Leveraging large language models to support individualized learning
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Lubin, Lindsey
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Abstract
This thesis explores the integration of artificial intelligence (AI) teaching assistants within advanced programming courses to enhance individualized learning and instructor support. The increasing reliance on large language models in programming education has sparked interest in their potential to improve student outcomes. However, challenges remain in effectively implementing AI in classroom environments with more advanced learning outcomes. This thesis presents a demonstration of a large language model (LLM)’s ability to enhance a student’s individual learning experience while providing instructors with enhanced management tools. Key contributions include the development of a dynamic AI teaching assistant integrated into an intro to cybersecurity course framework. The system offers context-aware AI support, code suggestions with guardrails, and summarization of student-AI interactions. Through data analysis, this thesis verifies the effect of course context. This research provides practical insights for educators considering the integration of AI in their classrooms. It proposes future directions for optimizing AI teaching assistants to meet the unique needs of advanced programming courses.
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2025-04-30
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