Evaluating an Interprofessional Artificial Intelligence Education Conference: Healthcare Professionals’ Perceptions of Learning, Relevance, and Intent to Apply Knowledge

Purpose. Since artificial intelligence (AI) is rapidly changing how health services are delivered, nurses and other healthcare professionals may have limited formal training in its practical applications. Continuing education is therefore well positioned to address this growing competence gap. The aim is to assess the educational and practical effects of an interprofessional, AI-oriented conference on participants' outcomes, including their satisfaction, learning, perceived relevance, and intended behavior. Method: A descriptive evaluation approach was used following the conference. The evaluation dataset comprised 103 anonymous survey responses collected immediately after the conference. Descriptive statistics summarized 5-point ratings of the conference, logistics, and behavioral intent selections, as well as respondents' willingness to attend future events. Open-ended feedback was analyzed descriptively. Results: Participants reported high ratings across conference domains. Speaker engagement received the highest mean rating (M = 4.69), followed by improved understanding of AI (M = 4.67), meeting expectations (M = 4.66), practical applicability (M = 4.66), keynote effectiveness (M = 4.63), overall conference rating (M = 4.53), and content relevance (M = 4.53). Among actions, exploring AI tools was selected by 67.0% of respondents, followed by learning more about healthcare AI (58.3%), applying AI-related ideas (53.4%), and collaborating on AI initiatives (42.7%). Conclusion: We conclude that the evidence indicates conference-based continuing education can provide a highly valued introduction to AI and may help improve readiness to incorporate AI into practice. Later evaluations should include pre- and post-assessments, longitudinal follow-up, and objective indicators of changes in practice.