2 citations · 3 across the 13 of their papers we have counts for
5 papers · 1 filter
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…
CLEAR-3K: Assessing Causal Explanatory Capabilities in Language Models
Naiming Liu, Richard Baraniuk, Shashank Sonkar
We introduce CLEAR-3K, a dataset of 3,000 assertion-reasoning questions designed to evaluate whether language models can determine if one statement causally explains another. Each…
Training LLM-based Tutors to Improve Student Learning Outcomes in Dialogues
Alexander Scarlatos, Naiming Liu, Jaewook Lee +2
Generative artificial intelligence (AI) has the potential to scale up personalized tutoring through large language models (LLMs). Recent AI tutors are adapted for the tutoring task…
Do LLMs Make Mistakes Like Students? Exploring Natural Alignment between Language Models and Human Error Patterns
Naiming Liu, Shashank Sonkar, Richard G. Baraniuk
Large Language Models (LLMs) have demonstrated remarkable capabilities in various educational tasks, yet their alignment with human learning patterns, particularly in predicting wh…
A Visual Tour Of Current Challenges In Multimodal Language Models
Shashank Sonkar, Naiming Liu, Richard G. Baraniuk
Transformer models trained on massive text corpora have become the de facto models for a wide range of natural language processing tasks. However, learning effective word represent…