Faculty-generated SBMCQs met all three ideal item-analysis criteria more often than ChatGPT-generated SBMCQs (19 vs. 9 of 40)¶
Claim · All claims
Evidence · 1 study · causalr2·q3
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q3 i? Among 40 items per source, 19 faculty SBMCQs (47.5%) versus 9 ChatGPT SBMCQs (22.5%) met ideal criteria of appropriate difficulty, high discrimination, and 100% distractor effectiveness. → Chauhan 2025
Evidence¶
Chauhan 2025¶
Chauhan, A., Khaliq, F., & Nayak, K. R. (2025). Assessing Quality of Scenario-Based Multiple-Choice Questions in Physiology: Faculty-Generated vs. ChatGPT-Generated Questions among Phase I Medical Students. International Journal of Artificial Intelligence in Education, 35, 2315–2344. https://doi.org/10.1007/s40593-025-00471-z
q3 · i? · causal · r2
Item analysis of four test sessions in a crossover study of 120 first-year MBBS students, each answering 20 faculty and 20 ChatGPT items. The article reports "19 (47.5%) of the SBMCQs met all three criteria" for faculty versus 9 (22.5%) for ChatGPT; no effect size or significance test is printed.
"In our study,19 (47.5%) of the SBMCQs met all three criteria for an ideal MCQ: appropriate Difficulty Index (30–70%), high Discrimination Index (≥ 0.25), and 100% Distractor Effectiveness. In contrast, the number of ChatGPT-generated SBMCQs meeting these criteria was 9(22.5%)."
Discussion¶
Related Claims¶
- ChatGPT-generated SBMCQs contained a higher rate of nonfunctional distractors than faculty-generated SBMCQs (33.75% vs. 13.75%) — related
- Faculty focus groups raised concerns about authenticity, validity, and reliability of ChatGPT-generated SBMCQs, including cultural and linguistic bias — related
- AI-generated assessment items frequently need faculty correction, supporting AI as a draft generator within quality assurance — possibly the same claim (merge candidate)
- Faculty-generated SBMCQs showed superior discrimination indices and distractor effectiveness compared with ChatGPT-generated SBMCQs — related
- ChatGPT-generated SBMCQs skewed easier than faculty-generated SBMCQs, whose difficulty was predominantly in the appropriate range — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
faculty-sbmcqs-outperform-chatgpt-ideal-criteria |
title |
Faculty-generated SBMCQs met all three ideal item-analysis criteria more often than ChatGPT-generated SBMCQs (19 vs. 9 of 40) |
description |
Faculty-generated SBMCQs met all three ideal item-analysis criteria more often than ChatGPT-generated SBMCQs (19 vs. |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2025 |
generated |
by process:wiki-ingest on 2026-10-27 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — chauhan-2025 |
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