A Design Framework for Building Intelligent Tutoring Systems Based on Learning Science Principles

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dc.contributor.advisorBaraniuk, Richarden_US
dc.creatorLiu, Lucyen_US
dc.date.accessioned2024-08-30T15:50:31Zen_US
dc.date.available2024-08-30T15:50:31Zen_US
dc.date.created2024-05en_US
dc.date.issued2024-07-22en_US
dc.date.submittedMay 2024en_US
dc.date.updated2024-08-30T15:50:31Zen_US
dc.description.abstractIntelligent Tutoring Systems (ITS) have gained popularity due to their ability to provide students with cost-effective and personalized learning experience. The development of Large Language Models (LLMs) offer great potential to revolutionize personalized education to autonomously create effective ITS without relying on manual, labor intensive processes. Our research introduces a novel design framework, named Conversational Learning with Analytical Step-by-Step Strategies (CLASS), which harnesses the potential of high-performance LLMs to construct advanced ITS. The CLASS framework empowers ITS with two critical capabilities. First, through a carefully curated scaffolding dataset, CLASS provides essential problem-solving capabilities, enabling it to provide tutor-like, step-by-step guidance to students. Second, by using a dynamic conversational dataset, CLASS facilitates natural language interactions, which fosters engaging student-tutor conversations. The CLASS framework also enhances the transparency of ITS, offering valuable insights into its internal decision-making process. This interpretability allows seamless integration of user feedback, thus facilitating continuous refinement and improvement. Additionally, we present a practical application of the CLASS framework through a proof-of-concept ITS, with a focus on introductory college-level biology content. A carefully constructed protocol was developed for the biology ITS'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 its capability to break down questions into manageable subproblems and provide encouraging responses to students.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationLiu, Lucy. A Design Framework for Building Intelligent Tutoring Systems Based on Learning Science Principles. (2024). Masters thesis, Rice University. https://hdl.handle.net/1911/117762en_US
dc.identifier.urihttps://hdl.handle.net/1911/117762en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectintelligent tutoring systemsen_US
dc.subjectlearning science principlesen_US
dc.subjectlarge language modelsen_US
dc.titleA Design Framework for Building Intelligent Tutoring Systems Based on Learning Science Principlesen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentElectrical and Computer Engineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Scienceen_US
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