ArticleJMIR medical education2026
AI-Enhanced Continuing Professional Development as an Evolving Sociotechnical System: Multimethod Theoretical Framework Development Study.
Article in JMIR medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) is changing continuing professional development (CPD) in health care and its interactions with the broader health care system. However, 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 sociotechnical AI-enhanced CPD system, as well as the new roles that AI-empowered patients and society play. Objective: The objective of this study is to propose a comprehensive, theory-driven framework that provides insight into how AI transforms CPD systems. The goal was to integrate established AI constructs with Complexity Theory (CT) and Actor-Network Theory (ANT) to develop a model that guides practice, research, and policy. Methods: We conducted a multimethod 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 pioneering AI use, demonstrating 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 6 pillars: AI literacy, explainability, ethics, readiness, reliability, and learning theories, and 2 theoretical lenses: CT and ANT. 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 explains how localized interactions among human and nonhuman actors shape AI-enhanced CPD. Together, these lenses illustrate how AI redistributes agency, amplifies tensions, and generates emergent learning dynamics within CPD and the broader health care 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 of the ALEERRT-CA framework. Educators, program leaders, and policymakers can use the framework 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 health care practice.
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Registered trials
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