Analyzing Competency Frameworks for Generative AI in Higher Education: Toward a Comprehensive Classification of Essential Competencies for Educators

This study aims to define a Competence Framework for higher education educators to effectively integrate Generative AI (GenAI) into their teaching practices. While GenAI holds transformative potential for education, research on the competencies required for its responsible and effective use remains limited. To address this gap, this study employs a Framework Analysis Method (FAM), combining bibliometric analysis with insights from policy and academic frameworks.

The research began with a bibliometric analysis of 1,737 documents from Scopus, which aimed to identify trends and competency clusters related to GenAI. However, the analysis revealed no significant focus on competencies in the most prominent keywords or clusters. As a result, the methodology shifted to FAM, guided by eight policy frameworks to establish an initial thematic framework. This was further refined through the analysis of 14 peer-reviewed articles, each presenting competency frameworks proposed by researchers. Using a chain-of-thought prompting approach with a Large Language Model (ChatGPT), competencies were categorized and indexed using WebQDA for systematic analysis, this indexed process was performed by two researchers to validate the initial framework created in Human-AI collaboration.

The findings identified three key domains of competencies: Teacher-Specific Competencies (Adaptive Teaching, Research Competencies, Prompting), Transversal Competencies (Critical Thinking, Analytical Thinking, Problem-Solving, Ethics), AI-Related competencies (AI Pedagogy, AI Literacy, Ethical AI Use). These domains highlight the intersection of technical proficiency, ethical considerations, and innovative pedagogical approaches required for GenAI integration.

The proposed framework emphasizes the critical need for competency-based training programs and institutional policies to support educators in addressing the challenges and opportunities of GenAI. This research offers actionable recommendations and establishes a foundation for future studies to validate and expand the framework, ensuring its applicability across diverse educational contexts.