Abstract
ABSTRACT
Application exercises are the most critical components of the sequenced steps of team‐based learning (TBL) that should be designed as complex, real‐world problems to elicit deep learning and foster student engagement. The process of crafting these exercises presents several challenges for educators—time constraints, alignment with learning objectives, scaffolding and authentic simulation of real‐world problem solving. The use of artificial intelligence (AI) cuts down time and effort for design and allows focus on refinement and error correction. In addition, it allows use of an iterative process to generate higher order application exercises that provide the context and competency level required for students to make learning gains.
We are sharing our knowledge on using AI to design application exercises (AEs) garnered from our international workshops on TBL. We empowered educators to write effective prompts using the Task–Role–Audience–Create–Intent (TRACI) framework to create engaging AEs that meet the 4S principles, i.e., significant problem, same problem, specific choice, simultaneous reporting, of TBL AEs, aligned to higher order Bloom's taxonomy objectives for critical thinking and learner engagement. We then demonstrated how we used Anthropic's Claude 4 Sonnet AI tool to design the exercises using an iterative process. The workshop concluded with a reflection exercise and feedback to promote intentionality.
Our collective experience facilitating this workshop in interdisciplinary and interprofessional settings, serving as TBL facilitators at our respective institutions and informal participant feedback at the workshops informed the development of this series of best practice recommendations for faculty development. This foundational yet critical skill is necessary in the rapidly evolving landscape of AI skill‐building competencies for health professions educators. We describe our best practice recommendations here within the paradigm of course design for TBL.