Themes
The symposium is organised around six themes that reflect the opportunities, challenges and emerging questions associated with artificial intelligence in healthcare education.
We welcome abstracts and posters that address these themes through original research, educational innovation, evaluation, critical reflection or interdisciplinary collaboration.
1. Clinical AI Competencies
Preparing healthcare professionals to use AI critically, confidently and safely.
This theme explores the knowledge, skills and professional capabilities required to understand, evaluate and apply AI in healthcare settings.
Potential areas include:
- AI literacy and digital capability
- Interpretation of AI-generated outputs
- Critical appraisal of AI tools and their limitations
- Clinical decision support and professional judgement
- Curriculum development and competency-based education
- Assessment of AI-related clinical skills
2. Human–AI Co-Reasoning: Clinical Learning and Practice
Understanding how human expertise and AI can work together.
This theme examines how healthcare learners and professionals interact with AI systems when reasoning, solving clinical problems and making decisions.
Potential areas include:
- Human–AI collaboration in clinical reasoning
- AI-supported case-based learning and simulation
- Trust, uncertainty and over-reliance on AI
- Maintaining independent judgement
- Cognitive processes and decision-making
- Designing learning activities around human–AI interaction
3. AI Apocaloptimism
Balancing the promise of AI with a critical understanding of its risks.
This theme invites thoughtful discussion of competing visions of AI's future in healthcare education, from transformative opportunities to concerns about disruption and unintended consequences.
Potential areas include:
- Optimism and scepticism about AI in education
- Generative AI and the changing role of educators
- Professional identity and the future clinical workforce
- Automation, de-skilling and over-dependence
- Student and staff attitudes towards AI
- Critical and future-oriented approaches to AI adoption
4. Authentic Assessment & Learning Analytics
Rethinking how learning and clinical competence are developed and evaluated.
This theme considers how AI can support assessment, feedback and learning analytics while preserving academic integrity and valid evidence of competence.
Potential areas include:
- Authentic assessment in the age of generative AI
- AI-supported feedback and personalised learning
- Learning analytics and educational evaluation
- Assessment validity, reliability and fairness
- Academic integrity and assessment redesign
- AI-supported simulation and performance assessment
5. Patient Safety, Algorithmic Bias, & Clinical Ethics
Ensuring that responsible AI remains central to healthcare education.
This theme addresses the ethical, professional and safety considerations associated with AI-supported learning and clinical decision-making.
Potential areas include:
- Algorithmic bias and health inequities
- Privacy, confidentiality and data governance
- Transparency, accountability and explainability
- Patient safety and responsible clinical AI use
- Ethical reasoning and professional responsibility
- Teaching learners to recognise and mitigate AI-related risks
6. Faculty Development & Health System Integration
Building the capabilities and partnerships needed for sustainable change.
This theme explores how educators, institutions and healthcare systems can prepare for and implement AI-enabled education.
Potential areas include:
- Faculty development and staff AI literacy
- Curriculum transformation and institutional readiness
- Integration of AI into clinical placements and training
- Collaboration with healthcare organisations and industry
- Organisational change and implementation evaluation
- Sustainable educational innovation and system-wide learning