Artificial Intelligence in Health Professions Education assessment: AMEE Guide No. 178.

Health Professions Education (HPE) assessment is being increasingly impacted by Artificial Intelligence (AI), and institutions, educators, and learners are grappling with AI's ever-evolving complexities, dangers, and potential. This AMEE Guide aims to assist all HPE stakeholders by helping them navigate the assessment uncertainty before them. Although the impetus is AI, the Guide grounds its path in pedagogical theory, considers the range of human responses, and then deals with assessment types, challenges, AI roles as tutor and learner, and required competencies.

Sharing Is Caring: Helping Institutions and Health Organizations Leverage Data for Educational Improvement.

Competency-based medical education (CBME) has produced large collections of data, which can provide valuable information about trainees and medical education systems. Many organizations continue to struggle with accessing, collecting, governing, analyzing, and visualizing their clinical and/or educational data. This hinders data sharing efforts within and across organizations, which are foundational in supporting system-wide improvements.

Educator's blueprint: Key considerations for using social media in survey-based medical education research.

In this paper, we present a set of recommendations for using social media as a tool for participant recruitment in survey-based medical education research. Drawing from a limited but growing body of literature, we discuss the opportunities and challenges inherent to social media recruitment. This article builds on the authors' previous educator's blueprints about survey design and administration.

Bias in Observed Assessments in Medical Education: A Scoping Review.

Purpose: Observed assessments are integral to medical education but may be biased against structurally marginalized communities. Current understanding of assessment bias is limited because studies have focused on single specialties, levels of training, or social identity characteristics (SIDCs). This scoping review maps studies investigating bias in observed assessments in medical education arising from trainees' observable SIDCs at different medical training levels, with consideration of medical specialties, assessment environments, and assessment tools.