The rapid spread of generative AI has a fundamental impact on higher education (Batista et al., 2024; Sahar & Munawaroh, 2025). It has become evident in recent years that the issue must be addressed at a strategic level (Yusuf et al., 2024) to be able to enjoy the benefits of this technology. Regulation and support pose significant challenges, as the structure and culture of institutions are more rigid than necessary for an emerging technology. Ethical and effective use are urgent issues (Al-Zahrani, 2024). Implementing AI into curricula and institutional operations is a source of competitive advantage, but it is increasingly becoming a core competency. Understanding the motivations and influencing factors of using AI in higher education is a crucial task for establishing effective responses to the upcoming tasks (Strzelecki, 2024). Great scientific attention confirms the relevance of the topic. At the same time, the wide-ranging research models pointed out that no ultimate solution was found. The results suggest that local characteristics must be considered, including national and organizational culture, university profiles, individual competencies of teachers and students, personal attitudes, and other distinguishing factors. Exploring the opinion patterns of the stakeholders and local best practices can contribute to the knowledge base and serve as initial points of future interventions.
A broad range of research framework models is available, including behaviour-based (Ajzen, 2012), technology-focused (Davis, 1989; Goodhue & Thompson, 1995) or unified approaches (Venkatesh et al., 2003, 2012), enabling comparable results about the influencing factors of AI use. This track primarily welcomes PLS-SEM, CB-SEM, and CFA analyses, but it is not limited to these methods. Theoretical reasoning and literature are accepted if practical applicability is emphasized.
The special track aims to collect research in exploring the influencing factors of AI use in relation to education, research, and operations of higher education, with a focus on the following topics:
–    Accepting AI tools and solutions by students and teachers
–    Evaluation of the technological preparedness of the institution for effective AI use,
–    Renewing learning materials and teaching methods with AI
–    The role of AI in managing academic writing and misconduct
–    Regulating and supporting AI use in higher education institutions.