Case Study B: AI for Adaptive Learning – A Case Study in Personalising Mammography Education

Abstract

Ensuring education meets the requirements of all learners is a challenge for educators, particularly in medicine where ‘… structured curricula, didactic teaching, and self-assessment modules to train and evaluate diagnostic accuracy …’ are generally employed in learning environments. This AI case study has developed its system to create a much more experientially bespoke approach to educating healthcare practitioners in the field of mammography imaging. The system combines user-level modelling, random analysis of case difficulty and predictive algorithms for reader–case interactions to create an adaptive learning environment. By addressing long-standing limitations in traditional self-assessment methods it enhances educational equity by tailoring content to individual learners needs.

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