Artificial Intelligence–Powered Simulation for Telehealth Communication Training

INTRODUCTION: Traditional simulation-based communication training remains resource-intensive and difficult to scale. While artificial intelligence (AI), particularly large language models, offers promising solutions for health care education, no blueprint exists for integrating AI-powered simulation training within operational quality improvement (QI) frameworks. This paper presents a 5-phase methodological blueprint, with evaluatory evidence, for implementing AI-powered simulation training to enhance transitional care communication skills. METHODS: We developed a 5-phase methodological framework integrating AI simulation with QI and educational principles, grounded in the Donabedian model. The phases comprised: (1) content validation using Lawshe's methodology, (2) simulation development applying cognitive load theory, (3) platform selection through expert consensus evaluation, (4) structured implementation including structural foundations and sequential deployment, and (5) outcome measurement using statistical process control. RESULTS: Iterative testing across multiple AI platforms revealed that traditional debriefing approaches (advocacy-inquiry, plus-delta) could not be reliably delivered by AI systems. Microdebriefing with rubric-focused feedback emerged as optimal for AI-mediated learning, leveraging AI's strengths in consistent, structured feedback delivery while working within limitations in complex facilitation. Clinical outcomes derived from rubric-based evaluations of recorded patient calls will be reported separately to allow for a more detailed analysis of communication quality and training effectiveness, which falls outside the scope of the present methods-focused study. CONCLUSIONS: This 5-phase methodological blueprint provides an approach for health care organizations seeking to implement cost-effective, scalable AI-powered communication training. By embedding simulation within QI infrastructure, institutions can systematically enhance communication skills while maintaining educational rigor. This work contributes to simulation science, telehealth education, and QI by demonstrating how AI can serve as a scalable alternative to traditional facilitator-led training for foundational communication skills.