Evaluation of the perceived usefulness of the framework suggests that a programme-level approach to assessing reflection, which considers moments of reflection as data points in the ongoing development of reflective practice, could support skill development and meaningful reflection in the age of AI.
Abstract
ABSTRACT Background Reflective practice is a core competency for all healthcare professionals however, ensuring valuable assessment of this key skill is challenging; especially in the age of generative artificial intelligence (AI). The use of a programme‐level approach to develop this capability is proposed as a potential way to support development and assessment of reflection in health professions education. Approach A cross‐disciplinary team of experienced health professions educators came together to develop a framework for assessing reflection in the age of AI. Consisting of three domains, three levels of expertise and artefacts of learning, the framework was informed by relevant healthcare competency standards, existing literature and programmatic assessment principles. Evaluation We sought to evaluate the perceived usefulness of the framework through educators and student feedback. Educators across multiple healthcare disciplines were invited to provide feedback via survey at a faculty education showcase where the proposed framework was presented. Students were invited to provide feedback in a dedicated focus group. A total of 13 participants accepted the invitation (n = 9 educators completed the survey, n = 4 students participated in the focus group), providing qualitative and quantitative data. Thematic analysis was used to analyse the qualitative responses. The quantitative data were used for demographic purposes. Implications Evaluation of the perceived usefulness of the framework suggests that a programme‐level approach to assessing reflection, which considers moments of reflection as data points in the ongoing development of reflective practice, could support skill development and meaningful reflection in the age of AI. Further evaluation of the framework is advised.
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