Cover Image

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

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Borchers, Conrad, Thomas, Danielle R., Lin, Jionghao, Abboud, Ralph, Koedinger, Kenneth R.
Format:	Preprint
Published:	2025
Subjects:	Computation and Language Computers and Society Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Beyond Agreement: Rethinking Ground Truth in Educational AI Annotation
by: Thomas, Danielle R., et al.
Published: (2025)

Leveraging LLMs to Assess Tutor Moves in Real-Life Dialogues: A Feasibility Study
by: Thomas, Danielle R., et al.
Published: (2025)

Improving Assessment of Tutoring Practices using Retrieval-Augmented Generation
by: Han, Zifei FeiFei, et al.
Published: (2024)

Using Large Language Models to Assess Tutors' Performance in Reacting to Students Making Math Errors
by: Kakarla, Sanjit, et al.
Published: (2024)

Brief but Impactful: How Human Tutoring Interactions Shape Engagement in Online Learning
by: Borchers, Conrad, et al.
Published: (2026)

LLM-Generated Feedback Supports Learning If Learners Choose to Use It
by: Thomas, Danielle R., et al.
Published: (2025)

Comparing Few-Shot Prompting of GPT-4 LLMs with BERT Classifiers for Open-Response Assessment in Tutor Equity Training
by: Kakarla, Sanjit, et al.
Published: (2025)

Starting Seatwork Earlier as a Valid Measure of Student Engagement
by: Gurung, Ashish, et al.
Published: (2025)

GPTutor: Great Personalized Tutor with Large Language Models for Personalized Learning Content Generation
by: Chen, Eason, et al.
Published: (2024)

Modernizing Ground Truth: Four Shifts Toward Improving Reliability and Validity in AI in Education
by: Thomas, Danielle R., et al.
Published: (2026)

Combining Large Language Models with Tutoring System Intelligence: A Case Study in Caregiver Homework Support
by: Venugopalan, Devika, et al.
Published: (2024)

Do Tutors Learn from Equity Training and Can Generative AI Assess It?
by: Thomas, Danielle R., et al.
Published: (2024)

Detecting LLM-Generated Short Answers and Effects on Learner Performance
by: Bhushan, Shambhavi, et al.
Published: (2025)

Leveraging Large Language Models for Identifying Knowledge Components
by: Wang, Canwen, et al.
Published: (2025)

3DG: A Framework for Using Generative AI for Handling Sparse Learner Performance Data From Intelligent Tutoring Systems
by: Zhang, Liang, et al.
Published: (2024)

Predicting Learning Performance with Large Language Models: A Study in Adult Literacy
by: Zhang, Liang, et al.
Published: (2024)

Does Multiple Choice Have a Future in the Age of Generative AI? A Posttest-only RCT
by: Thomas, Danielle R., et al.
Published: (2024)

Automatic Large Language Models Creation of Interactive Learning Lessons
by: Lin, Jionghao, et al.
Published: (2025)

Improving Automated Feedback Systems for Tutor Training in Low-Resource Scenarios through Data Augmentation
by: Xu, Chentianye, et al.
Published: (2025)

Disentangling Learning from Judgment: Representation Learning for Open Response Analytics
by: Borchers, Conrad, et al.
Published: (2025)

Large Language Models as Students Who Think Aloud: Overly Coherent, Verbose, and Confident
by: Borchers, Conrad, et al.
Published: (2026)

Understanding Teacher Revisions of Large Language Model-Generated Feedback
by: Borchers, Conrad, et al.
Published: (2026)

From Heuristics to Analytics: Forecasting Effort and Progress in Online Learning
by: Qiu, Eric S., et al.
Published: (2026)

Improving Hybrid Human-AI Tutoring by Differentiating Human Tutor Roles Based on Student Needs
by: Gurung, Ashish, et al.
Published: (2026)

The Digital Divide in Generative AI: Evidence from Large Language Model Use in College Admissions Essays
by: Lee, Jinsook, et al.
Published: (2026)

Coasting Through Class: Learning Opportunity Loss from Practice Avoidance During Individual Seatwork
by: Gurung, Ashish, et al.
Published: (2026)

How Can I Improve? Using GPT to Highlight the Desired and Undesired Parts of Open-ended Responses
by: Lin, Jionghao, et al.
Published: (2024)

VTutor for High-Impact Tutoring at Scale: Managing Engagement and Real-Time Multi-Screen Monitoring with P2P Connections
by: Chen, Eason, et al.
Published: (2025)

How Can I Get It Right? Using GPT to Rephrase Incorrect Trainee Responses
by: Lin, Jionghao, et al.
Published: (2024)

Representation Learning to Study Temporal Dynamics in Tutorial Scaffolding
by: Borchers, Conrad, et al.
Published: (2026)

Enhancing the De-identification of Personally Identifiable Information in Educational Data
by: Ji, Zilyu, et al.
Published: (2025)

An Integrated Platform for Studying Learning with Intelligent Tutoring Systems: CTAT+TutorShop
by: Aleven, Vincent, et al.
Published: (2025)

IDEAlign: Comparing Large Language Models to Human Experts in Open-ended Interpretive Annotations
by: Nam, Hyunji, et al.
Published: (2025)

Understanding Student Effort Using Response-Time Propensities During Problem Solving
by: Borchers, Conrad, et al.
Published: (2026)

Who Decides in AI-Mediated Learning? The Agency Allocation Framework
by: Borchers, Conrad, et al.
Published: (2026)

Evaluating the Impact of Data Augmentation on Predictive Model Performance
by: Švábenský, Valdemar, et al.
Published: (2024)

Toward Trait-Aware Learning Analytics
by: Borchers, Conrad, et al.
Published: (2026)

Measuring the Impact of Student Gaming Behaviors on Learner Modeling
by: Liu, Qinyi, et al.
Published: (2025)

SlideItRight: Using AI to Find Relevant Slides and Provide Feedback for Open-Ended Questions
by: Zhao, Chloe Qianhui, et al.
Published: (2025)

Push and Pull in Community College Cross-Enrollment: Remoteness, Articulation, and Student Mobility
by: Borchers, Conrad, et al.
Published: (2026)