The Mechanical Yes-Man: Emancipatory Ai Pedagogy In Higher Education

The proliferation of Large Language Models in higher education presents a fundamental challenge to traditional pedagogical frameworks. Drawing on Jacques Ranci\`ere's theory of intellectual emancipation, this paper examines how generative AI risks becoming a "mechanical yes-man" that reinforces passivity rather than fostering intellectual autonomy. Generative AI's statistical logic and lack of causal reasoning, combined with frictionless information access, threatens to hollow out cognitive processes essential for genuine learning. This creates a critical paradox: while generative AI systems are trained for complex reasoning, students increasingly use them to bypass the intellectual work that builds such capabilities. The paper critiques both techno-optimistic and restrictive approaches to generative AI in education, proposing instead an emancipatory pedagogy grounded in verification, mastery, and co-inquiry. This framework positions generative AI as material for intellectual work rather than a substitute for it, emphasising the cultivation of metacognitive awareness and critical interrogation of AI outputs. It requires educators to engage directly with these tools to guide students toward critical AI literacy, transforming pedagogical authority from explication to critical interloping that models intellectual courage and collaborative inquiry.