A Model for Student-selected Components in Artificial Intelligence: Design of Artificial Intelligence Tools for Real-world Application

Medical students will be graduating into an artificial intelligence (AI)-assisted workplace (1). Recent guidance from Health Data Research UK and the Medical Schools Council recommended that medical students should be AI literate and be able to consider the ethical and professional implications of the integration of AI into healthcare (2). The results of a pilot survey of Glasgow undergraduate medical school (UMS) academic and clinical staff members also found that teaching related to AI in healthcare should be focussed on real-world case studies. To address these unmet needs, second year medical students were offered an ‘Artificial intelligence in healthcare’ student selected component (SSC). During the SSC, students were tasked with identifying a real-world healthcare issue and designing a bespoke, ethical AI tool to address it. The SSC was delivered for eight students using several flexible teaching methods. Interactive, online-anytime teaching included podcasts, formative quizzes, short lecture recordings and critical reading tasks. These were supported by three in-person tutorials, designed to support a weekly formative task to 1) identify a healthcare issue of their choosing, 2) identify ethical issues related to their chosen issue, and 3) to conceptualise an ethical AI tool to address the issue. Week on week, students were invited to discuss and critique each other’s formative tasks in a feedback session facilitated by the tutors, using the task to learn each topic through peer- and tutor- feedback. The summative assessment of this SSC was an essay detailing the student’s chosen healthcare issue and AI tool design and had to include the ethical implications of their AI tool. A summative ten-minute oral presentation was also given by each student, focussing on the need for the AI-tool in their healthcare issue of interest. Together, these teaching, assessment and feedback methods were designed to be inclusive, flexible and driven by the student’s own interest in a specific healthcare issue. This presentation will discuss the results of the survey gathered from UMS staff as well as the reflexive design and implementation of this SSC, plus plans to scale-up the SSC and the challenges associated with this. The pervasive influence of AI means that literacy of AI and related ethical issues will be required by students across many workplaces; and is not exclusive to medicine. This SSC demonstrates a model of a five-week standalone course which could be adapted and applied to different disciplines and different levels of study, promoting inclusive and flexible learning, teaching and feedback using problem-solving of real-world applications. 1. Hale, J. et al. (2024) “Generative AI in Undergraduate Medical Education: A Rapid Review,” Journal of Medical Education and Curricular Development, 11. Available at: https://doi.org/10.1177/23821205241266697. 2. Health Data Research UK and Medical Schools Council (2025) Data science in the medical curriculum: Equipping medical students for the digital age. Available at: https://www.medschools.ac.uk/news/medical-schools-to-prepare-students-with-ai-and-data-science-skills#:~:text=Titled%20Data%20Science%20in%20the%20Medical%20Curriculum%3A%20Equipping,meet%20the%20demands%20of%20the%2021st-century%20healthcare%20landscape.