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Title: Fluid Interfaces and Fixed Patterns: Understanding LLM Behavior in Educational Contexts

Presenter and author: Aayush Kucheria

Thesis Supervisor: Nitin Sawhney

Thesis Advisor: Tomi Kauppinen

Abstract: As Large Language Models (LLMs) emerge as potential tutoring agents, they promise more fluid, adaptive educational interactions than traditional intelligent tutoring systems. However, the extent to which LLM behavior actually aligns with human tutoring patterns remains poorly understood. This thesis examines this tension between fluid interfaces and fixed behavioral patterns in AI tutoring. Drawing on constructivist learning theory and analysis of historical constraints in educational technology, we investigate how LLMs process and respond in the tutoring task compared to human teachers. Through systematic analysis of the CIMA dataset, we compare action distributions and response patterns between human tutors and three state-of-the-art LLMs (GPT-4o, Gemini Pro 1.5, and LLaMA 3.1 405B) in language teaching dialogues. Rather than evaluating performance or effectiveness, we focus on understanding fundamental differences in how artificial and human tutors structure their teaching interactions. Our results reveal systematic deviations in LLM behavior from human tutoring patterns, particularly in action selection and response adaptation to student behavior. These findings suggest that while LLMs enable more fluid interaction, they may develop fixed behavioral patterns distinct from human teaching strategies. This research contributes to both theoretical understanding of AI tutoring behavior and practical development of more effective educational technologies, while raising important questions about the nature of machine teaching and learning.

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