Evidence map›Paper›PMID 42438541›Full record

ArticleMedical science educator2026

Expanding the Roles of Medical Educators in the Generative Artificial Intelligence Era: A Qualitative Study.

Ikuo Shimizu, Hajime Kasai, Naoto Ozaki, Hiromu Nishiuchi, Kiyoshi Shikino, Nobuyuki Araki, Misaki Onodera, Yasuhiko Kimura, Tomoko Tsukamoto, Hiroshi Tajima and 2 more

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In one paragraph

Article in Medical science educator, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

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

12 authors.

Ikuo ShimizuDepartment of Medical Education, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chiba, 2608672 Japan.ORCID 0000-0002-6731-7104
Hajime KasaiDepartment of Medical Education, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chiba, 2608672 Japan.ORCID 0000-0002-6759-2026
Naoto OzakiDepartment of Medical Education, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chiba, 2608672 Japan.ORCID 0000-0001-8276-2647
Hiromu NishiuchiData Science Core, Chiba University, Chiba, Japan.ORCID 0000-0001-8515-2748
Kiyoshi ShikinoDepartment of Community-Oriented Medical Education, Graduate School of Medicine, Chiba University, Chiba, Japan.ORCID 0000-0002-3721-3443
Nobuyuki ArakiDepartment of Medical Education, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chiba, 2608672 Japan.ORCID 0000-0002-5703-3829
Misaki OnoderaDepartment of Medical Education, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chiba, 2608672 Japan.ORCID 0009-0002-0126-9143
Yasuhiko KimuraHealth Professional Development Center, Chiba University Hospital, Chiba, Japan.ORCID 0009-0009-6844-7347
Tomoko TsukamotoHealth Professional Development Center, Chiba University Hospital, Chiba, Japan.ORCID 0000-0002-1350-203X
Hiroshi TajimaDepartment of Medical Education, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chiba, 2608672 Japan.ORCID 0009-0004-2581-6193
Shoichi ItoDepartment of Medical Education, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chiba, 2608672 Japan.ORCID 0000-0003-3659-6152
Eiryo KawakamiDepartment of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan.ORCID 0000-0001-9955-4342

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid advancement of generative artificial intelligence (GAI) necessitates reconceptualizing medical educators' professional roles. While Harden's Six Roles of the Teacher have long defined educator identity, GAI integration may require expansion beyond this established framework. This study explored how medical educators perceive role evolution in the GAI era and identified emerging educator functions. A qualitative descriptive study was conducted during a faculty development program at a Japanese medical school in August 2025. Fifty-two faculty members discussed improving education using GAI. From 137 analyzed statements, content analysis applied Harden's framework deductively, while the SAMR model assessed transformation levels. Statements showing high transformation (Modification/Redefinition) underwent inductive analysis to identify new roles. Traditional roles dominated: resource developer (

Indexed as

Digital transformationGenerative artificial intelligenceMedical educationMedical educatorQualitative research

Identifiers

PMID42438541
PMCPMC13356178

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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.