Question Personalization in an Intelligent Tutoring System

TL;DR

This paper demonstrates that personalized question phrasing in intelligent tutoring systems, tailored to student proficiency levels, enhances learning outcomes, highlighting the importance of linguistic realization in educational technology.

Contribution

It introduces a method for generating personalized question variants based on student proficiency, showing improved learning gains through experimental validation.

Findings

Personalized question phrasing improves student learning outcomes.

Variants written by experts enhance effectiveness.

Linguistic realization impacts ITS effectiveness.

Abstract

This paper investigates personalization in the field of intelligent tutoring systems (ITS). We hypothesize that personalization in the way questions are asked improves student learning outcomes. Previous work on dialogue-based ITS personalization has yet to address question phrasing. We show that generating versions of the questions suitable for students at different levels of subject proficiency improves student learning gains, using variants written by a domain expert and an experimental A/B test. This insight demonstrates that the linguistic realization of questions in an ITS affects the learning outcomes for students.