[Preprint] A theoretical framework for integrating AI into health professions education

I’m continuing the trend of posting early versions of work to a preprint server, before submitting to a journal. I think this version probably represents a ‘good enough’ piece of work that it might be useful. You can download the preprint from the Open Science Framework (OSF).

This article presents a theoretical framework for integrating AI into health professions education. I developed the framework by analysing four learning theories: social constructivism, critical pedagogy, complexity theory, and connectivism. I conducted a systematic comparative analysis of these theories across six dimensions of learning: power dynamics, knowledge representation, agency, contextual influence, identity formation, and temporality. From the analysis, I developed seven principles that I’m calling the ACADEMIC framework. Rather than treating AI as a threat or a quick solution, the framework positions it as a learning partner that can help address longstanding challenges in preparing healthcare professionals.

ACADEMIC framework for integrating AI into HPE

1. Augmented dialogue: Position AI as a participant in learning conversations rather than an authority source
2. Critical consciousness: Help students critically evaluate AI outputs and understand embedded power dynamics
3. Adaptive expertise: Use AI to develop flexible knowledge application rather than just delivering content
4. Dynamic contexts: Ensure AI enhances rather than simplifies the authentic contexts of healthcare
5. Emergent curriculum: Support adaptive, responsive approaches to curriculum rather than rigid pathways
6. Metacognitive development: Use AI to make thinking processes visible and develop self-awareness
7. Interprofessional communities: Facilitate knowledge building across traditional boundaries

Abstract

Health professions education faces a significant challenge: graduates are simultaneously overwhelmed with information yet under-prepared for complex practice environments. Meanwhile, artificial intelligence (AI) tools are being rapidly adopted by students, revealing fundamental gaps in traditional educational approaches. This paper introduces the ACADEMIC framework, a theoretically grounded approach to integrating AI into health professions education (HPE) that shifts focus from assessing outputs to supporting learning processes. Drawing on social constructivism, critical pedagogy, complexity theory, and connectivism, I analysed learning interactions across six dimensions: power dynamics, knowledge representation, agency, contextual influence, identity formation, and temporality. From this comparative analysis emerged seven principles—Augmented dialogue, Critical consciousness, Adaptive expertise development, Dynamic contexts, Emergent curriculum design, Metacognitive development, and Interprofessional Community knowledge building—that guide the integration of AI into HPE. Rather than viewing AI as a tool for efficient content delivery or a threat to academic integrity, the ACADEMIC framework positions AI as a partner in learning that can address longstanding challenges. The framework emphasises that most students are not natural autodidacts and need guidance in learning with AI rather than simply using it to produce better outputs. By reframing the relationship between students and AI, educators can create learning environments that more authentically prepare professionals for the complexity, uncertainty, and collaborative demands of contemporary healthcare practice.