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Ruffle&Riley: From Lesson Text to Conversational Tutoring

Published: 15 July 2024 Publication History

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.

References

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Vincent Aleven, Bruce M McLaren, Jonathan Sewall, Martin Van Velsen, Octav Popescu, Sandra Demi, Michael Ringenberg, and Kenneth R Koedinger. 2016. Example-tracing tutors: Intelligent tutor development for non-programmers. International Journal of Artificial Intelligence in Education, Vol. 26 (2016), 224--269.
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Mary A Clark, Matthew Douglas, and Jung Choi. 2018. Biology 2e. OpenStax, Houston, TX, USA.
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David Duran. 2017. Learning-by-teaching. Evidence and implications as a pedagogical mechanism. Innovations in Education and Teaching International, Vol. 54, 5 (2017), 476--484.
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Arthur C Graesser, Shulan Lu, George Tanner Jackson, Heather Hite Mitchell, Mathew Ventura, Andrew Olney, and Max M Louwerse. 2004. AutoTutor: A tutor with dialogue in natural language. Behavior Research Methods, Instruments, & Computers, Vol. 36 (2004), 180--192.
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Arthur C Graesser, Natalie K Person, and Joseph P Magliano. 1995. Collaborative dialogue patterns in naturalistic one-to-one tutoring. Applied cognitive psychology, Vol. 9, 6 (1995), 495--522.
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Xudong Huang, Scotty D. Craig, Jun Xie, Arthur Graesser, and Xiangen Hu. 2016. Intelligent tutoring systems work as a math gap reducer in 6th grade after-school program. Learning and Individual Differences, Vol. 47 (2016), 258--265.
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James A. Kulik and J. D. Fletcher. 2016. Effectiveness of Intelligent Tutoring Systems: A Meta-Analytic Review. Review of Educational Research, Vol. 86, 1 (2016), 42--78.
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Krittaya Leelawong and Gautam Biswas. 2008. Designing learning by teaching agents: The Betty's Brain system. International Journal of Artificial Intelligence in Education, Vol. 18, 3 (2008), 181--208.
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OpenAI. 2023. GPT-4 Technical Report. arxiv: 2303.08774 [cs.CL]
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Robin Schmucker, Meng Xia, Amos Azaria, and Tom Mitchell. 2024. Ruffle&Riley: Insights from Designing and Evaluating a Large Language Model-Based Conversational Tutoring System. In Proceedings of the 25th International Conference on Artificial Intelligence in Education (AIED2024). Springer, Cham, 1--14.

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  1. Ruffle&Riley: From Lesson Text to Conversational Tutoring

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    Published In

    cover image ACM Other conferences
    L@S '24: Proceedings of the Eleventh ACM Conference on Learning @ Scale
    July 2024
    582 pages
    ISBN:9798400706332
    DOI:10.1145/3657604
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 15 July 2024

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    Author Tags

    1. authoring tools
    2. conversational tutoring systems
    3. intelligent tutoring systems
    4. large language models

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    Overall Acceptance Rate 117 of 440 submissions, 27%

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