Understanding the Multilevel Aspects of Resistance to AI Adoption in Higher Education

Abstract

This study aims to examine how resistance to artificial intelligence adoption in higher education is shaped by current research directions. By employing a systematic approach combined with bibliometric co-citation analysis authors seek to understand the multilevel phenomena of resistance to AI adoption and uncover possible research gaps in the existing scientific field. The analysis, performed in accordance with PRISMA statement and mixed-method approach, included 2121 peer-reviewed publications from Scopus with manual qualitative interpretation of thematic clusters. The results reveal four interconnected dimensions of resistance. First, behavioral traits were uncovered, capturing how such factors as perceived usefulness, perceived ease of use, self-efficacy, and expected performance outcomes influence educators’ desire to engage with AI tools. Secondly, organizational readiness for change including such parameters as institutional capacity, culture, and change management was highlighted as highly important in understanding the phenomena and nature of teachers and students resistance to adopt AI. Thirdly, ethical considerations were found to be crucial for current AI adoption management understanding. This research direction included variables such as data privacy, algorithmic bias, transparency, accountability, and broaden debates about the legitimacy of AI in knowledge production, assessment, and teaching practice. Finally, understanding psychological factors such as trust in AI, AI anxiety and perceived loss were also identified as promising research stream. These clusters combined demonstrated that resistance to AI adoption in higher education is a multi-layered phenomenon that can be observed complexly by scientists, combining different analysis levels rather than focusing solely on technological or attitudinal aspects. The findings contribute to organizational behavior theory research by clarifying how emotional, cultural, and institutional mechanisms interact in shaping AI resistance. The study provides a structured basis for integrating resistance factors into a broader model of organizational readiness for AI adoption in higher education, including institutional determinants in addition to psychological and organizational ones.