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ThinkMind // eLmL 2020, The Twelfth International Conference on Mobile, Hybrid, and On-line Learning // View article elml_2020_3_70_50051


Intelligent Tutoring Systems for Generation Z’s Addiction

Authors:
Ioana R. Goldbach
Felix G. Hamza-Lup

Keywords: intelligent tutoring systems; machine learning; adaptive systems; artificial intelligence

Abstract:
As generation Z’s big data is flooding the Internet through social nets, neural network based data processing is turning an important cornerstone, showing significant potential for fast extraction of data patterns. Online course delivery and associated tutoring are transforming into customizable, on-demand services driven by the learner. Besides automated grading, strong potential exists for the development and deployment of next generation intelligent tutoring software agents. Self-adaptive, online tutoring agents exhibiting “intelligent-like” behavior, being capable “to learn” from the learner, will become the next educational “superstars”. Over the past decade, computer-based tutoring agents were deployed in a variety of extended reality environments, from patient rehabilitation to psychological trauma healing. Most of these agents are driven by a set of conditional control statements and a large answers/questions pairs dataset. This article provides a brief introduction on Generation Z’s addiction to digital information, highlights important efforts for the development of intelligent dialogue systems, and explains the main components and important design decisions for Intelligent Tutoring System.

Pages: 77 to 80

Copyright: Copyright (c) IARIA, 2020

Publication date: March 22, 2020

Published in: conference

ISSN: 2308-4367

ISBN: 978-1-61208-764-1

Location: Valencia, Spain

Dates: from November 21, 2020 to November 25, 2020

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