Cogent-Adversary: Cognitive-State Assessment and Adaptive Control for Dual-Agent Tutoring in Education

Large language models (LLMs) have expanded the capabilities of intelligent tutoring systems, but adaptive regulation of instructional challenge and control of domain-specific factual drift remain difficult. We propose Cogent-Adversary, a dual-agent tutoring framework that combines cognitive-state-informed adaptive control (CODA) with Knowledge-Graph-Grounded Adversarial Reasoning and an adversarial consistency check (KGAR–ACCL). The framework was evaluated in a two-week stratified randomized pre-test–post-test study with a one-week delayed assessment at one Chinese university. A total of 78 students were randomized to four tutoring conditions, and 72 completed all sessions. In the primary T1 analyses, the CODA-adaptive condition was associated with higher adjusted knowledge and domain-specific reasoning scores than the single-agent condition (Hedges’ g=2.02 and 2.87, respectively), while participant-clustered models indicated lower factual-error odds than the fixed and rule-based adaptive conditions. Given the short, single-site design and self-developed outcomes, these unusually large effects should be interpreted cautiously. Post hoc offline replays were exploratory only. The findings therefore provide preliminary proof-of-concept evidence for dynamically regulated, knowledge-grounded adversarial tutoring.