PCW 13 - How Students Use AI and How Educators Can Leverage This to Enhance Learning: Practical Learner-Centered AI Skills for the Classroom
https://amee.org/events/amee-2026/programme/amee-2026-pre-conference-activities/
Date: 23 August 2026, Sunday
Time: 9:30 AM – 12:30 PM
Venue: Room 2.15, Level 2, Austria Center, Vienna
Fee: €104 + VAT
https://amee.org/events/amee-2026/programme/amee-2026-pre-conference-activities/
https://amee.org/events/amee-2026/programme/overview/ (for floor plans)
Presenters: Stella Goeschl1, Ken Masters2, Peter de Jong3, Poh-Sun Goh4, Kristina Pavloski5, Rakesh Patel6
1Imperial College London, London, UK. 2Sultan Qaboos University, Muscat, Oman. 3LeidenUniversity Medical Center, Leiden, Netherlands. 4National University of Singapore, Singapore, Singapore. 5European Medical Students’ Association (EMSA), Brussels, Belgium. 6St Mary’s University, Twickenham, London, UK
Background
Artificial Intelligence (AI) is rapidly transforming health professions education (HPE), from adaptive learning to AI-assisted assessment and curricular design. Educators are increasingly expected to understand and integrate these tools, yet many feel unprepared. Meanwhile, students experiment widely with generative AI, often without formal guidance.
This pre-conference workshop responds to the growing demand for practical AI training that aligns with learner needs. Led by members of the AMEE Technology-Enhanced Learning (TEL) Committee, it offers an introductory hands-on approach to integrating AI into teaching and assessment, covering AI tools, prompt engineering, and applications in learning and evaluation.
A unique feature is an interactive student Q&A panel, where health professions students share candid experiences with AI in learning and assessment. Participants will engage directly with students to co-create solutions, bridging perspectives and identifying practical opportunities and challenges.
The goal is to equip educators with a practical toolkit to use AI effectively and foster meaningful, learner-centered experiences.
Who Should Participate
Educators at all career stages seeking to apply AI in teaching or curriculum design; faculty developers and leaders integrating AI literacy institutionally; researchers and innovators in technology-enhanced learning; students or young professionals interested in shaping AI’s role in education.
Structure of Workshop
Introduction: Framing AI’s role in education in 2026 and its potential to enhance learner-centered design. (15 min)
Practical skills: Hands-on work with AI tools, prompt engineering, classroom or assessment applications. Several rounds of short demonstrations and guided practice. (60 min)
Discussion: Exploring broader applications, sharing cases, addressing institutional barriers. (15 min)
Break.
Student panel: Three HPE students share real-world AI use, followed by an interactive educator Q&A to discuss needs, opportunities, and concerns. (30 min)
Small-group work: Mixed student-educator teams to co-create practical, inclusive solutions. (30 min)
Reflection: Discussion of global perspectives. (15 min)
Debriefing & wrap-up: Key takeaways, listing actionable steps. (15 min)
Intended Outcomes
By the end of this workshop, participants will be able to:
Identify practical strategies for integrating learner-centered AI tools into teaching and assessment.
Compare student and educator perspectives and co-develop feasible solutions.
Take away concrete, context-specific action points for their own teaching practice and institutions.
Theme or Track
AI/Technology Enhanced Learning (TEL)
Phase of Education
Undergraduate and Graduate
Level of Workshop
Introductory
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Framing AI’s Role in Medical Education in 2026: Enhancing Learner-Centred Design
Poh-Sun Goh with the assistance of Microsoft CoPilot for background research including online references
By 2026, artificial intelligence (AI) has evolved from being a novel educational technology to becoming a foundational component of medical education. Rather than replacing educators, AI is increasingly viewed as a tool that augments teaching, enhances personalized learning, and supports competency-based education. The central challenge for medical schools is no longer whether to adopt AI, but how to integrate it responsibly while preserving the humanistic, ethical, and relational aspects of medicine.
Ahsan Z. (2025). Integrating artificial intelligence into medical education: a narrative systematic review of current applications, challenges, and future directions. BMC medical education, 25(1), 1187. https://doi.org/10.1186/s12909-025-07744-0
The Emerging Role of AI in Medical Education
AI is influencing medical education across several domains:
1. Personalized and Adaptive Learning
AI-powered platforms can analyze learners' performance, identify knowledge gaps, and provide individualized learning pathways. This allows students to progress at different rates, receive targeted feedback, and focus on areas requiring improvement. Such adaptive learning aligns closely with learner-centred educational principles, where instruction is tailored to individual needs rather than delivered through a uniform curriculum.
Ahsan Z. (2025). Integrating artificial intelligence into medical education: a narrative systematic review of current applications, challenges, and future directions. BMC medical education, 25(1), 1187. https://doi.org/10.1186/s12909-025-07744-0
Examples include:
AI tutors that provide real-time explanations and coaching.
Automated generation of clinical cases at varying levels of complexity.
Personalized revision plans based on assessment data.
Ahsan Z. (2025). Integrating artificial intelligence into medical education: a narrative systematic review of current applications, challenges, and future directions. BMC medical education, 25(1), 1187. https://doi.org/10.1186/s12909-025-07744-0
https://healthtopic.org/topics/ai-in-health-professions-education-assessment/
2. Clinical Reasoning and Decision-Making Support
Large language models (LLMs) and AI-enabled clinical simulators can expose learners to diverse patient scenarios, helping them develop diagnostic reasoning skills in safe environments. AI can generate virtual patients whose histories evolve dynamically based on learner decisions, creating more authentic learning experiences than static case studies.
Ahsan Z. (2025). Integrating artificial intelligence into medical education: a narrative systematic review of current applications, challenges, and future directions. BMC medical education, 25(1), 1187. https://doi.org/10.1186/s12909-025-07744-0
However, educators must teach learners to critically evaluate AI outputs and avoid over-reliance on algorithmic recommendations. AI should support, not replace, clinical judgment.
AI COMPETENCIES FOR MEDICAL EDUCATORS
Compiled by the CGEA Faculty Development SIG AI in Medical Education Workgroup
May 2025
3. Assessment and Feedback
AI is increasingly used to enhance formative assessment through:
Automated feedback on written work.
Analysis of clinical reasoning processes.
Tracking competency progression.
Identification of struggling learners.
https://healthtopic.org/topics/ai-in-health-professions-education-assessment/
Ahsan Z. (2025). Integrating artificial intelligence into medical education: a narrative systematic review of current applications, challenges, and future directions. BMC medical education, 25(1), 1187. https://doi.org/10.1186/s12909-025-07744-0
The focus shifts from episodic examinations toward continuous assessment and coaching, supporting competency-based medical education (CBME).
https://healthtopic.org/topics/ai-in-health-professions-education-assessment/
Foundational Competencies for Undergraduate Medical Education (AAMC)
https://engage.aamc.org/UME-Competencies-AAMC-ACGME-AACOM
AI as a Driver of Learner-Centred Design
The most significant educational contribution of AI may be its ability to advance learner-centred design.
From Teacher-Centred to Learner-Centred Learning
Traditional medical education has often been curriculum-driven and teacher-directed. AI enables a paradigm shift towards:
Self-directed learning
Personalized learning trajectories
Just-in-time learning
Continuous formative feedback
Reflective practice
Ahsan Z. (2025). Integrating artificial intelligence into medical education: a narrative systematic review of current applications, challenges, and future directions. BMC medical education, 25(1), 1187. https://doi.org/10.1186/s12909-025-07744-0
Instead of asking, "What should we teach?", educators increasingly ask, "What does this learner need next?"
Co-Creation of Learning
Emerging educational frameworks describe AI as a partner in educational co-creation. Learners can use AI to:
Generate learning resources.
Create clinical scenarios.
Develop research questions.
Practice communication skills.
Reflect on clinical experiences.
Suliman, S., Kassab, S. E., Iqbal, M. Z., Al-Bualy, R., Asoodar, M., Susilo, A. P., Aymee De Mortier, C., Bousse, M., Hancock, N. J., Mathew, T., Sriranga, J., & Konings, K. D. (2026). The role of artificial intelligence in co-creation of health professions education: Integrating innovation into collaboration. Medical Teacher, 48(7), 1101-1112. https://doi.org/10.1080/0142159X.2025.2581163
https://cris.maastrichtuniversity.nl/ws/portalfiles/portal/291733575/Asoodar-2025-The-role-of-artificial-intelligence.pdf
Meeting the Moment: AI Policy and Recommendations for Advancing AI in Academia and Medical Education by Anne L. Farmakidis, MPS
https://www.iamse.org/wp-content/uploads/2025/06/IAMSE-March-2025-AI-webinar.FARMAKIDIS.pdf
This transforms students from passive recipients of information into active participants in knowledge construction.
Supporting Lifelong Learning
As medical knowledge continues to expand rapidly, physicians must continuously update their competencies. AI can support lifelong learning through:
Personalized continuing professional development.
Evidence summarization.
Identification of practice gaps.
Adaptive learning recommendations.
https://www.aamc.org/about-us/medical-education/cbme/ai-competencies
Key Challenges
Despite its promise, AI integration presents several important challenges.
Ethical and Professional Considerations
Medical learners must understand:
Algorithmic bias
Transparency and explainability
Data governance
Privacy and confidentiality
Accountability for AI-supported decisions
Weidener, L., & Fischer, M. (2023). Teaching AI Ethics in Medical Education: A Scoping Review of Current Literature and Practices. Perspectives on medical education, 12(1), 399–410. https://doi.org/10.5334/pme.954
https://www.who.int/teams/digital-health-and-innovation/harnessing-artificial-intelligence-for-health
AI literacy should therefore become a core competency in medical curricula.
Preservation of Humanism
Medicine remains fundamentally relational. Over-reliance on AI may weaken empathy, professional identity formation, and teacher-student relationships. Educators must deliberately preserve opportunities for mentorship, reflection, and human interaction.
Hou Z, Chen J and Guo H (2026) From “teaching by word and deed” to “intelligent mentorship”: ethical reconsiderations of AI-enabled medical education — lessons from China. Front. Med. 12:1754139. doi: 10.3389/fmed.2025.1754139
https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1754139/full
Barry, B.A., Sharp, R.R. & McGowan, M.L. Teaching the bioethics of information technologies and artificial intelligence in healthcare: Case-based learning for identifying and addressing ethical issues. International Journal of Ethics Education 10, 251–264 (2025). https://doi.org/10.1007/s40889-025-00218-3 or https://link.springer.com/article/10.1007/s40889-025-00218-3
Faculty Development
Many faculty members require training to effectively integrate AI into teaching and assessment. Future educators will need competencies in:
Prompt engineering
AI evaluation
Educational design
Ethical AI use
Data literacy
AI COMPETENCIES FOR MEDICAL EDUCATORS
Compiled by the CGEA Faculty Development SIG AI in Medical Education Workgroup
May 2025
https://www.aamc.org/media/86881/download?attachment
AI COMPETENCIES FOR MEDICAL EDUCATORS Compiled by the CGEA Faculty Development SIG AI in Medical Education Workgroup May 2025
Strategic Vision for 2026
A useful framing for 2026 is that AI should function as:
A learning partner, not a replacement teacher.
The educator's role evolves from information provider to:
Learning designer
Coach and mentor
Facilitator of reflection
Professional role model
Ethical guide
Meeting the Moment: AI Policy and Recommendations for Advancing AI in Academia and Medical Education by Anne L. Farmakidis, MPS
https://www.iamse.org/wp-content/uploads/2025/06/IAMSE-March-2025-AI-webinar.FARMAKIDIS.pdf
Hou Z, Chen J and Guo H (2026) From “teaching by word and deed” to “intelligent mentorship”: ethical reconsiderations of AI-enabled medical education — lessons from China. Front. Med. 12:1754139. doi: 10.3389/fmed.2025.1754139
https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1754139/full
Meanwhile, learners develop the capability to critically engage with AI systems while maintaining clinical reasoning, professionalism, and patient-centred care.
Conclusion
In 2026, AI is reshaping medical education by enabling highly personalized, adaptive, and learner-centred learning experiences. Its greatest value lies not in automating education but in supporting individual learners, enhancing clinical reasoning practice, and strengthening competency-based education. However, successful implementation requires robust governance, AI literacy, ethical oversight, and a continued commitment to humanistic medicine. The future medical curriculum should therefore aim to produce physicians who can work effectively with AI while remaining compassionate, reflective, and accountable professionals.
Meeting the Moment: Supporting the Use of AI in Medical Education (AAMC, 2025)
https://www.aamc.org/media/82026/download?attachment
References and Useful Websites
Ahsan Z, et al. Integrating Artificial Intelligence into Medical Education: A Narrative Systematic Review (2025).
https://pmc.ncbi.nlm.nih.gov/articles/PMC12374307/
World Health Organization (WHO). Harnessing Artificial Intelligence for Health.
https://www.who.int/teams/digital-health-and-innovation/harnessing-artificial-intelligence-for-health
Weidener L, Fischer M. Teaching AI Ethics in Medical Education: A Scoping Review (2023).
https://pmc.ncbi.nlm.nih.gov/articles/PMC10588522/
Association of American Medical Colleges (AAMC). Artificial Intelligence Competencies Across the Learning Continuum.
https://www.aamc.org/about-us/medical-education/cbme/ai-competencies
AAMC. Principles for the Responsible Use of Artificial Intelligence in and for Medical Education (2025).
https://www.aamc.org/media/82026/download?attachment
AMEE Guide No. 190. The Role of Artificial Intelligence in Co-Creation of Health Professions Education (2026).
https://cris.maastrichtuniversity.nl/files/291733575/Asoodar-2025-The-role-of-artificial-intelligence.pdf
AMEE (Association for Medical Education in Europe).
https://amee.org/publications/amee-guides/
Medical Teacher. Artificial Intelligence in Health Professions Education Assessment: AMEE Guide No. 178 (2025).
https://healthtopic.org/topics/ai-in-health-professions-education-assessment/
AAMC News. Medical Schools Move from Worrying About AI to Teaching It (2025).
https://www.aamc.org/news/medical-schools-move-worrying-about-ai-teaching-it
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https://medicaleducationelearning.blogspot.com/2024/03/ai-powered-personalised-learning-in.html
https://medicaleducationelearning.blogspot.com/2024/12/the-generative-ai-odyssey-year-of.html
https://medicaleducationelearning.blogspot.com/2026/04/how-students-use-ai-and-how-educators.html
https://medicaleducationelearning.blogspot.com/2026/04/leveraging-generative-ai-for-open.html
https://medicaleducationelearning.blogspot.com/2025/11/dynamic-duo-how-open-education-and.html
