Detecting Student Intent for Chat-Based Intelligent Tutoring Systems

Abstract: Chat interfaces for intelligent tutoring systems (ITSs) enable interactivity and flexibility. However, when students interact with chat interfaces, they expect dialogue-driven navigation from the system and can express frustration and disinterest if this is not provided. Intent detection systems help students navigate within an ITS, but detecting students' intent during open-ended dialogue is challenging. We designed an intent detection system in a chatbot ITS, classifying a student's intent between continuing the current lesson or switching to a new lesson. We explore the utility of four machine learning approaches for this task - including both conventional classification approaches and fine-tuned LLMs - finding that using an intent classifier introduces trade-offs around implementation cost, accuracy, and prediction time. We argue that implementing intent detection in chat interfaces can reduce frustration and support student learning.