How Universities Shape Responsible AI Use? A Three-Layer Gap Model
DOI:
https://doi.org/10.34190/eckm.27.1.4798Keywords:
AI, AI in education, responsible AI, AI Governance, transformative accountability, Higher Education, EffectivenessAbstract
The development of artificial intelligence (AI) is increasing the role of universities as actors shaping responsible practices in its use. Despite the growing number of strategies and regulations concerning AI, knowledge remains limited regarding the extent to which institutional actions impact students' educational experiences and the development of their competencies. This article proposes and empirically verifies a model of universities’ influence on responsible AI use, covering institutional governance, teaching practice, attitude internalization, and future diffusion. The mixed-methods study combined Likert-scale survey items with open-ended responses. Quantitative data were analysed using descriptive statistics, Spearman rank correlations, and Cronbach’s α; qualitative data were analysed through content analysis. The analysis reveals varying strengths of relationships between successive levels of the model. A significant relationship was found between the institutional level and educational experiences, and between internalization and the declared diffusion of responsible practices. At the same time, the relationship between educational experiences and internalization of attitudes proved weak and statistically insignificant, indicating the limited impact of formal education on developing student competencies. The contribution of this article lies in the development of a sequential model and the identification of three gaps between successive levels: institutional governance, educational practice, internalization of competencies, and diffusion. The findings show that universities’ influence on responsible AI use is not uniform, but varies across the levels of the model. These findings point to the need to design educational activities that more effectively support the internalization process. In this way, the article contributes to research on AI governance in higher education by highlighting the importance of linking institutional actions with educational experience and the process of shaping student attitudes.
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