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Ruffle&Riley: Insights from Designing and Evaluating a Large Language Model-Based Conversational Tutoring System
This paper presents Ruffle&Riley, an LLM-based conversational tutoring system using a learning-by-teaching format with two AI agents (student and professor), and evaluates it through two online studies (N=200) measuring learning outcomes, engagement, and user experience in biology lessons compared to simpler chatbots and reading activities.
Conversational tutoring systems (CTSs) offer learning experiences through interactions based on natural language. They are recognized for promoting cognitive engagement and improving learning outcomes, especially in reasoning tasks. Nonetheless, the cost associated with authoring CTS content is a major obstacle to widespread adoption and to research on effective instructional design. In this paper, we discuss and evaluate a novel type of CTS that leverages recent advances in large language model