Scalable Micro-Credentials for AI Literacy in Healthcare: An AI-Assisted Framework for Expert-Led Education

Abstract

Abstract The rapid evolution of clinical guidelines and artificial intelligence has created a velocity gap in medical education, where traditional curricula frequently fail to keep pace with professional practice. Consequently, there is an urgent need for formalized artificial intelligence (AI) micro-credentials to ensure workforce readiness across the entire healthcare ecosystem. Furthermore, existing assessment models for digital credentialing often rely on multiple-choice questions that prioritize pattern recognition over active clinical reasoning, which obscures the underlying logic behind a learner’s choice. This paper introduces a web-based cyberinfrastructure designed to address these challenges by providing an expert-led, AI-assisted platform for the rapid creation and logic-based evaluation of AI micro-credentials. The system architecture utilizes a no-code, node-based visual editor and incorporates local open-source large language models (LLMs) to maintain institutional autonomy and data privacy. A central innovation is the requirement for learners to provide a written rationale for clinical decisions within branching scenarios, which the system evaluates in real-time to assess the underlying thought process for certification. Proof-of-concept modules regarding AI safety in clinical environments and nursing AI workflows were developed to illustrate the platform’s capabilities in managing complex clinical fail-states and recovery pathways. This paper establishes a comprehensive taxonomy of AI competency requirements across hospital roles and proposes a collaborative model to crowdsource an open-access curriculum for scalable AI credentialing in higher education and healthcare.

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