The related works section of the paper Assessment Modeling: Fundamental Pre-training Tasks for Interactive Educational Systems focuses on two main research areas:

1. Artificial Intelligence in Education (AIEd): AIEd applies artificial intelligence and deep learning (DL) to improve educational outcomes. Key applications include knowledge tracing, question analysis, and student grade prediction.

2. Pre-training Methods in Education: While pre-training is widely used in natural language processing (NLP), computer vision, and speech recognition, its application in AIEd often focuses on predicting student exam scores (e.g., the SAT) or graduation likelihoods. Early educational pre-training methods were primarily NLP tasks where data was produced from learning materials. Notable prior models include the Test-aware Attention-based Convolutional Neural Network (TACNN), which predicts exam question difficulty, and QuesNet, an embedding model trained on question data that demonstrated superior performance over previous models.