13 papers
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…
Simulating Students or Sycophantic Problem Solving? On Misconception Faithfulness of LLM Simulators
Heejin Do, Shashank Sonkar, Mrinmaya Sachan
Large language models (LLMs) can fluently generate student-like responses, making them attractive as simulated students for training and evaluating AI tutors and human educators. Y…
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…
When Can We Trust LLM Graders? Calibrating Confidence for Automated Assessment
Robinson Ferrer, Damla Turgut, Zhongzhou Chen +1
Large Language Models (LLMs) show promise for automated grading, but their outputs can be unreliable. Rather than improving grading accuracy directly, we address a complementary pr…
Circuit Complexity of Hierarchical Knowledge Tracing and Implications for Log-Precision Transformers
Naiming Liu, Richard Baraniuk, Shashank Sonkar
Knowledge tracing models mastery over interconnected concepts, often organized by prerequisites. We analyze hierarchical prerequisite propagation through a circuit-complexity lens…
Can LLMs Model Incorrect Student Reasoning? A Case Study on Distractor Generation
Yanick Zengaffinen, Andreas Opedal, Donya Rooein +3
Modeling plausible student misconceptions is critical for AI in education. In this work, we examine how large language models (LLMs) reason about misconceptions when generating mul…