5 papers
Tailoring Diagnostic Modeling to Individual Learners: Personalized Distractor Generation via MCTS-Guided Reasoning Reconstruction
Tao Wu, Jingyuan Chen, Wang Lin +6
Distractors-incorrect yet plausible answer choices in multiple-choice questions (MCQs)-are vital in educational assessments, as they help identify student misconceptions by present…
Embracing Imperfection: Simulating Students with Diverse Cognitive Levels Using LLM-based Agents
Tao Wu, Jingyuan Chen, Wang Lin +5
Large language models (LLMs) are revolutionizing education, with LLM-based agents playing a key role in simulating student behavior. A major challenge in student simulation is mode…
Reasoning Physical Video Generation with Diffusion Timestep Tokens via Reinforcement Learning
Wang Lin, Liyu Jia, Wentao Hu +6
Despite recent progress in video generation, producing videos that adhere to physical laws remains a significant challenge. Traditional diffusion-based methods struggle to extrapol…
Knowledge is Power: Harnessing Large Language Models for Enhanced Cognitive Diagnosis
Zhiang Dong, Jingyuan Chen, Fei Wu
Cognitive Diagnosis Models (CDMs) are designed to assess students' cognitive states by analyzing their performance across a series of exercises. However, existing CDMs often strugg…
WisdomBot: Tuning Large Language Models with Artificial Intelligence Knowledge
Jingyuan Chen, Tao Wu, Wei Ji +1
Large language models (LLMs) have emerged as powerful tools in natural language processing (NLP), showing a promising future of artificial generated intelligence (AGI). Despite the…