From exploration to implementation: developing an AI literacy framework for future clinicians

Since then, the project has moved from exploration into framework development. Through surveys, interviews, focus groups, literature reviews and stakeholder engagement, the team has been developing an evidence-informed curriculum framework designed to prepare future doctors and dentists for an increasingly AI-enabled healthcare environment.

We caught up with Sushma, Cassie and Josh to learn what the project has discovered so far, how the evidence has shaped the emerging framework, and what challenges lie ahead.

 

^{Roadmap for the development and implementation of the AI Curriculum framework}

Strong engagement and a clear appetite for AI literacy

One of the most significant developments since the project began has been the completion of a large-scale consultation exercise involving students, staff and external stakeholders.

The project survey attracted responses from 402 students, 37 staff members and 15 external stakeholders, providing a substantial evidence base for understanding current AI use, confidence levels and educational needs. The student responses represented a broad cross-section of MBBS and BDS cohorts, giving the team confidence that the findings reflected a wide range of perspectives.

The results revealed that AI is already deeply embedded in student learning. While 93% of students reported using AI tools, only a small proportion of respondents had received any formal training. Confidence in using AI was largely driven by experimentation rather than education, suggesting that many learners are developing practices independently, without structured guidance on critical evaluation, ethical use or professional responsibilities.

Despite differences in confidence levels, support for embedding AI literacy within the curriculum was consistently high among students, staff and external stakeholders. Participants recognised that AI is likely to become an increasingly important part of clinical practice and that graduates will need the skills to use these technologies safely, critically and professionally.

The survey findings were complemented by one-to-one interviews and focus groups with students, staff and external experts. These conversations enabled the team to explore emerging themes in greater depth and helped shape the next phase of framework development.

Building a curriculum for future clinical practice

A key challenge for the team was moving beyond questions of AI technology itself and asking a more fundamental educational question: what should an AI-literate clinician look like?

^{A snapshot of the curriculum}

The resulting framework is built around a vision of future doctors and dentists who are AI conscious, AI competent and AI confident, while remaining grounded in ethical practice, patient safety and professional accountability.

The framework aligns closely with both General Medical Council (GMC) and General Dental Council (GDC) expectations. Rather than positioning AI literacy as a standalone technical skill, the project proposes embedding it within existing professional, ethical and clinical competencies.

The curriculum framework begins with foundational concepts, including AI systems, data, ethics, governance and human oversight. It then progresses towards more applied capabilities such as evaluating AI tools, understanding performance metrics, using AI responsibly in education and research, and critically assessing its role within clinical practice.

Importantly, the project team wanted to ensure that AI literacy remained firmly connected to the human dimensions of healthcare. Throughout the interviews and workshop discussions, participants repeatedly highlighted the importance of maintaining patient-centred care, professional judgement and ethical responsibility in an environment where AI tools may increasingly influence decision-making.

As Cassie explained, the framework is designed not only to develop technical understanding, but also to support students in navigating questions of professional identity, accountability and lifelong learning.

Educational theory meets professional practice

The team also drew extensively on educational theory when developing the framework.

While Bloom’s Taxonomy provided a useful foundation for thinking about progression from foundational knowledge to higher-order application and evaluation, the team felt that cognitive development alone was insufficient. They therefore incorporated Fink’s Taxonomy of Significant Learning, which emphasises the integration of knowledge, human dimensions, caring, reflection and learning how to learn.

This proved particularly valuable when considering the wider implications of AI for healthcare education. Questions raised during the project extended beyond technical competence and included issues such as trust, professional judgement, patient communication and the changing relationship between clinicians and technology.

The resulting model combines progressive development of AI knowledge and application with a longitudinal emphasis on human-centred care, professional values and lifelong learning. The team describe this as a curriculum structure that develops both clinical readiness and professional identity simultaneously.

Looking ahead

While the project has made substantial progress, the team is clear that implementation will present challenges.

Several themes emerged consistently from the interviews and workshop discussions. Staff highlighted the need for continuing professional development and greater institutional support to build confidence in teaching AI-related topics. Participants also emphasised the importance of role models who can demonstrate effective and responsible AI use in educational and clinical contexts.

At a curriculum level, questions remain about how best to future-proof content in a field that evolves rapidly. The framework will need to remain adaptable while maintaining alignment with professional standards, regulatory requirements and emerging evidence. Participants also identified the need for cross-disciplinary collaboration, infrastructure investment, institutional guidance and careful change management if AI literacy is to become embedded successfully across programmes.

Despite these challenges, the project has established a strong foundation. By combining student partnership, stakeholder engagement, educational theory and professional regulation, the team has developed an ambitious framework that positions AI literacy as more than technical training. Instead, it presents AI literacy as part of preparing future clinicians to exercise sound judgement, uphold professional values and deliver safe, human-centred care in an increasingly AI-enabled world.

The team is now undertaking further analysis of the qualitative data and refining the framework based on stakeholder feedback, with plans to share outputs and recommendations more widely across Queen Mary and the broader medical and dental education community.

References

L Dee Fink (2003). Creating significant learning experiences : an integrated approach to designing college courses. San Francisco, Ca: Jossey-Bass.