Actionable companion · Motivation & engagement
Learning with agency: Revolutionizing medical physiology education: a narrative review of the transition from traditional classrooms to artificial intelligence
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): Higher education, Adult & professional learning. Health-professions learning must be distinguished from K–12 instruction.
Original authors’ abstract, reproduced under the linked source license:
Abstract Medical physiology is a foundational component of medical education, offering vital details about normal human function and a deep understanding of disease processes. Traditionally taught through lectures, textbooks, and laboratory demonstrations, physiology education has progressively evolved in response to advances in technology and changing learner needs. This review examines the historical development of physiology teaching and synthesizes contemporary innovations that are transforming the educational landscape. Emphasis is placed on simulation-based education, hybrid and virtual classrooms, immersive technologies such as virtual and augmented reality, gamification, and student-centered learning strategies, all of which promote active engagement, conceptual understanding, and integration of physiological knowledge into clinical contexts. The review also explores the emerging role of artificial intelligence in physiology education, including personalized learning pathways, adaptive assessments, and real-time physiological simulations, with relevance for resource-limited settings. Innovations in assessment, competency-based medical education, interdisciplinary integration, and clinically oriented physiology teaching are highlighted as key contributors to improved learning outcomes. Additionally, the review addresses inclusivity and accessibility through the use of open educational resources and digital platforms that reduce disparities in educational access. Despite these advances, several challenges remain, including infrastructure limitations, faculty training gaps, institutional resistance, and ethical considerations surrounding the use of advanced technologies. Furthermore, the review outlines future directions focused on holistic learning environments, deeper clinical integration, and a balanced adoption of technology alongside traditional teaching methods. Overall, this narrative review highlights the importance of adopting a learner-centered, adaptable, and inclusive approach to physiology education that equips medical students for complex clinical practice and lifelong learning. Keywords: artificial intelligence, gamification, medical education, personalized learning, simulation
What kind of evidence is this?
Supporting AI learning science · Evidence synthesis
Stage: Higher education; Adult & professional learning. 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.
Limitations and uncertainty · source passage
This narrative review aims to explore the evolution of physiology teaching and to acknowledge the recent advances in the field. It focuses on innovative techniques and methodologies in teaching and learning. Additionally, this review examines recent trends and emerging challenges that hinder the adoption of these new strategies, as well as possible solutions. The recent recommendations for enhanced knowledge comprehension and active learning are also included in this review. The scope of this review encompasses hybrid classes, simulation-based education, Virtual Reality (VR) applications in teaching physiology, gamification, Artificial Intelligence AI, challenges, and future directions. To the best of the author’s knowledge, currently, there is limited literature that comprehensively reviews the strategies used and recent advances in physiology teaching, along with their limitations and solutions for better global adoption.
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.
- 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
- 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. 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.
- 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. 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
Adham Al-Rahbi; Harshita Agarwal; Srinivasa Rao Sirasanagandla; Mohammad Orooj Azmi; Qusay Al-Badi; Aditi Kolekar; Srinivas Rachoori; Raju Suresh Kumar; Hussein Sakr; Mickael Antoine Joseph. Revolutionizing medical physiology education: a narrative review of the transition from traditional classrooms to artificial intelligence. Frontiers in Medicine (2026-05-26). https://doi.org/10.3389/fmed.2026.1781197
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.