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Mapping the Multidimensional Landscape of Resistance to AI Adoption in Educational Context
This study explores why educators resist adopting Artificial Intelligence (AI) in educational settings by mapping the literature’s key themes. Using a systematic review of 2,121 peer-reviewed sources and a mixed-method approach, a bibliometric co-citation analysis was conducted. The results reveal four interconnected dimensions of resistance: (1) user experience and behavioral intentions (e.g. how perceived usefulness and ease of use influence educators’ intent to adopt AI), (2) organizational transformation and readiness (e.g. institutional capacity, culture, and change management affecting AI implementation), (3) ethical and epistemic concerns (e.g. data privacy, bias, and debates over AI’s role in knowledge and teaching), and (4) emotional and psychological factors (e.g. trust, anxiety, and algorithm aversion among educators). Together, these clusters show that resistance to AI in education is not a singular issue, but a multifaceted phenomenon embedded in social context and human factors. The study offers a comprehensive synthesis of this landscape, contributing to organizational behavior theory by highlighting the social, cultural, and emotional dimensions of technology resistance. It also provides practical insights for designing inclusive AI adoption strategies in education, informing future research and policy to foster effective and ethically grounded AI integration.