Question Personalization in an Intelligent Tutoring System

Sabina Elkins, Ekaterina Kochmar, Robert Belfer, Iulian Serban, Jackie C.K. Cheung

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding


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.

Original languageEnglish
Title of host publicationArtificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium.
Subtitle of host publicationAIED 2022
EditorsMaria Mercedes Rodrigo, Noburu Matsuda, Alexandra I. Cristea, Vania Dimitrova
Place of PublicationCham, Switzerland
Number of pages5
ISBN (Print)9783031116469
Publication statusPublished - 26 Jul 2022
Event23rd International Conference on Artificial Intelligence in Education, AIED 2022 - Durham, UK United Kingdom
Duration: 27 Jul 202231 Jul 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13356 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference23rd International Conference on Artificial Intelligence in Education, AIED 2022
Country/TerritoryUK United Kingdom


  • Dialogue-based tutoring system
  • Intelligent tutoring system
  • Personalized learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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