Detecting Student Intent for Chat-Based Intelligent Tutoring Systems

TL;DR

This paper presents an intent detection system for chat-based intelligent tutoring systems that classifies whether students want to continue or switch lessons, aiming to improve user experience and learning outcomes.

Contribution

It introduces a novel intent detection approach using various machine learning models, including large language models, for dialogue management in ITS chat interfaces.

Findings

Intent classifiers impact accuracy and response time.

Fine-tuned large language models offer higher accuracy.

Implementation trade-offs include cost and speed.

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 large language models - 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.