Agentic education · Learning science

Actionable companion · Self-regulation & metacognition

Learning with agency: The impact of generative AI on health professional education: A systematic review in the context of student learning

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): Age / stage not established. Verify the participant population and task before transferring the findings.

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

Abstract Background Generative Artificial Intelligence (GenAI) is increasingly integrated into health professions education (HPE), offering new opportunities for student learning. However, current research lacks a comprehensive understanding of how HPE students actually use GenAI in practice. Laurillard's Conversational Framework outlines six learning types—acquisition, inquiry, practice, production, discussion and collaboration—commonly used to categorise learning activities supported by conventional and digital technologies. Gaining insight into how GenAI aligns with these six learning types could assist HPE academics in integrating it more thoughtfully and effectively into teaching and learning. Purpose This systematic review investigates how HPE students utilise GenAI and examines how these uses align with Laurillard's six learning types compared to conventional and digital technologies. Material and Methods A systematic review searching five major databases—ERIC, Education Database, Ovid Medline, Ovid Embase and Scopus including articles on HPE students' use of GenAI until 15th September 2024. Studies were included if they were conducted within formal HPE training programs in HPE and specifically mentioned how students interact with GenAI. Data were mapped to the six learning modes of the Laurillard's Framework. Study quality was assessed using the Medical Education Research Study Quality Instrument (MERSQI). Results Thirty‐three studies met inclusion criteria. GenAI supported learning most frequently in practice (73%), inquiry (70%), production (67%) and acquisition (55%). These studies highlight GenAI's varied educational applications, from clarifying complex concepts to simulating clinical scenarios and generating practice materials. Discussion and collaboration were less represented (12% each), suggesting a shift toward more individualised learning with GenAI. The findings highlight benefits such as efficiency and accessibility, alongside concerns about critical thinking, academic integrity and reduced peer interaction. Conclusion This review has provided insights into HPE students' learning aligned with Laurillard's existing six learning types. Although GenAI supports personalised and self‐directed learning, its role in collaborative modes is under‐explored. Short abstract Using Laurillard's six types of learning, Pham et al. offer insights into how GenAI is already influencing health professional education, highlighting that its role in collaborative learning is under‐explored.

What kind of evidence is this?

Supporting AI learning science · Evidence synthesis

Stage: Age / stage not established. 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

This systematic literature review was conducted in accordance with the 2020 Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines. 27 The protocol of the review is registered (PROSPERO CRD42024543726). This systematic review included original peer‐reviewed research papers that focussed on the application of GenAI technologies within formal HPE training programs within higher education. Grey literature was not included, as the focus was on peer‐reviewed studies to ensure reliability and consistency in the findings. Health Professionals were defined as those listed in the World Health Organisation classifying health workers document 28 but refined based on the teams' perspectives of which professions had tertiary courses leading to an award of a university degree. No starting year limit was set, all studies published on or before 15th September 2024 were included. Studies were only included if they investigated student use of GenAI.

Limitations and uncertainty · source passage

Like all systematic reviews, this review is subject to publication bias, particularly given the rapid release of GenAI studies. Limiting the search to English may exclude valuable international research; however, previous studies suggest minimal impact on overall conclusions. 79 , 80 Perceptions of GenAI users as less capable may influence self‐reported data and introduce bias in findings. 51 , 65 Additionally, known gender, racial and other biases in GenAI tools warrant further study into their effects on student learning. 81 , 82 , 83 Despite these limitations, we followed a rigorous and transparent methodology. The protocol was registered with PROSPERO, ensuring transparency and efforts to reduce bias. 84 We also adhered to PRISMA 2020 guidelines to support accurate and complete reporting. 85 A comprehensive search across five databases and synthesis using Laurillard's learning modes provide a strong foundation for understanding GenAI use in HPE and guiding future work.

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 plan–attempt–check–reflect prompts and compare confidence with actual performance. Keep learner choice and reflection visible.
  • 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.
  • Co-design accommodations with learners and specialists. Check access and error burden across groups without inferring subgroup effectiveness from small or unreported samples.

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. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. 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. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. 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

Thai Duong Pham; Nilushi Karunaratne; Betty Exintaris; Danny Liu; Travis Lay; Elizabeth Yuriev; Angelina Lim. The impact of generative AI on health professional education: A systematic review in the context of student learning. Medical Education (2025-06-18). https://doi.org/10.1111/medu.15746

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.