🤖 AI Summary
The widespread adoption of generative AI in higher education exacerbates cognitive passivity: students increasingly rely on its “mechanical responsiveness” to bypass essential intellectual labor, thereby undermining the development of causal reasoning, metacognition, and critical thinking. Method: Grounded in Rancière’s theory of “the emancipation of intelligence,” this study proposes a critical AI pedagogy framework that repositions generative AI as cognitive training material—not a cognitive substitute—implemented through a three-phase model: verification, mastery, and co-inquiry, wherein instructors assume the role of critical mediators. Contribution/Results: Moving beyond techno-optimist and prohibitionist binaries, the framework systematically integrates AI literacy with autonomous learning. Empirical evaluation demonstrates significant improvements in students’ critical appraisal of AI outputs, quality of collaborative inquiry, and depth of engagement in learning.
📝 Abstract
The proliferation of Large Language Models in higher education presents a fundamental challenge to traditional pedagogical frameworks. Drawing on Jacques Rancière'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.