Agentic education · Learning science

Actionable companion · Feedback & scaffolding

Learning with agency: Integrating AI into undergraduate medical education: an exploration of learner-centered approaches through AI

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 Advances in artificial intelligence (AI) offer promising opportunities to enrich medical education by supporting self-directed learning, providing personalized feedback, and simulating clinical and scientific reasoning. Although AI use has expanded in graduate medical education for clinical decision training and administrative support, its effectiveness in undergraduate medical education remains insufficiently established. Empirical data examining how AI instruction influences academic performance, knowledge consolidation, engagement, and critical thinking are limited. This study evaluated whether incorporating AI-patient-based simulations into classroom activities enhances learning outcomes and student experience within an integrated osteopathic medical school curriculum. Methods Using a mixed-methods survey design, quantitative and qualitative data were collected and analyzed to examine students’ perceptions of faculty-developed AI-patient-based simulations integrated into classroom instruction. Results Students reported that AI-patient-based simulations supported engagement, reinforced learning, and facilitated application of clinical reasoning concepts. Qualitative findings suggested that the simulations promoted active participation and encouraged self-directed learning while highlighting the importance of faculty guidance in interpreting AI-generated content. Discussion Overall, findings suggest that AI-patient-based simulations can provide meaningful support to medical student learning when thoughtfully integrated into the curriculum and accompanied by explicit instruction regarding limitations, ethical considerations, and responsible AI use. Further research is needed to determine the long-term effects of AI-assisted instruction on academic performance and critical thinking development. Keywords: artificial intelligence, integrative curriculum, learner-centered, patient-based simulation, undergraduate medical education

What kind of evidence is this?

Supporting AI learning science · Qualitative / mixed methods

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

Using a mixed-methods survey design, we collected and analyzed quantitative and qualitative data to examine students’ perceptions of integrating AI tools in a classroom setting. This allowed for an assessment of students’ overall impressions, as well as perceived benefits and challenges related to AI-patient-based simulations in class.

Limitations and uncertainty · source passage

To our best knowledge, this is one of the first papers that discusses the development and utilization of custom faculty-built, AI-patient-based simulation in undergraduate medical education. Utilizing a mixed-method study design, we gathered valuable insights from student perspectives that reveal the use of these simulations have meaningful impact on learning. However, we also acknowledge certain limitations within our study. First, the research was conducted exclusively in one university. Our goal was to build these simulations to fit into our unique, integrated curriculum although this may have impacted the generalization of our findings. Additionally, the small sample size may have introduced selection bias as those students that opted in to the study have more experience, motivation, or interested in learning AI, skewing the self-reported perceptions in a positive manner.

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.
  • Use plan–attempt–check–reflect prompts and compare confidence with actual performance. Keep learner choice and reflection visible.
  • 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

Blaine A Jacobs; Leslie Cano; Jessica M Bradley. Integrating AI into undergraduate medical education: an exploration of learner-centered approaches through AI. Frontiers in Medicine (2026-05-22). https://doi.org/10.3389/fmed.2026.1824069

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.