William Lam¹
Priya Khanna¹
1 The University of Sydney

The recent advancements within the field of Artificial Intelligence (AI), particularly large language models such as AI powered chatbots (e.g., Chat GPT and Google Bard) seems to have created unprecedented disruptions in higher education. The use of AI solutions in automation of tasks has penetrated student learning and assessment of learning. Global concerns around student use of chatbots in assignment submission is created concerns and scepticism among faculty around academic integrity. Whilst much has been written about the challenges and affordances of AI in higher education, not much is known about the ways in which students in programs such as Medicine are capitalising on using AI for assessment of and for learning. This presentation provides research being undertaken by group of medical students around developing and validating a scale to assess the usage and attitudes of medical students towards AI technologies in education and practice. Technology Assisted Model and Kane’s Validity framework are being referred to as theoretical frameworks for scale development and validation. The initial survey will be administered to a sample of medical students affiliated to various universities in Australia, Sheffield University, UK and New Orleans, USA. Participants will also be invited to take part in semi-structured interviews. 

The validated scale and qualitative findings will contribute to the field of medical education by providing a reliable and valid tool to assess medical students' readiness for AI-assisted assessment. The findings will inform both students, faculty and administrators in curriculum development, instructional design, and policymaking regarding better and informed integration of AI technologies in medical education programs. 

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