@inproceedings{afzal-etal-2019-development,
title = "Development and Deployment of a Large-Scale Dialog-based Intelligent Tutoring System",
author = "Afzal, Shazia and
Dhamecha, Tejas and
Mukhi, Nirmal and
Sindhgatta, Renuka and
Marvaniya, Smit and
Ventura, Matthew and
Yarbro, Jessica",
editor = "Loukina, Anastassia and
Morales, Michelle and
Kumar, Rohit",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-2015",
doi = "10.18653/v1/N19-2015",
pages = "114--121",
abstract = "There are significant challenges involved in the design and implementation of a dialog-based tutoring system (DBT) ranging from domain engineering to natural language classification and eventually instantiating an adaptive, personalized dialog strategy. These issues are magnified when implementing such a system at scale and across domains. In this paper, we describe and reflect on the design, methods, decisions and assessments that led to the successful deployment of our AI driven DBT currently being used by several hundreds of college level students for practice and self-regulated study in diverse subjects like Sociology, Communications, and American Government.",
}
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<abstract>There are significant challenges involved in the design and implementation of a dialog-based tutoring system (DBT) ranging from domain engineering to natural language classification and eventually instantiating an adaptive, personalized dialog strategy. These issues are magnified when implementing such a system at scale and across domains. In this paper, we describe and reflect on the design, methods, decisions and assessments that led to the successful deployment of our AI driven DBT currently being used by several hundreds of college level students for practice and self-regulated study in diverse subjects like Sociology, Communications, and American Government.</abstract>
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%0 Conference Proceedings
%T Development and Deployment of a Large-Scale Dialog-based Intelligent Tutoring System
%A Afzal, Shazia
%A Dhamecha, Tejas
%A Mukhi, Nirmal
%A Sindhgatta, Renuka
%A Marvaniya, Smit
%A Ventura, Matthew
%A Yarbro, Jessica
%Y Loukina, Anastassia
%Y Morales, Michelle
%Y Kumar, Rohit
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F afzal-etal-2019-development
%X There are significant challenges involved in the design and implementation of a dialog-based tutoring system (DBT) ranging from domain engineering to natural language classification and eventually instantiating an adaptive, personalized dialog strategy. These issues are magnified when implementing such a system at scale and across domains. In this paper, we describe and reflect on the design, methods, decisions and assessments that led to the successful deployment of our AI driven DBT currently being used by several hundreds of college level students for practice and self-regulated study in diverse subjects like Sociology, Communications, and American Government.
%R 10.18653/v1/N19-2015
%U https://aclanthology.org/N19-2015
%U https://doi.org/10.18653/v1/N19-2015
%P 114-121
Markdown (Informal)
[Development and Deployment of a Large-Scale Dialog-based Intelligent Tutoring System](https://aclanthology.org/N19-2015) (Afzal et al., NAACL 2019)
ACL
- Shazia Afzal, Tejas Dhamecha, Nirmal Mukhi, Renuka Sindhgatta, Smit Marvaniya, Matthew Ventura, and Jessica Yarbro. 2019. Development and Deployment of a Large-Scale Dialog-based Intelligent Tutoring System. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers), pages 114–121, Minneapolis, Minnesota. Association for Computational Linguistics.