The artificial intelligence (AI) revolution has arrived, reshaping how many professionals conduct their daily work in Continuing Medical Education/Continuing Professional Development (CME/CPD). In this interactive session, we will present compelling insights from the third annual ACEHP survey of AI Use in CME/CPD (December 2025). Through comparisons to prior surveys conducted in late 2023 and 2024, we will document the evolution from basic AI awareness to significant integration into daily CME/CPD workflows. In provocative discussions, we will highlight dramatic shifts in professional attitudes toward AI (i.e., from curiosity to adoption) and the persistent barriers that are hindering broader AI implementation. We will also confront important ethical questions that have the potential to impact CME/CPD program development, accreditation standards, and learner expectations. This session will incorporate live polling to capture audience sentiments in real-time. We will also organize interactive breakout sessions that provide you an opportunity to articulate and work through real-world challenges such as institutional skepticism, accuracy of AI results, and the potential for generating misleading, biased, or hallucinated content. Finally, we will incorporate compelling case studies from CME innovators who are successfully harnessing AI in ways that demonstrate practical value. By participating in this session, you will be better prepared to understand the current role of AI in CME/CPD and incorporate innovative AI technologies into your workflows to enhance the quality, efficiency, and impact of your professional endeavors in CME/CPD.
Learning Objectives:
Evaluate key trends and barriers to AI adoption within CME/CPD through longitudinal survey data of the ACEHP membership.
Apply insights from survey data and real-world case studies to enhance the integration of generative AI into CME/CPD planning, delivery, and evaluation.
Identify strategies to address ethical and practical risks associated with AI use in CME/CPD such misinformation, bias, and institutional resistance.
