5 papers
Misconception Acquisition Dynamics in Large Language Models
Naiming Liu, Xinghe Chen, Richard Baraniuk +2
Effective educational AI depends on modeling student misconceptions. Such models enable realistic learner simulation and diagnostic, adaptive tutoring. However, instruction-tuning…
Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey
Zulun Zhu, Tiancheng Huang, Kai Wang +3
Large language models (LLMs) struggle with the factual error during inference due to the lack of sufficient training data and the most updated knowledge, leading to the hallucinati…
MalruleLib: Large-Scale Executable Misconception Reasoning with Step Traces for Modeling Student Thinking in Mathematics
Xinghe Chen, Naiming Liu, Shashank Sonkar
Student mistakes in mathematics are often systematic: a learner applies a coherent but wrong procedure and repeats it across contexts. We introduce MalruleLib, a learning-science-g…
The Imitation Game for Educational AI
Shashank Sonkar, Naiming Liu, Xinghe Chen +1
As artificial intelligence systems become increasingly prevalent in education, a fundamental challenge emerges: how can we verify if an AI truly understands how students think and…
LLM-based Cognitive Models of Students with Misconceptions
Shashank Sonkar, Xinghe Chen, Naiming Liu +2
Accurately modeling student cognition is crucial for developing effective AI-driven educational technologies. A key challenge is creating realistic student models that satisfy two…