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A central question in current discourse is whether Generative Artificial Intelligence (GenAI) fundamentally transforms educational practices or primarily amplifies existing ones. While various pedagogical frameworks address aspects of GenAI integration, there remains a lack of models that systematically map the interplay between opportunities and challenges across amplification and transformation. To address this gap, this paper introduces an analytical framework - ATOC (Amplification, Transformation, Opportunities, and Challenges) designed to categorize and visualize these dynamics with examples from the higher education context. The ATOC framework builds on established models, SWOT (Strengths, Weaknesses, Opportunities, Threats), and SAMR (Substitution, Augmentation, Modification, and Redefinition) by integrating their respective strengths while addressing limitations in capturing the dynamic and dual nature of emerging technologies. ATOC explicitly distinguishes between amplification and transformation, accounting for both opportunities and challenges. Applying the ATOC framework to 69 statements derived from 11 semi-structured interviews with STEM educators indicates that GenAI is currently used predominantly to amplify existing educational practices rather than to transform them. However, the discussion suggests that such amplifications serve as a foundation for future transformative practices. The framework highlights the role of disciplinary factors in shaping perceptions of GenAI integration and allows to identify educators’ ATOC profiles.