24 citations · 53 across the 27 of their papers we have counts for
9 papers · 1 filter
Do LLMs Exhibit Coherent Knowledge Structures in Mathematical Reasoning? A Perspective from Knowledge Space Theory
Peng Cui, Heejin Do, Mrinmaya Sachan
Human knowledge is inherently structured and interdependent: mastery of a concept requires prior mastery of its prerequisites, a principle formalized by Knowledge Space Theory (KST…
Last Translation Benchmark
Vilém Zouhar, Niyati Bafna, Mukund Choudhary +263
For scientific progress, we need benchmarks that test the limits of state-of-the-art models, and evaluation methods that inform us about failure cases. As models get stronger, stan…
SWIM: Student Writing Simulation via Proficiency-Conditioned Generation
Heejin Do, Jakub Kontak, Mrinmaya Sachan
Writing proficiency manifests in how students develop content, organize ideas, choose words, and use language. Despite growing interest in LLM-based student simulation, whether LLM…
GPAgentBench-2K: Benchmarking Large Language Model Agents in Complex Clinical Action Space
Boqi Chen, Xudong Liu, Yunke Ao +2
Large Language Models (LLMs) show great potential as clinical agents, yet existing benchmarks reduce clinical workflows to static predictions or unconstrained Markov Decision Proce…
NL2Scratch: An Executable Benchmark and Evaluation for Block-Based Programming
Heejin Do, Alexandre Ballenghien, Yang Wu +1
Block-based programming environments such as Scratch are widely used in early programming education, yet natural-language-to-code (NL2Code) research has focused primarily on text-b…
Segment-level Tree Search for Long Meeting Document Summarization
Sangwon Ryu, Heejin Do, Jun Seo +4
Meeting documents are challenging to summarize due to their length and complex conversational structure. Existing approaches typically adopt multi-stage pipelines that extract info…
Benchmarking and Enhancing Text-to-Image Models for Generating Visual Representations in Early Arithmetic Education
Junling Wang, Boqi Chen, Heejin Do +3
AI systems are increasingly used to support educational content creation, yet it remains unclear whether they can generate outputs that faithfully represent the pedagogical concept…
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…
Behavior-Aware Item Modeling via Dynamic Procedural Solution Representations for Knowledge Tracing
Jun Seo, Sangwon Ryu, Heejin Do +2
Knowledge Tracing (KT) aims to predict learners' future performance from past interactions. While recent KT approaches have improved via learning item representations aligned with…