REVIEW article
Front. Educ.
Sec. Higher Education
Volume 10 - 2025 | doi: 10.3389/feduc.2025.1594199
This article is part of the Research TopicArtificial Intelligence in Educational and Business Ecosystems: Convergent Perspectives on Agency, Ethics, and TransformationView all articles
Competency-Based Training Framework for Generative AI in Higher Education
Provisionally accepted
- 1Universidad San Francisco de Quito, Quito, Ecuador
- 2University of Aveiro, Aveiro, Aveiro, Portugal
- 3Marquette University, Milwaukee, Wisconsin, United States
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Integrating Generative Artificial Intelligence (GenAI) in higher education (HE) requires educators to develop new competencies. However, while GenAI holds transformative potential for education, research on the competencies needed for its responsible and effective use remains limited. This study employs a mixed Framework Analysis Method, combining quantitative and qualitative analysis to identify key competencies essential for HE teachers. The research began with a bibliometric analysis of 1,737 documents from Scopus and proceeded with an in-depth analysis of 14 peer-reviewed articles. Using a Chain-of-Thought (CoT) prompting approach, the analysis integrates a Human-GenAI collaboration to identify patterns in existing competency frameworks and empirical publications, aiming to classify and define competencies. The findings reveal that while AI literacy and ethical awareness are frequently mentioned, there is no unified competency framework addressing the pedagogical and technical dimensions of GenAI integration. The FAM process resulted in the identification of three key domains of competencies and a set of 16 competencies. The results highlight the need for a structured, yet flexible competency model tailored to educators. Future research should focus on empirical validation and the development of professional development programs to bridge the identified gaps.
Keywords: Generative AI, competency frameworks, higher education, AI literacy, teacher training, Chain-of-Thought (CoT) prompting
Received: 15 Mar 2025; Accepted: 13 May 2025.
Copyright: © 2025 Burneo-Arteaga, Lira, Costa and Murzi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Pablo Burneo-Arteaga, Universidad San Francisco de Quito, Quito, Ecuador
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