Agentic education · Learning science

Actionable companion · Motivation & engagement

Learning with agency: Piloting an AI Introduction Program for Incoming Medical Students: A Novel Approach to Medical Education

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:

Background Artificial intelligence (AI) is increasingly recognized for its potential to enhance personalized learning and clinical decision-making in medical education. However, limited integration of AI into medical curricula may leave students feeling underprepared for AI-driven healthcare. This study piloted an AI education program for first-year medical students, assessing post-session perceptions of knowledge, confidence, and perspectives regarding AI in medicine. Methods An interactive AI session was conducted for 241 first-year medical students at University of Minnesota Medical School. The session included components of lecture, live demonstrations, and exercises with AI tools such as ChatGPT, Copilot, and Gemini, where students applied AI to various educational and clinical scenarios. Discussions included potential benefits, harms, and limitations including ethical implications, bias and privacy concerns. A post-session survey captured students’ understanding, perceived utility, and confidence regarding AI use in education and in medicine. Results Of 241 medical students, 180 attended the session, and 92 of attendees (51%) completed the post-session survey. Findings showed that a majority recognized AI’s value for enhancing study efficiency and diagnostic support, with over 50% indicating increased confidence in incorporating AI tools into their education and possible future practice. Concerns emerged around AI reliability, potential over-reliance, and ethical issues, especially regarding data privacy and bias. Conclusions This pilot study provides preliminary evidence supporting the early introduction of AI concepts in medical education. Students’ ethical concerns highlight the need for balanced curricula that combine foundational AI literacy with critical ethical understanding. The study’s descriptive, post-session design limits causal inference; future work should incorporate pre- and post-assessments and longitudinal follow-up to evaluate the sustained impact of AI education on clinical competencies and ethical awareness. Practitioner's Points 1. Early and Structured Artificial Intelligence Education is Crucial. With the increasing use of Artificial Intelligence (AI) tools in modern day practice, it is crucial learners are up-to-date with modern practices rather than behind after exiting training. Our pilot program's findings suggest that introducing AI concepts to medical students as early as their first-year orientation may be highly effective. An initial, mandatory session resulted in high post-session students' confidence and perception of AI tools, creating an equitable foundation for all learners, regardless of their prior exposure. This early introduction can help normalize AI as an integral part of modern medicine and prepares students to engage with it throughout their training. 2. Balance Technical "How-To" with Critical and Ethical Discussions. Our post session survey results suggest the duality of learner perception of AI tools. Students are enthusiastic about AI's potential to improve study efficiency and provide diagnostic support, while having significant, valid concerns. The most prominent issues are the accuracy and reliability of AI-generated information, the risk of over-reliance that could degrade clinical skills, and ethical issues like data privacy and algorithmic bias. An effective curriculum must directly address these concerns, teaching students not only how to properly use AI tools but also how to critically evaluate their outputs and use them responsibly. 3. Plan for Longitudinal Integration, Not a One-Off Session. While a single introductory session is a valuable starting point, our study's future directions highlight the need for a longitudinal and integrated curriculum. AI education should not be isolated to an orientation week. To be truly effective, it must be woven into subsequent courses, such as clinical skills and clerkships, with dedicated sessions on clinical applications (e.g., differential diagnosis, documentation) and deeper ethical considerations. This approach reinforces learning and demonstrates AI's practical role in evolving clinical practice.

What kind of evidence is this?

Supporting AI learning science · Survey / observational study

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

An interactive educational session was developed to introduce students to selected AI tools and their practical applications in medical education. The session was designed for first-year medical students at the University of Minnesota Medical School, aiming to enhance their understanding of AI technologies, their capabilities, and limitations in a variety of scenarios (Supplementary 1). The content included a foundational introduction to AI in medicine with discussions of benefits, harms, and limitations of AI tools such as ChatGPT (OpenAI), Co-Pilot (Microsoft), and Gemini (Google), and discussions on their potential role in improving medical knowledge, study efficacy, diagnostic accuracy, patient communication, and decision-making in clinical practice. The session was structured with a mix of lecture (55 min), live demonstration (10 min), hands-on activities (30 min) where students engaged directly with AI tools through a variety of prompts, and a large group discussion (5 min).

Limitations and uncertainty · source passage

This study has several important limitations that should be considered when interpreting its findings. First and most significantly, no pre-session assessment was administered, meaning the study can only report post-session perceptions rather than measured changes or improvements from baseline. All claims regarding student confidence, understanding, and attitudes should be interpreted as self-reported cross-sectional perceptions captured at a single time point following the intervention, not as evidence of demonstrated change attributable to the session. Future iterations should incorporate matched pre- and post-session assessments to enable comparison and more robust evaluation of educational impact.

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.

  • 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

  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

Kenny T. L. Ta; Ayush Patel; Nathan Keller; Shivansh R Pandey; Abhinab Kc; Cade Arries. Piloting an AI Introduction Program for Incoming Medical Students: A Novel Approach to Medical Education. Medical Science Educator (2026-06-06). https://doi.org/10.1007/s40670-026-02782-9

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.