As artificial intelligence (AI) becomes increasingly embedded in higher education, understanding resistance to its adoption has become a pressing scholarly concern. This chapter explores theoretical frameworks that help researchers, educators, and graduate students conceptualize AI resistance in academic settings. Drawing a parallel between graduate students’ challenges in aligning research problems with theory and the evolving landscape of AI scholarship, it underscores the need for stronger theoretical grounding. The chapter traces the development of major technology adoption models—Diffusion of Innovations, Theory of Reasoned Action, Theory of Planned Behavior, Technology Acceptance Model, and the Unified Theory of Acceptance and Use of Technology—examining how each explains motivations for or barriers to AI use. It critiques the dominance of quantitative approaches in this field and advocates for more qualitative, participatory methods that prioritize lived experience. By offering a chronological and critical overview, the chapter equips scholars to ask more nuanced questions and support more thoughtful, inclusive AI integration in education.