Twelve tips on applying AI tools in HPE scholarship using Boyer's model.

AI has changed the landscape of health professions education. With the hype now behind us, we find ourselves in the phase of reckoning, considering what's next; where do we start and how can educators use these powerful tools for daily teaching and learning. We recognize the great need for training to use AI meaningfully for education.

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.

Co-Designing Case Scenarios and Survey Strategies to Examine the Classification and Reporting of Restrictive Care Practices in Adult Mental Health Inpatient Settings: Perspectives From International Stakeholders.

There is a global initiative to reduce the use of restrictive care practices in mental health settings. Variations in the reported rates across regions complicate the understanding of their use and tracking trends over time. However, it remains unclear whether these discrepancies reflect real differences in the implementation of these practices or are sourced from inconsistencies in incident classification and reporting methods.

Reflections From the Pandemic: Is Connectivism the Panacea for Clinicians?

The COVID-19 pandemic and the recent increased interest in generative artificial intelligence (GenAI) highlight the need for interprofessional communities' collaboration to find solutions to complex problems. A personal narrative experience of one of the authors compels us to reflect on current approaches to learning and knowledge acquisition and use solutions to the challenges posed by GenAI through social learning contexts using connectivism. We recognize the need for constructivism and experiential learning for knowledge acquisition to establish foundational understanding.