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Conversational tutoring systems (CTSs) offer learning experiences driven bynatural language interaction. They are known to promote high levels ofcognitive engagement and benefit learning outcomes, particularly in reasoningtasks. Nonetheless, the time and cost required to author CTS content is a majorobstacle to widespread adoption. In this paper, we introduce a novel type ofCTS that leverages the recent advances in large language models (LLMs) in twoways: First, the system induces a tutoring script automatically from a lessontext. Second, the system automates the script orchestration via two LLM-basedagents (Ruffle&Riley) with the roles of a student and a professor in alearning-by-teaching format. The system allows a free-form conversation thatfollows the ITS-typical inner and outer loop structure. In an initialbetween-subject online user study (N = 100) comparing Ruffle&Riley to simplerQA chatbots and reading activity, we found no significant differences inpost-test scores. Nonetheless, in the learning experience survey, Ruffle&Rileyusers expressed higher ratings of understanding and remembering and furtherperceived the offered support as more helpful and the conversation as coherent.Our study provides insights for a new generation of scalable CTS technologies.

Ruffle&Riley: Towards the Automated Induction of Conversational Tutoring Systems