A Chatbot-Server Framework for Scalable Machine Learning Education through Crowdsourced Data.

ACM Conference on Learning @ Scale (L@S)(2022)

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摘要
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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