CogRaise: From Cognitive-Process Diagnosis to Progression-Aware LLM Tutoring

Effective tutoring requires diagnosing how learners process knowledge and determining how instruction should support their cognitive progression. Yet existing LLM tutors emphasize observable pedagogical behaviors, with limited explicit modeling of learner cognition. To address this gap, we introduce CogRaise, a 4B cognition-aware tutor that operationalizes fine-grained cognitive processes as explicit training and optimization targets. First, we construct a Theory-Driven Dataset grounded in the 19 cognitive processes of Anderson’s revised Bloom taxonomy, comprising multi-turn diagnosis-intervention-response trajectories for SFT-based initialization and reward learning. Second, a multi-head reward model separately scores cognition-related tutoring criteria and general response quality. Third, GRPO incorporates a cognitive-advancement reward conditioned on the learner’s current cognitive level to encourage interventions supporting cognitive advancement. Finally, we introduce CogEdu-Bench, an 849-instance multi-subject benchmark evaluating fine-grained cognitive diagnosis, pedagogical decisions, and tutoring response quality. CogRaise improves fine-grained diagnosis QWK by 37.6% relative to its SFT initialization and enhances scaffolding on MathTutorBench, while general reasoning performance remains within 0.3 percentage points of that initialization on each of GSM8K, MATH500, and MMLUPro. These results suggest that established educational theory can be operationalized into structured supervision and reward signals to align LLM tutors with cognitive progression.