Medical Education and eLearning Portfolio

Wednesday, 29 July 2026

Conference Workshop - Leveraging AI to Develop Open Education Resources (OER) in Global Health Professions Education @ AMEE 2026

Conference Workshop - Leveraging AI to Develop Open Education Resources (OER) in Global Health Professions Education

Tao Le, Poh-Sun Goh

leveraging-ai-to-develop-open-education-resources-oer-in-global-health-professions-education-conference-workshop-amee-2026-pdffrom Poh-Sun Goh

https://medicaleducationelearning.blogspot.com/2026/04/leveraging-generative-ai-for-open.html

https://medicaleducationelearning.blogspot.com/2025/11/strategies-for-developing-open.html

https://medicaleducationelearning.blogspot.com/2025/01/strategies-for-developing-open.html

PCW 13 - How Students Use AI and How Educators Can Leverage This to Enhance Learning: Practical Learner-Centered AI Skills for the Classroom @ AMEE 2026

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

Time: 9:30 AM – 12:30 PM

Venue: Austria Center, Vienna

Fee: €104 + VAT

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

framing-ai-s-role-in-medical-education-in-2026-enhancing-learner-centred-design-and-competency-based-learningfrom Poh-Sun Goh

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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.

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.

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.

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

https://www.aamc.org/media/86881/download?attachment

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/

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

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

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

Tuesday, 19 May 2026

M3 Neuro Learning - Strokes (Ischaemic and Haemorrhagic), and Space Occupying Lesions (including Tumours) - 22 June 2026, Monday, 3pm

NUS Elantra

https://www.slideshare.net/slideshow/comprehensive-neurology-clinical-pathology-conference-cases-and-insights/288080609

(password protected)

Some tips to increase your learning and training effectiveness and efficiency:

Poh-Sun Goh

1. Reflect on the purpose of your medical education and expected outcomes of your undergraduate education and clinical training process.

2. Your current clinical exposure to case based learning in the emergency department, wards, and clinics-outpatient settings builds on a foundation of pre-clinical basic sciences in year 1 and 2 of the medical program (with anatomy and pathology particularly applicable to the interpretation of radiology / imaging studies of your patients).

3. For the head and brain, think about what anatomical structures lie within the area of interest/clinical presentation, or may present with the clinical symptoms and signs exhibited by your patients. Ask yourself what radiological investigation you might request or review in order to make a diagnosis - to rule in, or rule out potential clinical diagnosis. Radiology helps you to "see living anatomy and pathology" in your patients. Thus confirm or exclude diagnosis. Keep in mind that early disease may have very subtle of "negative" imaging during the early stages of clinically symptomatic disease.

4. Review the Radiology studies for your patients. Ask yourself why certain investigations were performed. And in what order.

5. Correlate imaging findings with your clinical observations. Do they make sense, correlate with clinical findings, or do you have to entertain alternative diagnostic possibilities?

6. Use textbooks and a wide variety of online resources to improve your diagnostic and interpretative skills. Practice, practice, practice with reflection and feedback, using a range of resources, to increase your exposure to the variety and spectrum of clinical and imaging findings, both normal and abnormal, from typical, to less common and atypical (as you progress to increase your experience and mastery of clinical practice, and improve your knowledge and skills from undergraduate, through postgraduate to continuing professional development and lifelong learning settings).

7. Compare and contrast is one of the simplest and most effective methods to learn to recognise and differentiate between normal and abnormal XRs and scans. Review a series of normal XRs and scans, and then do side by side comparison between examples of normal, normal and abnormal, and examples of abnormal XRs and scans.

https://effectiviology.com/interleaving/ (mixed practice or interleaving superior to blocked practice)

and

https://www.learningscientists.org/interleaving

https://www.learningscientists.org/downloadable-materials

⬇️

Google Image Search - "CT of ischaemic and haemorrhagic strokes"

Google Image Search - "CT of brain abscesses"

Google Image Search - "CT of brain metastases"

⬇

https://www.radiologymasterclass.co.uk/tutorials/ct/ct_brain_anatomy/ct_brain_anatomy_start

https://casestacks.com/anatomy/brain/ct-brain-anatomy

https://casestacks.com/anatomy/brain/mri-brain-anatomy

https://www.radiologymasterclass.co.uk/tutorials/ct/ct_acute_brain/ct_brain_start

https://geekymedics.com/the-basics-of-mri-interpretation/

https://radiopaedia.org/articles/mri-brain-summary

https://learnneuroradiology.com/brain/brain-imaging-course-5-common-imaging-pathology/

Thursday, 30 April 2026

How Students Use AI and How Educators Can Leverage This to Enhance Learning: Practical Learner-Centered AI Skills for the Classroom - Pre-conference workshop, AMEE 2026

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

Time: 9:30 AM – 12:30 PM

Venue: Austria Center, Vienna

Fee: €104 + VAT

Presenters: Stella Goeschl1, Ken Masters2, Peter de Jong3, Poh-Sun Goh4, Kristina Pavloski5, Rakesh Patel6

Background

The goal is to equip educators with a practical toolkit to use AI effectively and foster meaningful, learner-centered experiences.

Who Should Participate

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

Wednesday, 29 April 2026

Leveraging Generative AI for Open Education Resources (OER): Accelerating Accessible Health Professions Education for All - Focus Session, IAMSE 2026 Conference

https://julnet.swoogo.com/iamse2026/schedule

Name

Focus Session: Leveraging Generative AI for Open Education Resources (OER): Accelerating Accessible Health Professions Education for All

Date & Time

Sunday, 7 June 2026, 09:45 - 11:15

Speakers

Poh Sun GOH - National University of Singapore

Tao Le - University of Louisville

Elisabeth Schlegel - Alice L. Walton School of Medicine

Presentation Track(s)

Technology & eLearning

Presentation Topic(s)

Technology and eLearning

Description

Shared open education resources (OER) can address health professions education content development challenges driven by near-universal time and resource constraints while providing faculty development opportunities. Recent UNESCO education conferences have highlighted the global opportunity for generative AI to transform the development of OER. This interactive session will provide an overview of OER for health professions educators and provide hands-on strategies and generative AI tools for designing and developing high-quality OER. It will also address potential pitfalls and challenges with quality, critical appraisal, copyright and attribution issues when leveraging AI.

Historically, high-quality health professions education has been expensive to develop at scale and limited to major commercial or academic publishers. New digital platforms have accelerated the development of open education resources (OER) which evolved to address critical cost and accessibility issues in education globally. Notably, UNESCO has developed guidelines and policies supporting OER development at the country and institutional level. Emerging artificial intelligence (AI) tools, in particular, offer unprecedented opportunities to streamline content creation, personalize learning, and automate resource curation, further expanding equitable access to quality educational materials. UNESCO has highlighted these potential applications at the Third UNESCO World OER Congress in 2024 and the 2025 UNESCO Digital Learning Week Conference. Finally, novel shared curricular ecosystem platforms can standardize and facilitate the management of OER, empowering a global community of health professions educators to share, collaborate, and go further together. However, AI applications for curriculum design and development are often misunderstood, and educators have limited experience with quality, critical appraisal, copyright and attribution issues when leveraging AI.

leveraging-generative-ai-to-transform-open-education-resources-in-health-professions-educationfrom Poh-Sun Goh

https://medicaleducationelearning.blogspot.com/2025/11/strategies-for-developing-open.html