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  3. Ai-enhanced Continuing Professional Development As An Evolving Sociotechnical System: Multimethod Theoretical Framework Development Study.
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  3. Ai-enhanced Continuing Professional Development As An Evolving Sociotechnical System: Multimethod Theoretical Framework Development Study.

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AI-Enhanced Continuing Professional Development as an Evolving Sociotechnical System: Multimethod Theoretical

Vjekoslav Hlede1, Sofia Valanci2, G Robert D'Antuono3

  • 1Learning Division, American Society of Anesthesiologists, 1061 American Lane, Schaumburg, IL, 60173-4973, United States, 1 7734705592.

JMIR Medical Education
|February 12, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Artificial intelligence (AI) transforms healthcare continuing professional development (CPD) by integrating Complexity Theory and Actor-Network Theory. The ALEERRT-CA framework guides AI readiness, responsible practice design, and future research in this evolving sociotechnical system.

Keywords:
AIAI explainabilityAI readinessAI reliabilityAI-enhanced CPDActor-Network Theoryartificial intelligenceblack boxcomplexitycontinuing professional developmentlearning theories

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Area of Science:

  • Sociotechnical Systems
  • Health Professions Education
  • Artificial Intelligence in Healthcare

Background:

  • Current research lacks a unified model for AI's impact on healthcare continuing professional development (CPD) as a complex sociotechnical system.
  • Existing frameworks often isolate factors like ethics or literacy, neglecting their interactions and the roles of AI-empowered patients and society.
  • There's a need for a comprehensive model to understand AI's dynamic influence on CPD and the broader healthcare ecosystem.

Purpose of the Study:

  • To propose a theory-driven framework, integrating AI constructs with Complexity Theory (CT) and Actor-Network Theory (ANT), to explain AI's transformation of CPD systems.
  • To develop a model that offers insights into the practical, research, and policy implications of AI in CPD.
  • To provide a robust theoretical foundation for analyzing AI-enhanced CPD.

Main Methods:

  • A multimethod theory construction approach was employed, starting with identifying AI-enhanced CPD as a phenomenon.
  • A structured literature review identified key AI-enhanced CPD components and the theoretical underpinnings of CT and ANT.
  • An iterative, AI-assisted abductive analysis developed the model, which was validated using a case study of an organization using AI in CPD.

Main Results:

  • The developed ALEERRT-CA framework comprises six pillars (AI literacy, explainability, ethics, readiness, reliability, learning theories) and two theoretical lenses (CT and ANT).
  • Complexity Theory (CT) explains macro-level system dynamics like emergence and adaptation in AI-enhanced CPD.
  • Actor-Network Theory (ANT) clarifies how human and nonhuman actors shape AI-enhanced CPD, illustrating AI's redistribution of agency and emergent learning dynamics.

Conclusions:

  • This study introduces a novel sociotechnical model for AI-enhanced CPD, the ALEERRT-CA framework, integrating CT and ANT for improved explanatory power.
  • The framework serves as a practical toolset for educators, leaders, and policymakers to assess AI readiness, design responsible CPD, and guide future research.
  • ALEERRT-CA offers a crucial theoretical lens for navigating the rapidly evolving landscape of AI in healthcare education and practice.