Evidence map›Paper›PMID 42174548›Full record

ArticleBMC medical education2026

AI-powered simulated patients with automated feedback for enhancing headache history-taking skills: a convergent mixed-methods study.

Kridipaka Sindhvananda, Kewalin Ruengwattanachot, Surachai Leksuwankun, Thanakit Pongpitakmetha, Akarin Hiransuthikul, Totsapol Surawattanawong, Prakit Anukoolwittaya, Poonnakarn Panjasriprakarn

Abstract read
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Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Kridipaka Sindhvananda *Division of Integrated Innovation and Digital Technologies, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Kewalin Ruengwattanachot *Division of Integrated Innovation and Digital Technologies, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Surachai LeksuwankunDivision of Academic Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.ORCID http://orcid.org/0000-0002-0854-3056
Thanakit PongpitakmethaComprehensive Headache and Orofacial Pain (CHOP) Service and Research Group, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.ORCID http://orcid.org/0000-0002-8338-4649
Akarin HiransuthikulComprehensive Headache and Orofacial Pain (CHOP) Service and Research Group, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Totsapol SurawattanawongChulalongkorn Comprehensive Epilepsy Center of Excellence (CCEC), King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand.ORCID http://orcid.org/0009-0004-7200-7524
Prakit AnukoolwittayaComprehensive Headache and Orofacial Pain (CHOP) Service and Research Group, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.ORCID http://orcid.org/0000-0001-9440-1996
Poonnakarn PanjasriprakarnDivision of Integrated Innovation and Digital Technologies, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand. poonnakarn.p@chula.ac.th.ORCID http://orcid.org/0000-0002-7711-3826

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMedical students often struggle with headache disorders, which are common but diagnostically challenging due to limited clinical exposure and feedback during early clinical training. This study evaluated the feasibility and effectiveness of an AI-powered simulated patient (AISP) chatbot in enhancing history-taking and clinical reasoning for three primary headache disorders-migraine, tension-type, and cluster headache-among medical students at a tertiary university hospital in Thailand.

methodsWe developed a simulated patient platform that provided automated, rubric-based feedback across three headache cases. Third-year medical students who had completed pre-clerkship requirements participated in a mixed-methods experimental study with a pre-post design. The study consisted of a 1-week self-study phase followed by a pre-test Objective Structured Clinical Examination (OSCE), a 1-week washout period, and a 1-week AISP practice phase followed by a post-test OSCE. Quantitative outcomes included changes in OSCE scores, Clinical Reasoning Indicator-History Taking (CRI-HT) scores, usability/satisfaction measured using the Chatbot Usability Questionnaire (CUQ). Focus group discussions (FGDs) explored participants' learning experiences, perceived system limitations, and recommendations for future implementation. Qualitative data were analyzed thematically.

resultsTwenty-five medical students participated. OSCE scores increased by 22.5 points (p < 0.001, Cohen's d = 1.93), and CRI-HT scores increased by 8.8 points (p < 0.001, Cohen's d = 1.81). CUQ findings indicated high usability. Students commonly cited the platform's intuitive interface and informative feedback, although some noted the chatbot's responses were overly robotic. FGDs further highlighted three themes: (1) convenient, structured practice support learning; (2) dialogue felt realistic but was limited for practicing communication skills; and (3) students recommended greater case variety and integration into the curriculum.

conclusionsAn AISP platform with automated, rubric-based feedback was feasible and associated with improvements in headache history-taking and clinical reasoning. It serves as a valuable complement to traditional teaching by enabling structured, repeated practice with timely feedback.

Indexed as

Clinical CompetenceEducation, Medical, UndergraduateMedical History TakingPatient SimulationAdultClinical ReasoningFeasibility StudiesFeedbackFemaleHumansMaleMigraine DisordersStudents, MedicalThailandAI simulated patientClinical reasoningDeliberate practiceHeadacheHistory-takingLarge language modelMedical educationOSCE

Identifiers

PMID42174548
PMCPMC13371515

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LicenceCC BY-NC-ND
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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.