21 citations · 37 across the 29 of their papers we have counts for
4 papers · 1 filter
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…
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…