21 citations · 37 across the 29 of their papers we have counts for
6 papers · 1 filter
Atomic Learning Objectives Labeling: A High-Resolution Approach for Physics Education
Naiming Liu, Shashank Sonkar, Debshila Basu Mallick +2
This paper introduces a novel approach to create a high-resolution "map" for physics learning: an "atomic" learning objectives (LOs) system designed to capture detailed cognitive p…
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…
MalAlgoQA: Pedagogical Evaluation of Counterfactual Reasoning in Large Language Models and Implications for AI in Education
Naiming Liu, Shashank Sonkar, Myco Le +1
This paper introduces MalAlgoQA, a novel dataset designed to evaluate the counterfactual reasoning capabilities of Large Language Models (LLMs) through a pedagogical approach. The…
Synthetic Context Generation for Question Generation
Naiming Liu, Zichao Wang, Richard Baraniuk
Despite rapid advancements in large language models (LLMs), QG remains a challenging problem due to its complicated process, open-ended nature, and the diverse settings in which qu…
Marking: Visual Grading with Highlighting Errors and Annotating Missing Bits
Shashank Sonkar, Naiming Liu, Debshila B. Mallick +1
In this paper, we introduce "Marking", a novel grading task that enhances automated grading systems by performing an in-depth analysis of student responses and providing students w…
Student Data Paradox and Curious Case of Single Student-Tutor Model: Regressive Side Effects of Training LLMs for Personalized Learning
Shashank Sonkar, Naiming Liu, Richard G. Baraniuk
The pursuit of personalized education has led to the integration of Large Language Models (LLMs) in developing intelligent tutoring systems. To better understand and adapt to indiv…