Actionable companion · Feedback & scaffolding

Learning with agency: Motivational interviewing skills practice enhanced with artificial intelligence: ReadMI

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

Abstract 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. Keywords: Motivational interviewing, Artificial intelligence, Medical education, Patient engagement, Feedback

What kind of evidence is this?

Supporting AI learning science · Comparative / pre-post study reported

Stage: Higher education. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

The first 20 months of our medical school curriculum constitute the pre-clinical curriculum, and students then begin their required clinical rotations. During this “third year,” medical students (N = 125) doing their required Family Medicine Clerkship participated in the study. There were six clerkship rotations during the academic year (June 2020– March 2021), with each rotation including approximately 21 students. The Wright State University Institutional Review Board (IRB) determined that the study was exempt from human subjects review as it involved research on the effectiveness of our comparison of instructional techniques (IRB #06719, June 18, 2019). Before the educational activity, the informed consent process consisted of providing medical students with a clear description of the research being conducted as part of the evaluation of the educational experience.

Limitations and uncertainty · source passage

A significant limitation of our study was the measured difference in MI skills between the intervention and control groups at baseline. This difference may have been due to the group randomization process and the timing of when students in each rotation participated. Students in the first two rotations (both randomized to the intervention condition) were closer in time to their most recent exposure to MI during the preparatory “bootcamp” prior to students beginning their clerkships, potentially contributing to the intervention groups having better baseline metrics compared to the control groups. The fact that we utilized a group rather than individual randomization process was itself a limitation of our study, a choice that was made to coincide with how motivational training is done during each clerkship rotation. Another limitation is that reliability and validity data are not available for the Motivational Interviewing Knowledge Test used in our study. Additionally, it is important to note that the intervention group received two sources of feedback after their first role play (i.e., facilitator and ReadMI metrics) whereas the control group only had one source of feedback (i.e., facilitator). The possibility cannot be ruled out that additional feedback alone is responsible for better intervention group performance rather than something specific to the ReadMI metrics. Our study sample included all third-year medical students. We did not perform a power calculation prior to the study. A limitation is that our study may not have been well-powered to assess all effects, particularly the interactions in the LMM. Finally, the fact that we did not do any formal analyses of the qualitative data comparing the intervention and control groups is a limitation of this study.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.
Limit simultaneous instructions and unnecessary dialogue. Compare task performance and a suitable mental-effort measure; easier-feeling work is not automatically better learning.
Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.
Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

A 90-day district pilot

Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Paul J Hershberger; Yong Pei; Dean A Bricker; Timothy N Crawford; Ashutosh Shivakumar; Angie Castle; Katharine Conway; Raveendra Medaramitta; Maria Rechtin; Josephine F Wilson. Motivational interviewing skills practice enhanced with artificial intelligence: ReadMI. BMC Medical Education (2024-03-05). https://doi.org/10.1186/s12909-024-05217-4

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.