21 citations · 26 across the 16 of their papers we have counts for
6 papers · 1 filter
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…
Learning Context Matters: Measuring and Diagnosing Personalization Gaps in LLM-Based Instructional Design
Johaun Hatchett, Debshila Basu Mallick, Brittany C. Bradford +1
The adoption of generative AI in education has accelerated dramatically in recent years, with Large Language Models (LLMs) increasingly integrated into learning environments in the…
MetaCLASS: Metacognitive Coaching for Learning with Adaptive Self-regulation Support
Naiming Liu, Richard Baraniuk, Shashank Sonkar
Large language models can generate fluent explanations, but effective tutoring requires supporting the learner's thought process, not just delivering content. Metacognitive tutorin…
Scalable Generation and Validation of Isomorphic Physics Problems with GenAI
Naiming Liu, Leo Murch, Spencer Moore +4
Traditional synchronous STEM assessments face growing challenges including accessibility barriers, security concerns from resource-sharing platforms, and limited comparability acro…
Learning Context: A Unified Framework and Roadmap for Context-Aware AI in Education
Naiming Liu, Brittany Bradford, Johaun Hatchett +5
We introduce a unified Learning Context (LC) framework designed to transition AI-based education from context-blind mimicry to a principled, holistic understanding of the learner.…
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…