The Skill Drip Approach: A Microlearning Framework for Faculty AI Literacy Development: A Concept Paper

Abstract

The rapid advancement of artificial intelligence (AI) is transforming education, creating new demands on faculty who must adapt pedagogical practices to evolving technologies. Many educators express curiosity about AI's potential yet lack structured, accessible pathways for professional growth. This concept paper proposes the Skill Drip Approach, a microlearning framework for faculty development that fosters AI confidence, curiosity, and competence through brief, practical learning segments. Grounded in cognitive load theory, andragogy, and heutagogy, the framework emphasizes low-barrier, just-in-time learning opportunities that integrate technical, ethical, and pedagogical AI competencies into daily teaching practice. The paper presents five core principles: cognitive manageability, self-determined engagement, practice-embedded learning, reflective integration, and scaffolded competency development. Their implementation is demonstrated through specific design features. A pilot implementation at a mid-sized public university in the northeastern United States demonstrates the framework's feasibility and initial outcomes, including increased faculty confidence, reduced anxiety, and emergent peer-to-peer learning. The paper concludes with implementation guidelines and institutional recommendations, offering higher education leaders a scalable, sustainable model for ongoing professional development adaptable to rapidly evolving technological contexts. This framework addresses a critical gap in literature by synthesizing cognitive science, adult learning principles, and AI literacy competencies into a theoretically grounded, practically feasible model specifically designed for faculty professional development in rapidly changing technological environments.