Automated Personalized Feedback Improves Learning Gains in an Intelligent Tutoring System

Ekaterina Kochmar, Dung Do Vu, Robert Belfer, Varun Gupta, Iulian Vlad Serban, Joelle Pineau

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

25 Citations (SciVal)


We investigate how automated, data-driven, personalized feedback in a large-scale intelligent tutoring system (ITS) improves student learning outcomes. We propose a machine learning approach to generate personalized feedback, which takes individual needs of students into account. We utilize state-of-the-art machine learning and natural language processing techniques to provide the students with personalized hints, Wikipedia-based explanations, and mathematical hints. Our model is used in Korbit (, a large-scale dialogue-based ITS with thousands of students launched in 2019, and we demonstrate that the personalized feedback leads to considerable improvement in student learning outcomes and in the subjective evaluation of the feedback.
Original languageEnglish
Title of host publicationAIED 2020: Artificial Intelligence in Education
EditorsI. Bittencourt, M. Cukurova, K. Muldner, R. Luckin, E. Millan
Number of pages7
ISBN (Electronic)978-3-030-52240-7
ISBN (Print)978-3-030-52239-1
Publication statusE-pub ahead of print - 30 Jun 2020
EventAIED 2020: The 2020 conference on Artificial Intelligence in Education - Virtual, Ifrane, Morocco
Duration: 6 Jul 202010 Jul 2020

Publication series

NameLecture Notes in Computer Science
PublisherSpringer, Cham.


ConferenceAIED 2020
Abbreviated titleAIED 2020
Internet address


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