Interpretable Difficulty-Aware Knowledge Tracing in Tutor-Student Dialogues

Huang, S., Scarlatos, A., Lee, J., Lan, A. (2026) — arXiv preprint.

📄 Full text (arXiv)

Analysis

This paper bridges LLM-based dialogue tutoring and interpretable student modeling. By mapping opaque LLM representations to Item Response Theory parameters — student ability (θ) and question difficulty (b) — the framework makes turn-by-turn predictions both accurate and cognitively meaningful. This connects directly to knowledge-tracing-irt by extending IRT beyond static assessment into live dialogue.^2605.01097

The framework was validated across two tutor-student dialogue datasets and outperformed existing KT baselines. The approach also operationalizes intelligent-tutoring by enabling tutors to calibrate scaffolds based on explicit difficulty-aware readiness estimates, and supports llm-student-modeling-memory by providing a principled way to convert LLM outputs into structured student state representations.

Related Pages

• knowledge-tracing-irt — Difficulty-aware dialogue KT extends IRT-based tracing to conversational settings

• intelligent-tutoring — Interpretable ability/difficulty parameters for tutor-student dialogues

• llm-student-modeling-memory — IRT-mapped LLM outputs for student modeling in dialogues

• feedback-loop — Turn-level assessment enables immediate feedback calibration

• ai-tutor-behavioral-evaluation — Turn-by-turn student performance assessment through IRT-based difficulty modeling

• tutoring-specific-vs-general-ai — General LLMs reframed as psychometric instruments through IRT mapping

Citation

APA: Huang et al. (2026). Interpretable Difficulty-Aware Knowledge Tracing in Tutor-Student Dialogues. arXiv:2605.01097. arXiv preprint.