Evidence map›Paper›PMID 38353690›Full record

ArticleEuropean journal of clinical pharmacology2024

ChatGPT for generating multiple-choice questions: Evidence on the use of artificial intelligence in automatic item generation for a rational pharmacotherapy exam.

Yavuz Selim Kıyak, Özlem Coşkun, Işıl İrem Budakoğlu, Canan Uluoğlu

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Article in European journal of clinical pharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  4. Artificial Intelligence-Assisted Generation of Case Scenarios and Multiple-Choice Questions in Psychiatry: A Pilot Study.Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yavuz Selim KıyakDepartment of Medical Education and Informatics, Faculty of Medicine, Gazi University, Ankara, Turkey. yskiyak@gazi.edu.tr.ORCID http://orcid.org/0000-0002-5026-3234
Özlem CoşkunDepartment of Medical Education and Informatics, Faculty of Medicine, Gazi University, Ankara, Turkey.ORCID http://orcid.org/0000-0001-8716-1584
Işıl İrem BudakoğluDepartment of Medical Education and Informatics, Faculty of Medicine, Gazi University, Ankara, Turkey.ORCID http://orcid.org/0000-0003-1517-3169
Canan UluoğluDepartment of Medical Pharmacology, Faculty of Medicine, Gazi University, Ankara, Turkey.ORCID http://orcid.org/0000-0003-0682-5794

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeArtificial intelligence, specifically large language models such as ChatGPT, offers valuable potential benefits in question (item) writing. This study aimed to determine the feasibility of generating case-based multiple-choice questions using ChatGPT in terms of item difficulty and discrimination levels.

methodsThis study involved 99 fourth-year medical students who participated in a rational pharmacotherapy clerkship carried out based-on the WHO 6-Step Model. In response to a prompt that we provided, ChatGPT generated ten case-based multiple-choice questions on hypertension. Following an expert panel, two of these multiple-choice questions were incorporated into a medical school exam without making any changes in the questions. Based on the administration of the test, we evaluated their psychometric properties, including item difficulty, item discrimination (point-biserial correlation), and functionality of the options.

resultsBoth questions exhibited acceptable levels of point-biserial correlation, which is higher than the threshold of 0.30 (0.41 and 0.39). However, one question had three non-functional options (options chosen by fewer than 5% of the exam participants) while the other question had none.

conclusionsThe findings showed that the questions can effectively differentiate between students who perform at high and low levels, which also point out the potential of ChatGPT as an artificial intelligence tool in test development. Future studies may use the prompt to generate items in order for enhancing the external validity of the results by gathering data from diverse institutions and settings.

Indexed as

HypertensionStudents, MedicalArtificial IntelligenceHumansSchools, MedicalArtificial intelligenceAutomatic item generationChatGPTMedical educationMultiple-choice questionsRational pharmacotherapy

Identifiers

PMID38353690

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.