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This paper presents INSIDE, a framework that fine-tunes LLMs to generate internal dialogue grounded in Bloom's Taxonomy, enabling student simulators to model both latent reasoning and observable actions. Evaluations show improved action fidelity and reasoning alignment compared to prompting baselines.
Proposes Phase-Aware Knowledge Tracing (PAKT), a framework that decomposes student interactions into ability and proficiency phases, using a multi-branch Transformer to model phase-specific and holistic knowledge states, achieving consistent improvements over baselines on six benchmarks.
This paper introduces the Unified Behavioral Prediction and Calibration Analysis Pipeline (UBP-CAP), an integrated framework linking student performance prediction, calibration error calculation, and variance decomposition of metacognitive misalignment. Evaluated on a dataset of 1,195 interaction records, the pipeline identifies key predictors of correctness and reveals that metacognitive calibration is primarily situational rather than a stable trait.