Dataset title

Raw qualitative dataset for the article
“Expanding the Roles of Medical Educators in the Generative Artificial Intelligence Era: A Qualitative Study”

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Related publication

Shimizu I, Kasai H, Ozaki N, et al.
Expanding the Roles of Medical Educators in the Generative Artificial Intelligence Era: A Qualitative Study.
(under review / accepted manuscript)

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Corresponding author

Ikuo Shimizu, MD, MHPE, PhD
Department of Medical Education,
Graduate School of Medicine, Chiba University, Chiba, Japan
E-mail: qingshuiyufu@gmail.com

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Data collection period

August 2025.

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Data description

This dataset contains anonymized qualitative data collected during a faculty development program at a Japanese medical school.

During group discussions, faculty members were asked the following question:

“What can medical educators do to improve education using generative artificial intelligence (GAI)?”

Participants discussed in small groups and recorded their ideas on sticky notes.
All notes were collected, anonymized, and transcribed into textual data.

A total of 141 textual statements were collected.
Four statements unrelated to the research question were excluded.
The remaining 137 statements constitute the analytical dataset used in the study.

All original statements were written in Japanese.
Coding and category generation were conducted in Japanese to preserve contextual and cultural nuances.

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File structure

The dataset is provided as a single excel file:
	•	GAI-teacher_upload.xlsx

Each row corresponds to one statement recorded during the group discussions.

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Variable (column) description

In the excel file, the columns represent the following information:
	•	statement_id
A unique identifier assigned to each statement.
	•	original_text_ja
The original statement written in Japanese.
	•	harden_role
The category assigned based on Harden’s Six Roles of the Teacher
(information provider, role model, facilitator, assessor, planner, resource developer).
	•	samr_level
The level of digital transformation assigned using the SAMR model
(Substitution, Augmentation, Modification, or Redefinition).
	•	emergent_role
The newly identified educator role derived from inductive analysis for statements classified as Modification or Redefinition.
Possible values are:
coordinator, content generator, analyst, lifelong learner, and fact checker.
For statements classified as Substitution or Augmentation, this field is left blank.

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Data processing and analysis

All statements were independently coded by three researchers.
Disagreements were resolved through consensus meetings.
Coding decisions and revisions were documented in a shared codebook.

Statements were first coded deductively according to Harden’s Six Roles of the Teacher and then evaluated using the SAMR model.
Statements classified as Modification or Redefinition were further analyzed inductively to identify emergent educator roles.

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Ethical considerations

The study was approved by the Ethics Committee of the Graduate School of Medicine, Chiba University (Approval No. 3425).

All data in this dataset are fully anonymized.
No personal identifiers of participants are included.

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Usage notes

This dataset is intended for secondary analysis of medical educators’ perceptions of professional roles in the era of generative artificial intelligence.
Users should acknowledge the original publication when using this dataset for research or educational purposes.