6 papers
Assessing the Impact and Underlying Pathways of Sequenced AI feedback on Student Learning
Jie Cao, Chloe Qianhui Zhao, Christian Schunn +3
Feedback is essential for learning, but its effectiveness relies heavily on how well it engages students in the educational process. Generative AI offers novel opportunities to eff…
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.…
Comparing RAG and GraphRAG for Page-Level Retrieval Question Answering on a Math Textbook
Eason Chen, Chuangji Li, Eric Li +4
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…
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…