Actionable companion · Assessment & reasoning
Learning with agency: AI-informed healthcare pedagogy: a review of the literature and application to pedagogical practice informed by case studies from dental 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.
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. Health-professions learning must be distinguished from K–12 instruction.
Original authors’ abstract, reproduced under the linked source license:
Abstract Artificial intelligence (AI) applications in healthcare continue to grow at a rapid pace. Healthcare education is struggling to effectively integrate the manifold applications and opportunities presented by AI to education. Concerns and fears around negative impacts on student learning and potential for misuse alongside a paucity of guidance has led to a sluggish response across much of healthcare education. This paper offers a short review of the applications of AI in healthcare including clinical dentistry and makes suggestions for bridging the gap between education and practice so that educators can better prepare students for their professional careers. This paper argues that open, reflective classroom discussions around ethical concerns and climate impacts of AI are as important as the positive clinical applications to endow students with the knowledge to make decisions that align with social justice and social accountability. Two widely used assessments in dental and wider healthcare education − reflective assessments and evidence-based practice projects − are explored as case studies to develop an updated, AI-informed approach to teaching. Drawing on pedagogical literature, the author offers suggestions for educators to support thoughtful and ethical integration of AI within a student's learning repertoire so that AI becomes supplementary to, and not a substitute for, learning. Key points Artificial intelligence (AI) is being increasingly adopted in clinical practice and applied to support patient communication, diagnostics and treatment. Healthcare education has been slower to integrate AI into teaching and learning for students. Reflective assessments and evidence-based practice projects build critical thinking and self-awareness. Suggestions to integrate AI into these are made.
What kind of evidence is this?
Supporting AI learning science · Design requires full-text appraisal
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
Integrating AI into EBP projects may facilitate learning for students and have positive outcomes through personalising learning,72 and help students more efficiently locate relevant research articles. Depending on prompts used, AI programs can produce effective summaries of articles and highlight methodological details (e.g., study design, outcomes, limitations), to help students become familiar with structure and content and scaffold learning73 before undertaking deep appraisal. Spending less time retrieving information allows more time for appraisal and synthesis of data.
Limitations and uncertainty · source passage
Below, two common assessments in healthcare education, including dentistry, will be explored as case studies with a view towards creating an AI-informed approach that has application for all professional healthcare educators. Suggestions are made for where and how to integrate AI to foster critical reflection and critical evaluation. The first is reflective assessments where students could convincingly use AI tools to generate rapid reflective responses on their behalf. Using AI to generate reflections would negate the methodical and deliberately slow nature of personal reflection which is fundamental to the cognitive processing that can lead to change. Guidance is provided on ways to support and deepen student reflection even if AI is used. The second case study explores the strengths and limitations of AI within evidence-based projects designed to encourage students to critically evaluate research.
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
- 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. 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.
- 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. 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
Nilufar Ahmed. AI-informed healthcare pedagogy: a review of the literature and application to pedagogical practice informed by case studies from dental education. British Dental Journal (2026-09-11). https://doi.org/10.1038/s41415-026-9954-6
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.