Agentic education · Learning science

Actionable companion · Assessment & reasoning

Learning with agency: Rapidly Rising Preference among New-entry Medical Students for Using Generative Artificial Intelligence in Reflective Reports

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. Health-professions learning must be distinguished from K–12 instruction.

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

Abstract Introduction: As artificial intelligence (AI) continues to proliferate, it becomes imperative that medical students are not only instructed in the use of AI but also afforded regular opportunities to interact with it throughout their medical education. In 2023 and 2024, an art-based reflective reports assignment was implemented among new-entry students of the medical program at a university in Japan. The assignment required students to use generative AI within their submissions. The objective of this study was to assess the perceptions of these students regarding the use of generative AI in their reflective reports over the course of two academic years. Methods: First-year medical students were tasked with submitting an art-based reflective report about their experiences during the community-based inquiry learning session. The assignment was a component of their reflective competencies assessment. We conducted a survey of the 2023 and 2024 student cohorts and examined their perceptions of using generative AI using a five-point Likert scale. We compared the survey responses of the two cohorts using the t-test. Results: The participants exhibited a notably higher mean score for positive perceptions toward generative AI in 2024 compared with 2023 (3.8 versus 2.9 points; p < 0.01). In addition, the proportion of participants who expressed a preference for using generative AI was significantly higher in 2024 than in 2023 (32.0% versus 17.4%; p = 0.02). Conclusions: A substantial and rapid increase has been observed in the proportion of medical students indicating a preference for using generative AI. The joint adoption of generative AI by medical students and faculties signifies a significant and urgently required development in AI use within medical education. Keywords: artificial intelligence (AI), medical students, arts-based education, community-based inquiry learning, writing assignments

What kind of evidence is this?

Supporting AI learning science · Evidence synthesis

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 study setting was a community-based inquiry learning session for first-year medical students. During the two-month program, students learned about community medicine by visiting and observing nine healthcare facilities, such as hospitals, clinics, and nursing homes, in groups of ten students each.

Limitations and uncertainty · source passage

The present study is not without its limitations. First, it was conducted within a single course at a single medical school in Japan. Therefore, it is uncertain whether the current findings can be applied to other educational settings. However, there is a possibility that elements of our results may be applicable to other educational contexts that share similar teaching and learning environments. Second, this study was conducted in an educational setting that employed generative AI to develop students’ IT skills. However, the effectiveness of incorporating generative AI into the course was not evaluated, such as by comparing it with a course that did not utilize AI. A holistic program evaluation study is needed to overcome these limitations.

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.

  • 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

  1. 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.
  2. 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.
  3. 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.
  4. 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

Chihiro Kawakami; Osamu Nomura; Miyuki Takahashi; Ritsuki Takaha; Takayasu Ito; Hiroaki Ushikoshi; Takuya Saiki. Rapidly Rising Preference among New-entry Medical Students for Using Generative Artificial Intelligence in Reflective Reports. JMA Journal (2025-03-28). https://doi.org/10.31662/jmaj.2024-0390

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.