[Paper Review] AI-PACE: A Framework for Integrating AI into Medical Education

Scott McGrath, Katherine K. Kim+3 more|arXiv (Cornell University)|Feb 11, 2026

Artificial Intelligence in Healthcare and Education0 citations

TL;DR

The paper synthesizes existing AI in medical education literature and introduces AI-PACE, a longitudinal, generalist framework (Psychomotor, Affective, Cognitive, Embedded) for integrating AI literacy across the medical education continuum.

ABSTRACT

The integration of artificial intelligence (AI) into healthcare is accelerating, yet medical education has not kept pace with these technological advancements. This paper synthesizes current knowledge on AI in medical education through a comprehensive analysis of the literature, identifying key competencies, curricular approaches, and implementation strategies. The aim is highlighting the critical need for structured AI education across the medical learning continuum and offer a framework for curriculum development. The findings presented suggest that effective AI education requires longitudinal integration throughout medical training, interdisciplinary collaboration, and balanced attention to both technical fundamentals and clinical applications. This paper serves as a foundation for medical educators seeking to prepare future physicians for an AI-enhanced healthcare environment.

Motivation & Objective

• Identify gaps in current AI education frameworks across the medical education continuum (UME, GME, CME).
• Propose a generalist, longitudinal model for AI literacy that spans the entire medical training pathway.
• Operationalize AI competencies using Bloom’s Taxonomy domains with an Embedded pillar for sustained integration.

Proposed method

• Conduct a systematic literature search (PubMed, MEDLINE, ERIC, Asta) for 2016–2025 related to AI in medical education.
• Perform thematic analysis to extract recurring competencies and structural models.
• Compare existing frameworks against longitudinal integration, generalizability, and domain breadth.
• Synthesize findings to develop the AI-PACE framework.
• Map AI-PACE domains to a curriculum across the medical education continuum (UME, GME, CME).

Experimental results

Research questions

• RQ1 What are the gaps in existing AI education frameworks for general medical practice?
• RQ2 How can AI literacy be structured longitudinally to span the full medical education continuum?
• RQ3 What competencies (cognitive, psychomotor, affective) are essential for AI-enabled clinical practice, and how should they be embedded in training?
• RQ4 How can AI education be integrated rather than added as isolated modules to avoid knowledge decay?

Key findings

• Existing AI education is fragmented and heavily specialty-focused (notably imaging/Radiology).
• There is a generalist gap: lack of a comprehensive, non-specialty-specific model for AI literacy in general medicine.
• Current efforts emphasize competencies but not how to structure progression across UME, GME, and CME.
• AI-PACE offers a spiral, longitudinal framework aligned with Bloom’s domains plus an Embedded pillar for sustained integration.
• Affective competencies (trust calibration, empathy, collaboration) are under-addressed in prior frameworks and are central to human-AI partnership.