CLASS: A Design Framework for Building Intelligent Tutoring Systems Based on Learning Science principles

Shashank Sonkar, Naiming Liu, Debshila Mallick, Richard Baraniuk

Abstract
We present a design framework called Conversational Learning with Analytical Step-by-Step Strategies (CLASS) for building advanced Intelligent Tutoring Systems (ITS) powered by high-performance Large Language Models (LLMs). The CLASS framework empowers ITS with two key capabilities. First, through a carefully curated scaffolding dataset, CLASS equips ITS with essential problem-solving strategies, enabling it to provide tutor-like, step-by-step guidance to students. Second, by using a dynamic conversational dataset, CLASS assists ITS in facilitating natural language interactions, fostering engaging student-tutor conversations. The CLASS framework also provides valuable insights into ITS’s internal decision-making process which allows seamless integration of user feedback, thus enabling continuous refinement and improvement. We also present a proof-of-concept ITS, referred to as SPOCK, which is trained using the CLASS framework with a focus on introductory college level biology content. A carefully constructed protocol was developed for SPOCK’s preliminary evaluation, examining aspects such as the factual accuracy and relevance of its responses. Experts in the field of biology offered favorable remarks, particularly highlighting SPOCK’s capability to break down questions into manageable subproblems and provide encouraging responses to students.
Anthology ID:
2023.findings-emnlp.130
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1941–1961
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.130
DOI:
10.18653/v1/2023.findings-emnlp.130
Bibkey:
Cite (ACL):
Shashank Sonkar, Naiming Liu, Debshila Mallick, and Richard Baraniuk. 2023. CLASS: A Design Framework for Building Intelligent Tutoring Systems Based on Learning Science principles. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 1941–1961, Singapore. Association for Computational Linguistics.
Cite (Informal):
CLASS: A Design Framework for Building Intelligent Tutoring Systems Based on Learning Science principles (Sonkar et al., Findings 2023)
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PDF:
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