1 citations · 2 across the 6 of their papers we have counts for
6 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…
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…
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…
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…
Code Soliloquies for Accurate Calculations in Large Language Models
Shashank Sonkar, MyCo Le, Xinghe Chen +3
High-quality conversational datasets are crucial for the successful development of Intelligent Tutoring Systems (ITS) that utilize a Large Language Model (LLM) backend. Synthetic s…