MerryQuery: A Trustworthy LLM-Powered Tool Providing Personalized Support for Educators and Students
Benyamin Tabarsi, Aditya Basarkar, Xukun Liu, Dongkuan (DK) Xu, Tiffany Barnes
Abstract
The potential of Large Language Models (LLMs) in education is not trivial, but concerns about academic misconduct, misinformation, and overreliance limit their adoption. To address these issues, we introduce MerryQuery, an AI-powered educational assistant using Retrieval-Augmented Generation (RAG), to provide contextually relevant, course-specific responses. MerryQuery features guided dialogues and source citation to ensure trust and improve student learning. Additionally, it enables instructors to monitor student interactions, customize response granularity, and input multimodal materials without compromising data fidelity. By meeting both student and instructor needs, MerryQuery offers a responsible way to integrate LLMs into educational settings.
BibTeX
@article{Tabarsi_Basarkar_Liu_Xu_Barnes_2025, title={MerryQuery: A Trustworthy LLM-Powered Tool Providing Personalized Support for Educators and Students}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35372}, DOI={10.1609/aaai.v39i28.35372}, abstractNote={The potential of Large Language Models (LLMs) in education is not trivial, but concerns about academic misconduct, misinformation, and overreliance limit their adoption. To address these issues, we introduce MerryQuery, an AI-powered educational assistant using Retrieval-Augmented Generation (RAG), to provide contextually relevant, course-specific responses. MerryQuery features guided dialogues and source citation to ensure trust and improve student learning. Additionally, it enables instructors to monitor student interactions, customize response granularity, and input multimodal materials without compromising data fidelity. By meeting both student and instructor needs, MerryQuery offers a responsible way to integrate LLMs into educational settings.}, number={28}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Tabarsi, Benyamin and Basarkar, Aditya and Liu, Xukun and Xu, Dongkuan (DK) and Barnes, Tiffany}, year={2025}, month={Apr.}, pages={29700-29702} }