Artificial Intelligence (AI)-based technologies are increasingly transforming higher education, leading to substantial advances in educational methodologies. Universities must document and classify existing or newly developed AI-based teaching and learning scenarios. Such classification is essential for helping instructors make informed decisions, estimate associated development and operational costs, and facilitate effective utilization. Existing literature frequently focuses classifications on either
technological tool characteristics or the student's viewpoint. In contrast, this article proposes a complementary educator-centered taxonomy to make the pedagogical benefits and constraints of AIsupported educational scenarios more transparent, particularly from the educator’s perspective. We
propose evaluating the three core dimensions: repetition (R), data access (D), and semantic discrimination (S). By assessing these dimensions, educators gain a better understanding of which teaching scenarios benefit significantly from AI support. After reviewing existing classification literature
in higher education contexts, we introduce our taxonomy and demonstrate its practical applicability in selected educational use cases.
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