Beyond UNESCO: A Comparative Framework Analysis and Growth Ladder Model of AI Competency for Higher Education Teacher Professional Development

Tandon, Bharti
Singh, Aakriti
Gupta, Adit

Journal of Applied Artificial Intelligence in Education
Vol 2, No 1 (2026): July 2026

Artificial intelligence (AI) is rapidly reshaping professional practice across higher education; however, most faculty lack structured, theoretically grounded frameworks to support responsible and pedagogically effective AI integration. This study addresses this gap through a four-phase comparative analysis of six international AI competency frameworks: UNESCO (2024), ISTE (2023), DigCompEdu with AI Extensions (2023), OECD Learning Compass (2019), Asia Society–OECD Global Competence (2018), and China’s National AI Teacher Competency Framework (2024), using systematic thematic coding with substantial inter-rater reliability (κ = 0.82). The analysis identified six critical gaps: cultural contextualization, economic adaptability, infrastructure flexibility, multilingual provision, indigenous knowledge integration, and disability accessibility. An adapted framework was developed by synthesizing three theoretical traditions–extended Technological Pedagogical Content Knowledge (TPACK), Adult Learning Theory, and Social Constructivism–selected for their collective capacity to address these gaps. Central to the model is the Growth Ladder, a four-level progression comprising Acquire, Adapt, Act, and Create, grounded in established models of teacher expertise and organized across five competency domains. A further theoretical contribution is the collective professional trajectory, which reframes AI competency development as a bidirectional process through which individual educator growth shapes and is shaped by institutional and community contexts. Unlike existing frameworks that treat cultural diversity or developmental progression in isolation, the adapted framework combines culturally responsive competency descriptors with explicit economic scalability principles, embedding Indigenous knowledge integration and multilingual provision as required dimensions absent from current international models. The framework is presented as a theoretically derived model intended for empirical validation using the Delphi method.