A chatbot-server framework for scalable machine learning education through crowdsourced data

Author(s)
Author(s)
Li, J.
Tan, C. W.
Qi, X.
Date Issued
2022
Publisher
Association for Computing Machinery
Related Publication(s)
Proceedings of the Ninth ACM Conference on Learning@Scale (L@S '22)
Start page
271
End page
274
Abstract
In this paper, we propose a novel chatbot-server computer programming framework for students to learn Artificial Intelligence (AI) by creating game AI chatbot applications, whilst conforming to a distributed frontend-backend application structure (e.g., client-server model). The chatbot interface allows students to share their work over online social networks and invite other human players to test-drive the game AI and to collect data for training of machine learning models by crowdsourcing. We introduce a few test cases in which the framework facilitates the online learning of AI, introduces full-stack software development to students and enables a progressive learning of machine learning education using crowdsourcing.
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DOI
SFU Affiliated Publication
No
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