Motivational interviewing skills practice enhanced with artificial intelligence: ReadMI
Open accessAbstract
Background: Finding time in the medical curriculum to focus on motivational interviewing (MI) training is a challenge in many medical schools. We developed a software-based training tool, “Real-time Assessment of Dialogue in Motivational Interviewing” (ReadMI), that aims to advance the skill acquisition of medical students as they learn the MI approach. This human-artificial intelligence teaming may help reduce the cognitive load on a training facilitator. Methods: During their Family Medicine clerkship, 125 third-year medical students were scheduled in pairs to participate in a 90-minute MI training session, with each student doing two role-plays as the physician. Intervention group students received both facilitator feedback and ReadMI metrics after their first role-play, while control group students received only facilitator feedback. Results: While students in both conditions improved their MI approach from the first to the second role-play, those in the intervention condition used significantly more open-ended questions, fewer closed-ended questions, and had a higher ratio of open to closed questions. Conclusion: MI skills practice can be gained with a relatively small investment of student time, and artificial intelligence can be utilized both for the measurement of MI skill acquisition and as an instructional aid.
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Providing effective feedback to medical students can be challenging.
“Providing effective feedback to medical students can be challenging [144–147].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [144]
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- Authors
- Hershberger, P. J., Pei, Y., Bricker, D. A., Crawford, T. N., Shivakumar, A., Castle, A., Conway, K., Medaramitta, R., Rechtin, M., & Wilson, J. F.
- Affiliations
- Wright State University (US)
- Kennesaw State University (US)
- Corresponding author
- Hershberger, Paul J.
- ORCID
- Venue
- BMC Medical Education
- Published
- 2024-03-05
- Volume
- 24
- Issue
- 1
- Pages
- 237-237
- ISSN
- 1472-6920 (ISSN-L)
- PMC
- PMC10916112
- OpenAlex
- W4392457305
- Cited by
- 18
- References
- 34
- Citations by year
- 2026: 5 · 2025: 12 · 2024: 1
- FWCI
- 7.42
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Medical Education (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- CORE Scholar (Wright State University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Agency for Healthcare Research and Quality (R21HS026548)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- motivational interviewing, artificial intelligence, medical students, role play, open-ended questions, closed-ended questions, skill acquisition, family medicine clerkship, human-AI teaming, medical education software
- MeSH terms
- Artificial Intelligence, Curriculum, Humans, Software, Students, Medical, Motivational Interviewing
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