7 citations · 10 across the 7 of their papers we have counts for
7 papers
"I want to be pushed, I want to grow": Enabling social workers to design evaluations of LLM augmentation in their work
Anna Kawakami, Chloe Qianhui Zhao, Renee Shelby +3
Workers are increasingly asked to adopt AI systems to assist their work, yet are rarely given a voice in defining what meaningful AI augmentation should look like or how to evaluat…
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 +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…
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…