Strategies for developing AI competencies in higher education

Abstract

As Artificial intelligence (AI) continues to transform industries and redefine professional roles, integrating AI competence development into education has become a strategic priority. This exploratory study implements a LLM-based Delphi methodology to identify essential AI competencies, examine barriers to AI integration in academic settings, and develop actionable strategies for competency development in higher education. The research process employed large language models (LLMs) to conduct a simulated exploration with inductive thematic analysis of interdisciplinary perspectives, prioritize critical themes through iterative rating cycles, and resolve polarization via structured deliberation of disputed concepts. The key outputs include the development of a consensus framework outlining universal AI literacy standards, human-AI collaborative pedagogy models, equity-centered implementation protocols, and ethical guardrails for responsible adoption, together with a toolkit with practical guidelines to operationalize the consensus findings. The study aims to assess AI's potential as a collaborative agent in educational design and to evaluate to what extent an AI-generated framework meets established OECD criteria for quality and robustness. This report addresses three primary audiences: curriculum designers developing AI competency models, institutional leaders implementing equity-focused AI policies, and researchers examining pedagogical impacts of human-AI collaboration.

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