Health professions students occupy a distinctive position in relation to generative AI (GAI): they encounter it as learners navigating its growing presence in higher education, and as future practitioners who will work alongside AI systems in clinical settings where errors carry significant consequences. Despite this, few structured opportunities exist for students to examine GAI critically – interrogating not just how to use GAI tools, but what assumptions they embed, whose interests they serve, and what harms they may cause (Bearman, 2025).

This presentation reports on the development of a critical AI literacy workshop currently being co-designed with undergraduate and postgraduate health professions students (Ambrosetti et al, 2025). Student involvement shapes both the content priorities and the pedagogical approach, ensuring the workshop addresses concerns that are relevant to learners at different stages of training.

The workshop engages participants directly with datasets and case studies to examine specific issues including algorithmic bias in clinical AI, the environmental costs of large language models, cognitive offloading and its implications for clinical reasoning, and the dynamics of human-chatbot interaction. Activities are designed in accordance with Universal Design for Learning principles to support both individual reflection and group discussion, with digital tools used to enable flexible modes of participation (Doyle et al, 2025).

Pre- and post-workshop surveys are being developed to capture shifts in participants' perspectives and self-reported literacy. Findings from the co-design process will be shared, along with practical implications for health professions education.

References

Ambrosetti, É., Gaudin, C., Flandin, S., & Poizat, G. (2025). Students as co-designers in health professional education: a scoping review. BMC Medical Education, 25(1), 645.

Bearman, M. (2025). Learning to Work With Artificial Intelligence as Part of Clinical Education. The Clinical Teacher, 22, e70260.

Doyle, A. J., O’Toole, M., Cassidy, D., & Condron, C. M. (2025). Universal Design for Learning (UDL) in simulation-based health professions education. Advances in Simulation, 10(1), 33.