61 citations · 182 across the 14 of their papers we have counts for
5 papers · 1 filter
Improving Student Learning with Hybrid Human-AI Tutoring: A Three-Study Quasi-Experimental Investigation
Danielle R. Thomas, Jionghao Lin, Erin Gatz +8
Artificial intelligence (AI) applications to support human tutoring have potential to significantly improve learning outcomes, but engagement issues persist, especially among stude…
How to Teach Programming in the AI Era? Using LLMs as a Teachable Agent for Debugging
Qianou Ma, Hua Shen, Kenneth Koedinger +1
Large Language Models (LLMs) now excel at generative skills and can create content at impeccable speeds. However, they are imperfect and still make various mistakes. In a Computer…
Comparative Analysis of GPT-4 and Human Graders in Evaluating Praise Given to Students in Synthetic Dialogues
Dollaya Hirunyasiri, Danielle R. Thomas, Jionghao Lin +2
Research suggests that providing specific and timely feedback to human tutors enhances their performance. However, it presents challenges due to the time-consuming nature of assess…
Using Large Language Models to Provide Explanatory Feedback to Human Tutors
Jionghao Lin, Danielle R. Thomas, Feifei Han +4
Research demonstrates learners engaging in the process of producing explanations to support their reasoning, can have a positive impact on learning. However, providing learners rea…
Is AI the better programming partner? Human-Human Pair Programming vs. Human-AI pAIr Programming
Qianou Ma, Tongshuang Wu, Kenneth Koedinger
The emergence of large-language models (LLMs) that excel at code generation and commercial products such as GitHub's Copilot has sparked interest in human-AI pair programming (refe…