AAAI2025
MerryQuery: A Trustworthy LLM-Powered Tool Providing Personalized Support for Educators and Students
Benyamin T. Tabarsi, Aditya Basarkar, Xukun Liu, Dongkuan Xu, Tiffany Barnes
3 citations
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 citations 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.