19 citations · 29 across the 26 of their papers we have counts for
7 papers · 1 filter
EduClaw-Bench: A Long-Horizon Benchmark for Pedagogical LLM Agents with Simulated Learners
Unggi Lee, Sookbun Lee, Yeil Jeong +3
Large language models (LLMs) power educational applications from tutoring to essay scoring, but each is a point solution to a single task, and only recently have these point soluti…
Reinforcement Learning for Special Education: Aligning LLM Tutors to Diverse Learners through Disability-Adaptive Training
Unggi Lee, Jihoi Na, Yeil Jeong +2
Large language models are increasingly deployed as intelligent tutors, yet research on aligning them for special education remains absent. Recent work has applied reinforcement lea…
The Tutoring Effectiveness Index: Predicting LLM Math Tutor Quality from Four Conversation Signals
Shim Jaechang, Unggi Lee
Aligning large language models (LLMs) as math tutors typically demands costly reinforcement-learning (RL) training and external LLM judges. We ask whether a frozen model's internal…
Llama-Polya: Instruction Tuning for Large Language Model based on Polya's Problem-solving
Unggi Lee, Yeil Jeong, Chohui Lee +4
This paper introduces Llama-Polya, an instruction-tuned large language model that integrates Polya's four-step problem-solving framework into its dialogue structure to support math…
Exploring the Role of Automated Feedback in Programming Education: A Systematic Literature Review
Yeonji Jung, Yunseo Lee, Jiyeong Bae +4
Automated feedback systems have become increasingly integral to programming education, where learners engage in iterative cycles of code construction, testing, and refinement. Desp…
How Can Video Generative AI Transform K-12 Education? Examining Teachers' Perspectives through TPACK and TAM
Unggi Lee, Yeil Jeong, Seungha Kim +4
The rapid advancement of generative AI technology, particularly video generative AI (Video GenAI), has opened new possibilities for K-12 education by enabling the creation of dynam…
MonaCoBERT: Monotonic attention based ConvBERT for Knowledge Tracing
Unggi Lee, Yonghyun Park, Yujin Kim +2
Knowledge tracing (KT) is a field of study that predicts the future performance of students based on prior performance datasets collected from educational applications such as inte…