Comparing the Utility, Preference, and Performance of Course Material Search Functionality and Retrieval-Augmented Generation Large Language Model (RAG-LLM) AI Chatbots in Information-Seeking Tasks
Leonardo Pasquarelli, Charles Koutcheme, Arto Hellas

TL;DR
This study compares a course material search function and a RAG-LLM AI chatbot in supporting students' information-seeking tasks, analyzing their effectiveness, user preferences, and perceptions through a controlled user study.
Contribution
It introduces and evaluates a RAG-LLM AI chatbot for educational support and compares its utility and user preferences against traditional search tools in a practical setting.
Findings
Both support mechanisms are useful for specific tasks.
Students showed a preference for the second support mechanism they experienced.
Preferences varied depending on the initial support mechanism provided.
Abstract
Providing sufficient support for students requires substantial resources, especially considering the growing enrollment numbers. Students need help in a variety of tasks, ranging from information-seeking to requiring support with course assignments. To explore the utility of recent large language models (LLMs) as a support mechanism, we developed an LLM-powered AI chatbot that augments the answers that are produced with information from the course materials. To study the effect of the LLM-powered AI chatbot, we conducted a lab-based user study (N=14), in which the participants worked on tasks from a web software development course. The participants were divided into two groups, where one of the groups first had access to the chatbot and then to a more traditional search functionality, while another group started with the search functionality and was then given the chatbot. We assessed…
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Taxonomy
TopicsAI in Service Interactions · Topic Modeling
