Simulating Question-answering Correctness with a Conditional Diffusion

A question-answering (QA) simulator is a model that simulates human students QA behaviors. By leveraging QA history to estimate the probability of correctly answering a newly recommended question, the simulator enables the educational recommender systems to be trained in a simulated environment, protecting human students from the potential negative impact of low-quality recommendations. Despite its significant importance, the construction of QA simulators has not been thoroughly explored in the research domain of AI. Previous methods mainly rely on existing knowledge tracing (KT) models to construct such a simulator. However, due to the discrepancy between the KT task and the simulation task, those KT-based simulators suffer from severe bias accumulation, which limits the effectiveness of the simulation. In this paper, we propose a method called Diffusion-based Simulator (DSim), which takes advantage of diffusion to alleviate the bias accumulation. To our knowledge, DSim is the first to focus on building a QA simulator.