An Artificial Intelligence Competency Taxonomy: Mapping User Integration Levels and Progression Pathways

This conceptual paper proposes an AI Competency Taxonomy that defines user integration levels of AI tools within educational contexts. The rapid advancement of Artificial Intelligence (AI) has transformed teaching, learning, and knowledge creation, particularly in higher education. While existing studies discuss AI literacy and competency, they lack a comprehensive taxonomy that provides actionable guidance for identifying competency stages and evaluating integration pathways. To address this gap, this study develops an AI Competency Taxonomy grounded in Bloom’s Revised Taxonomy—Remembering, Understanding, Applying, Analyzing, Evaluating, and Creating—while synthesizing insights from diverse AI literacy and competency models published between 2021 and 2025. A concept-driven, iterative selection process identified 22 relevant sources, including 21 peer-reviewed journal articles and the UNESCO (2024) AI literacy and competency framework, which was purposefully included as an authoritative global guideline shaping educational policies and competency standards. Findings present a taxonomy comprising six progressive categories based on user roles: (i) Learner-as-Recipient, (ii) Learner-as-Supported, (iii) Learner-as-Collaborator, (iv) Learner-as-Co-Designer, (v) Learner-as-Leader, and (vi) Learner-as-Strategist. These stages represent a continuum of AI integration, ranging from basic, receptive usage to advanced, strategic engagement in academic contexts. This study further identifies key determinants influencing integration levels and outlines structured pathways for progression. The proposed taxonomy offers both a theoretical foundation and a practical framework to guide future empirical research, curriculum design, and policy development aimed at fostering responsible, creative, and effective AI use in education.