JMIR Publications

ABSTRACT

Background:

Artificial intelligence (AI) is changing continuing professional development (CPD) in healthcare and its interactions with the broader healthcare system. Yet current scholarship lacks an integrated theoretical model that explains how AI impacts CPD as a complex sociotechnical system. Existing frameworks usually focus on isolated phenomena, such as ethics, literacy, or learning theory, leaving unaddressed the dynamics of how those phenomena interact in the complex socio-technical AI-enhanced CPD system, as well as the new roles that AI-empowered patients and society play.

Objective:

Artificial intelligence (AI) is changing continuing professional development (CPD) in healthcare and its interactions with the broader healthcare system. Yet current scholarship lacks an integrated theoretical model that explains how AI impacts CPD as a complex sociotechnical system. Existing frameworks usually focus on isolated phenomena, such as ethics, literacy, or learning theory, leaving unaddressed the dynamics of how those phenomena interact in the complex socio-technical AI-enhanced CPD system, as well as the new roles that AI-empowered patients and society play.

Methods:

We conducted a multi-method theory construction. The process started with identifying the AI-enhanced CPD as an established yet evolving phenomenon. Through a structured literature review, the main building blocks of AI-enhanced CPD were identified, as well as the ontological base (CT and ANT). The model was developed through iterative human-led and AI-assisted abductive analysis. The final model was abductively validated on a case study of a national organization that is pioneering AI use, demonstrating that the theoretical model makes sense in practice. All conceptual decisions were reviewed collaboratively by the author group.

Results:

The ALEERRT-CA framework is made of six pillars: AI Literacy, Explainability, Ethics, Readiness, Reliability, and Learning Theories, and two theoretical lenses: Complexity Theory and Actor-Network Theory. CT elucidates macro-level system behaviors in the AI-enhanced CPD system. Those behaviors include emergence, feedback loops, adaptation, and reality made of nested complex systems. ANT, on the other hand, explains how localized interactions among human and nonhuman actors shape AI-enhanced CPD. Together, these lenses show how AI redistributes agency, amplifies tensions, and generates emergent learning dynamics within CPD and the broader healthcare system.

Conclusions:

This study presents a novel, conceptual model of AI-enhanced CPD as a sociotechnical system. The integration of CT and ANT with AI constructs improves explanatory power on ALEERRT-CA. Educators, program leaders, and policymakers can use the frameworks as a structured toolset to evaluate AI readiness, design responsible AI-enhanced CPD practices, and plan future empirical research. The framework provides a theoretical lens for observing the rapidly evolving field of AI-enhanced CPD and healthcare practices.