activity
20232026
most citedCombining Large Language Models with Tutoring System Intelligence: A Case Study in Caregiver Homework Support

18 citations · 81 across the 30 of their papers we have counts for

collaborators
Showing 2025Show all

13 papers · 1 filter

cs.IR2025★ 1 cited

Comparing RAG and GraphRAG for Page-Level Retrieval Question Answering on a Math Textbook

Eason Chen, Chuangji Li, Eric Li +3

Large language models (LLMs) show promise as educational aids but often lack alignment with specific course materials. We investigate Retrieval-Augmented Generation (RAG) and Graph…

cs.AI2025

Beyond Agreement: Rethinking Ground Truth in Educational AI Annotation

Danielle R. Thomas, Conrad Borchers, Kenneth R. Koedinger

Humans can be notoriously imperfect evaluators. They are often biased, unreliable, and unfit to define "ground truth." Yet, given the surging need to produce large amounts of train…

cs.CY2025

Optimizing Mastery Learning by Fast-Forwarding Over-Practice Steps

Meng Xia, Robin Schmucker, Conrad Borchers +1

Mastery learning improves learning proficiency and efficiency. However, the overpractice of skills--students spending time on skills they have already mastered--remains a fundament…

cs.CL2025

Leveraging LLMs to Assess Tutor Moves in Real-Life Dialogues: A Feasibility Study

Danielle R. Thomas, Conrad Borchers, Jionghao Lin +6

Tutoring improves student achievement, but identifying and studying what tutoring actions are most associated with student learning at scale based on audio transcriptions is an ope…

cs.HC2025

Detecting LLM-Generated Short Answers and Effects on Learner Performance

Shambhavi Bhushan, Danielle R Thomas, Conrad Borchers +5

The increasing availability of large language models (LLMs) has raised concerns about their potential misuse in online learning. While tools for detecting LLM-generated text exist…

cs.CL2025★ 1 cited

LLM-Generated Feedback Supports Learning If Learners Choose to Use It

Danielle R. Thomas, Conrad Borchers, Shambhavi Bhushan +3

Large language models (LLMs) are increasingly used to generate feedback, yet their impact on learning remains underexplored, especially compared to existing feedback methods. This…

cs.HC2025

Combining Log Data and Collaborative Dialogue Features to Predict Project Quality in Middle School AI Education

Conrad Borchers, Xiaoyi Tian, Kristy Elizabeth Boyer +1

Project-based learning plays a crucial role in computing education. However, its open-ended nature makes tracking project development and assessing success challenging. We investig…

cs.CY2025

Starting Seatwork Earlier as a Valid Measure of Student Engagement

Ashish Gurung, Jionghao Lin, Zhongtian Huang +4

Prior work has developed a range of automated measures ("detectors") of student self-regulation and engagement from student log data. These measures have been successfully used to…

cs.HC2025

VTutor for High-Impact Tutoring at Scale: Managing Engagement and Real-Time Multi-Screen Monitoring with P2P Connections

Eason Chen, Xinyi Tang, Aprille Xi +5

Hybrid tutoring, where a human tutor supports multiple students in learning with educational technology, is an increasingly common application to deliver high-impact tutoring at sc…

cs.CL2025

Can Large Language Models Match Tutoring System Adaptivity? A Benchmarking Study

Conrad Borchers, Tianze Shou

Large Language Models (LLMs) hold promise as dynamic instructional aids. Yet, it remains unclear whether LLMs can replicate the adaptivity of intelligent tutoring systems (ITS)--wh…

cs.CY2025

An Integrated Platform for Studying Learning with Intelligent Tutoring Systems: CTAT+TutorShop

Vincent Aleven, Conrad Borchers, Yun Huang +6

Intelligent tutoring systems (ITSs) are effective in helping students learn; further research could make them even more effective. Particularly desirable is research into how stude…

cs.CL2025★ 1 cited

Augmenting Human-Annotated Training Data with Large Language Model Generation and Distillation in Open-Response Assessment

Conrad Borchers, Danielle R. Thomas, Jionghao Lin +2

Large Language Models (LLMs) like GPT-4o can help automate text classification tasks at low cost and scale. However, there are major concerns about the validity and reliability of…

stat.ML2025

Toward Sufficient Statistical Power in Algorithmic Bias Assessment: A Test for ABROCA

Conrad Borchers

Algorithmic bias is a pressing concern in educational data mining (EDM), as it risks amplifying inequities in learning outcomes. The Area Between ROC Curves (ABROCA) metric is freq…