Aiming to Enhance Higher Education Practice Through Generative AI Integration: A Theoretical Exploration of Critical Success Factors

Abstract

This study aims to explore the criteria and success factors for the application of Artificial Intelligence Generated Content (AIGC) in higher education, and guide its practice through the construction of a comprehensive system and framework. This study first identifies seven primary criteria, encompassing technical robustness, integration with existing systems, evidence-based practice, user acceptance and engagement, ethical considerations, collaborative ecosystems, and cultural and contextual sensitivity. These criteria are further refined into 19 subfactors. Utilizing the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method for analysis, the results indicate that user acceptance and engagement occupy a central position in AIGC applications, emerging as the primary factor influencing successful implementation. Simultaneously, the establishment of a collaborative ecosystem is identified as a critical aspect. Additionally, factors such as technical robustness, integration with existing systems, and evidence-based practice not only directly impact user acceptance and engagement but also indirectly affect other elements like the collaborative ecosystem. In terms of specific key success factors, scalability and feedback mechanisms play a crucial role in AIGC implementation. Furthermore, partnerships demonstrate high prominence in higher education AIGC applications, highlighting the importance of building and maintaining strong collaborative relationships for successful implementation. This study provides significant insights into theories in educational technology and offers practical guidance for higher education institutions in their applications.

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