activity
20202026
most citedA New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions

33 citations · 42 across the 9 of their papers we have counts for

collaborators

11 papers

cs.CL2026

Towards Reward Modeling for AI Tutors in Math Mistake Remediation

Kseniia Petukhova, Ekaterina Kochmar

Evaluating the pedagogical quality of AI tutors remains challenging: standard NLG metrics do not determine whether responses identify mistakes, scaffold reasoning, or avoid reveali…

cs.CL2024★ 1 cited

How Teachers Can Use Large Language Models and Bloom's Taxonomy to Create Educational Quizzes

Sabina Elkins, Ekaterina Kochmar, Jackie C. K. Cheung +1

Question generation (QG) is a natural language processing task with an abundance of potential benefits and use cases in the educational domain. In order for this potential to be re…

cs.CL2023★ 3 cited

How Useful are Educational Questions Generated by Large Language Models?

Sabina Elkins, Ekaterina Kochmar, Jackie C. K. Cheung +1

Controllable text generation (CTG) by large language models has a huge potential to transform education for teachers and students alike. Specifically, high quality and diverse ques…

cs.CL2022★ 1 cited

Raising Student Completion Rates with Adaptive Curriculum and Contextual Bandits

Robert Belfer, Ekaterina Kochmar, Iulian Vlad Serban

We present an adaptive learning Intelligent Tutoring System, which uses model-based reinforcement learning in the form of contextual bandits to assign learning activities to studen…

cs.CL2022★ 1 cited

Few-shot Question Generation for Personalized Feedback in Intelligent Tutoring Systems

Devang Kulshreshtha, Muhammad Shayan, Robert Belfer +3

Existing work on generating hints in Intelligent Tutoring Systems (ITS) focuses mostly on manual and non-personalized feedback. In this work, we explore automatically generated que…

cs.CL2022

Question Personalization in an Intelligent Tutoring System

Sabina Elkins, Robert Belfer, Ekaterina Kochmar +2

This paper investigates personalization in the field of intelligent tutoring systems (ITS). We hypothesize that personalization in the way questions are asked improves student lear…

cs.CY2022★ 33 cited

A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions

Francois St-Hilaire, Dung Do Vu, Antoine Frau +28

Despite artificial intelligence (AI) having transformed major aspects of our society, less than a fraction of its potential has been explored, let alone deployed, for education. AI…

cs.CY2021

Comparative Study of Learning Outcomes for Online Learning Platforms

Francois St-Hilaire, Nathan Burns, Robert Belfer +16

Personalization and active learning are key aspects to successful learning. These aspects are important to address in intelligent educational applications, as they help systems to…

cs.CL2021★ 1 cited

Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems

Matt Grenander, Robert Belfer, Ekaterina Kochmar +3

We explore creating automated, personalized feedback in an intelligent tutoring system (ITS). Our goal is to pinpoint correct and incorrect concepts in student answers in order to…

cs.CL2020★ 2 cited

Automated Personalized Feedback Improves Learning Gains in an Intelligent Tutoring System

Ekaterina Kochmar, Dung Do Vu, Robert Belfer +3

We investigate how automated, data-driven, personalized feedback in a large-scale intelligent tutoring system (ITS) improves student learning outcomes. We propose a machine learnin…

cs.CY2020

A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM

Iulian Vlad Serban, Varun Gupta, Ekaterina Kochmar +6

We present Korbit, a large-scale, open-domain, mixed-interface, dialogue-based intelligent tutoring system (ITS). Korbit uses machine learning, natural language processing and rein…