A Conceptual Framework for Mind-Expanding Agents: Integrating Adversarial Questioning with AI-Guided Critical Thinking Development

Abstract

Traditional AI educational systems prioritise knowledge delivery over critical thinking development, reinforcing convergent reasoning patterns that limit intellectual growth. This paper presents a novel conceptual framework integrating adversarial questioning principles with AI-guided critical thinking development to address fundamental limitations in current educational technologies. Our model encourages recasting AI as a cognitive adversary, rather than a traditional tutoring system that gives straight answers, by methodically crafting hostile questions that produce beneficial epistemic dissonance.  To lay the theoretical groundwork for turning AI into an active collaborator in cognitive development rather than a passive information supplier, the framework combines adversarial machine learning ideas with classical Socratic pedagogical approaches. To address Bloom's 2-sigma dilemma, this conceptual contribution proposes scalable methods for teaching students critical thinking on an individual level. Optimal cognitive dissonance, safety-constrained questioning techniques, and dynamic user modelling are all elements of the framework, which aims to maintain users' mental well-being while fostering their intellectual growth. Formalising epistemic challenge calibration, developing adversarial pedagogical principles, and establishing ethical frameworks for cognitive confrontation in educational settings are all significant theoretical innovations.