AI Economy and Higher Education

Coulter-Smith, Liz ORCID: https://orcid.org/0000-0003-4228-8105 (2022) AI Economy and Higher Education. In: In Digital Transformation and Disruption of Higher Education. Cambridge University Press, Cambridge, England, pp. 391-404. ISBN 9781108979146

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Abstract / Summary

This chapter focuses on four leading AI economies: China, the EU, the United States and the United Kingdom. We are interested in how these economic AI plans impact higher education (HE). Universities are critical to the workforce and, therefore, the financial health of a country. Nevertheless, are they ready to contribute to this AI economy? Are our governments preparing for the futuristic and ultramodern approach being adopted in, for instance, China? What will be the consequences if higher education falls far behind in some and not others? Some governments are (and have) made numerous alliances with large multinational industries, including Google, Microsoft, Facebook, Amazon, Huawei, Baidu and Alibaba, amongst the most prominent. Later in this chapter, we will take a look at some of these partnerships and the future thinking and planning taking place. The strategic plans, and approach to partnerships, differ in depth, substance and persuasive style in the documentation we are relying on. Some of these differences will alter how our universities adapt, plan and develop the curriculum necessary for a robust AI economy. Higher education has a critical role to play in this economic shift and is in fact at a ‘crossroads of disruption’as Kaplan suggests (Kaplan 2021). Embracing the AI economy is broadly considered vital and transformational across all sectors of economic productivity and particularly as we recover from the COVID-19 crisis. This ‘economic shift’cannot be understated as Chapter 1 clearly points out.

Item Type:	Book Section
ISBN:	9781108979146
Subjects:	Business Computer Science, Information & General Works Science
Courses by Department:	The Games Academy > Computing for Games
Depositing User:	Liz Coulter-Smith
Date Deposited:	04 Jan 2024 16:16
Last Modified:	04 Jan 2024 16:16
URI:	https://repository.falmouth.ac.uk/id/eprint/4848

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