DiffKT: a Diffusion Model for Fine-Grained Knowledge Tracing

Accurately modeling learners’ evolving knowledge states is a long-standing challenge in intelligent education systems, especially under fine-grained temporal dynamics and inherently noisy learning interactions. Most existing knowledge tracing methods rely on deterministic graph-based or sequential modeling, which often struggle to disentangle genuine knowledge mastery from interaction noise such as guessing and careless errors. To address these challenges, this paper proposes DiffKT, a diffusion-based framework for fine-grained knowledge tracing that models learners’ knowledge states as probabilistic distributions rather than fixed point estimates. DiffKT integrates a dual-graph representation to capture student–question interactions and question–skill associations, together with a state-space sequence model that efficiently encodes long-range learning dependencies with linear complexity. Building upon these representations, a conditional diffusion model with an adaptive noise scheduling strategy is introduced to explicitly distinguish different types of interaction noise, enabling robust denoising and more accurate estimation of latent knowledge states. Extensive experiments on three real-world educational datasets demonstrate that DiffKT consistently outperforms advanced knowledge tracking methods in terms of prediction accuracy and stability, highlighting its effectiveness in modeling noisy and fine-grained learning behaviors.