discrimination). Subgroup analyses by AI model and educational domain did not fully explain it.

The authors recommend a precautionary approach with provenance, continuous psychometric surveillance and human gatekeeping, to support equitable, culturally safe, high-quality outputs. They frame AI items as supplements to expert development rather than replacements for now.
sources:
- peer_reviewed | Medical Teacher | https://doi.org/10.1080/0142159x.2026.2691072 | 2026-06-21
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