33 citations · 64 across the 28 of their papers we have counts for
17 papers · 1 filter
Towards Breaking the Learning System Wall Using Multimodal Tutoring Transcriptions
Danielle R. Thomas, Marie Cynthia Abijuru Kamikazi, Ashish Gurung +5
Past research using log data has faced the "learning system wall," whereby few methods exist for generalizing models of student learning across platforms. Increasingly, online lear…
Does the TalkMoves Codebook Generalize to One-on-One Tutoring and Multimodal Interaction?
Corina Luca Focsan, Marie Cynthia Abijuru Kamikazi, Tamisha Thompson +5
Accountable Talk theory has been widely adopted to analyze classroom discourse and is increasingly used to annotate tutoring interactions. In particular, the TalkMoves codebook, gr…
Evaluating a Data-Driven Redesign Process for Intelligent Tutoring Systems
Qianru Lyu, Conrad Borchers, Meng Xia +4
Past research has defined a general process for the data-driven redesign of educational technologies and has shown that in carefully-selected instances, this process can help make…
LLM-based Multimodal Feedback Produces Equivalent Learning and Better Student Perceptions than Educator Feedback
Chloe Qianhui Zhao, Jie Cao, Jionghao Lin +1
Providing timely, targeted, and multimodal feedback helps students quickly correct errors, build deep understanding and stay motivated, yet making it at scale remains a challenge.…
Generative AI alone may not be enough: Evaluating AI Support for Learning Mathematical Proof
Eason Chen, Sophia Judicke, Kayla Beigh +16
We evaluate the effectiveness of LLM-Tutor, a large language model (LLM)-powered tutoring system that combines an AI-based proof-review tutor for real-time feedback on proof-writin…
AI Knows Best? The Paradox of Expertise, AI-Reliance, and Performance in Educational Tutoring Decision-Making Tasks
Eason Chen, Jeffrey Li, Scarlett Huang +4
We present an empirical study of how both experienced tutors and non-tutors judge the correctness of tutor praise responses under different Artificial Intelligence (AI)-assisted in…
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…
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…
VTutor: An Animated Pedagogical Agent SDK that Provide Real Time Multi-Model Feedback
Eason Chen, Chenyu Lin, Yu-Kai Huang +4
Pedagogical Agents (PAs) show significant potential for boosting student engagement and learning outcomes by providing adaptive, on-demand support in educational contexts. However,…
From First Draft to Final Insight: A Multi-Agent Approach for Feedback Generation
Jie Cao, Chloe Qianhui Zhao, Xian Chen +4
Producing large volumes of high-quality, timely feedback poses significant challenges to instructors. To address this issue, automation technologies-particularly Large Language Mod…
SlideItRight: Using AI to Find Relevant Slides and Provide Feedback for Open-Ended Questions
Chloe Qianhui Zhao, Jie Cao, Eason Chen +2
Feedback is important in supporting student learning. While various automated feedback systems have been implemented to make the feedback scalable, many existing solutions only foc…
Toward Automated Qualitative Analysis: Leveraging Large Language Models for Tutoring Dialogue Evaluation
Megan Gu, Chloe Qianhui Zhao, Claire Liu +4
Our study introduces an automated system leveraging large language models (LLMs) to assess the effectiveness of five key tutoring strategies: 1. giving effective praise, 2. reactin…
VTutor: An Open-Source SDK for Generative AI-Powered Animated Pedagogical Agents with Multi-Media Output
Eason Chen, Chenyu Lin, Xinyi Tang +4
The rapid evolution of large language models (LLMs) has transformed human-computer interaction (HCI), but the interaction with LLMs is currently mainly focused on text-based intera…
Comparing Few-Shot Prompting of GPT-4 LLMs with BERT Classifiers for Open-Response Assessment in Tutor Equity Training
Sanjit Kakarla, Conrad Borchers, Danielle Thomas +2
Assessing learners in ill-defined domains, such as scenario-based human tutoring training, is an area of limited research. Equity training requires a nuanced understanding of conte…
Do Tutors Learn from Equity Training and Can Generative AI Assess It?
Danielle R. Thomas, Conrad Borchers, Sanjit Kakarla +5
Equity is a core concern of learning analytics. However, applications that teach and assess equity skills, particularly at scale are lacking, often due to barriers in evaluating la…
Does Multiple Choice Have a Future in the Age of Generative AI? A Posttest-only RCT
Danielle R. Thomas, Conrad Borchers, Sanjit Kakarla +5
The role of multiple-choice questions (MCQs) as effective learning tools has been debated in past research. While MCQs are widely used due to their ease in grading, open response q…
What Should We Engineer in Prompts? Training Humans in Requirement-Driven LLM Use
Qianou Ma, Weirui Peng, Chenyang Yang +3
Prompting LLMs for complex tasks (e.g., building a trip advisor chatbot) needs humans to clearly articulate customized requirements (e.g., "start the response with a tl;dr"). Howev…