Ruffle&Riley: From Lesson Text to Conversational Tutoring

Robin Schmucker, Meng Xia, Amos Azaria, Tom Mitchell

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Conversational tutoring systems (CTSs) offer learning experiences driven by natural language interactions. They are recognized for promoting cognitive engagement and improving learning outcomes, especially in reasoning tasks. Ruffle&Riley is a novel type of CTS that explores the potential of LLMs for efficient AI-assisted content authoring and for facilitating structured free-form conversational tutoring. This interactive event enables participants to engage with the LLM-based CTS introduced in our recent AIED2024 paper in two ways: (1) Attendees will interact with the web application using their personal devices. (2) Attendees will learn how to import learning materials into the system and generate custom tutoring scripts through a detailed tutorial. Ruffle&Riley is an extendable, open-source framework that promotes research on effective instructional design of LLM-based learning technologies. The interactive event will foster related discussions.

Original languageEnglish
Title of host publicationL@S 2024 - Proceedings of the 11th ACM Conference on Learning @ Scale
Pages547-549
Number of pages3
ISBN (Electronic)9798400706332
DOIs
StatePublished - 9 Jul 2024
Event11th ACM Conference on Learning @ Scale, L@S 2024 - Atlanta, United States
Duration: 18 Jul 202420 Jul 2024

Publication series

NameL@S 2024 - Proceedings of the 11th ACM Conference on Learning @ Scale

Conference

Conference11th ACM Conference on Learning @ Scale, L@S 2024
Country/TerritoryUnited States
CityAtlanta
Period18/07/2420/07/24

Keywords

  • authoring tools
  • conversational tutoring systems
  • intelligent tutoring systems
  • large language models

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