Assessing generative A.I. through the lens of the 2023 Gartner Hype Cycle for Emerging Technologies: a collaborative autoethnography

Article


King, S. and Prasetyo, J. 2023. Assessing generative A.I. through the lens of the 2023 Gartner Hype Cycle for Emerging Technologies: a collaborative autoethnography. Frontiers in Education. 8. https://doi.org/10.3389/feduc.2023.1300391
TypeArticle
TitleAssessing generative A.I. through the lens of the 2023 Gartner Hype Cycle for Emerging Technologies: a collaborative autoethnography
AuthorsKing, S. and Prasetyo, J.
Abstract

This brief research report examines claims made across contemporary media channels that generative artificial intelligence can be used to develop educational materials, in an experiment to develop a new course for advertising, PR and branding professionals. A collaborative auto-ethnography is employed to examine the journey and unintended consequences experienced by a non-technology lecturer engaging with generative AI for the first-time and is examined under the lens of the 2023 Gartner Hype Cycle for Emerging Technologies. The researchers were able to map lived experiences to stages of the Gartner model, presenting evidence that this tool could have extended utility in the field of human resources for the support of technology integration projects. They also recorded several potential manifestations of symptoms related to the problematic use of Internet (PUI). The implications of the findings contribute to ongoing public discourse regarding the introduction of artificial intelligence within education, with insights for policy development and governance, as well as faculty and student wellbeing.

Keywordsgenerative A.I.; problematic use of Internet; autoethnography; Gartner Technology Hype Cycle; curriculum design
Sustainable Development Goals9 Industry, innovation and infrastructure
Middlesex University ThemeCreativity, Culture & Enterprise
PublisherFrontiers Media
JournalFrontiers in Education
ISSN
Electronic2504-284X
Publication dates
Online01 Dec 2023
Print01 Dec 2023
Publication process dates
Submitted03 Oct 2023
Accepted03 Nov 2023
Deposited13 Jan 2025
Output statusPublished
Publisher's version
License
File Access Level
Open
Copyright Statement

Copyright © 2023 King and Prasetyo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Digital Object Identifier (DOI)https://doi.org/10.3389/feduc.2023.1300391
Web of Science identifierWOS:001124517000001
LanguageEnglish
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