@inproceedings{3759726c1d0c4ff29b0fd6c82b3f427e,
title = "Ruffle&Riley: From Lesson Text to Conversational Tutoring",
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.",
keywords = "authoring tools, conversational tutoring systems, intelligent tutoring systems, large language models",
author = "Robin Schmucker and Meng Xia and Amos Azaria and Tom Mitchell",
note = "Publisher Copyright: {\textcopyright} 2024 Owner/Author.; 11th ACM Conference on Learning @ Scale, L@S 2024 ; Conference date: 18-07-2024 Through 20-07-2024",
year = "2024",
month = jul,
day = "9",
doi = "https://doi.org/10.1145/3657604.3664719",
language = "الإنجليزيّة",
series = "L@S 2024 - Proceedings of the 11th ACM Conference on Learning @ Scale",
pages = "547--549",
booktitle = "L@S 2024 - Proceedings of the 11th ACM Conference on Learning @ Scale",
}