Exploring the Implications of Emerging Artificial Intelligence Technologies at Edge Computing in Higher Education

Exploring the Implications of Emerging Artificial Intelligence Technologies at Edge Computing in Higher Education

Omega John Unogwu, Ruchi Doshi, Kamal Kant Hiran, Maad M. Mijwil, Ankar Tersoo Catherine, Mostafa Abotaleb
Copyright: © 2024 |Pages: 14
DOI: 10.4018/979-8-3693-2440-0.ch009
OnDemand:
(Individual Chapters)
Available
$37.50
Current Special Offers
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

In this chapter, the effects of cutting-edge artificial intelligence (AI) technologies at edge computing are examined in higher education. Edge computing offers a decentralized method of computing in which processing is done near the data source. Due to less network traffic, response times can be quicker. AI technology can be implemented at the edge to offer instructors and students intelligent and individualized services. The chapter addresses the advantages of edge computing and AI in higher education, including enhanced student involvement, better learning results, and simplified administrative procedures. It also looks at the difficulties of implementing AI at the edge, such as data privacy and security issues. To fully fulfill the potential of AI, the article's conclusion emphasizes the necessity for additional study in this field.
Chapter Preview
Top

The Current State Of Ai In Higher Institutions

The world we live in is rapidly evolving due to artificial intelligence (AI), and higher education is no exception. Artificial intelligence (AI) is becoming a crucial component of higher education due to its ability to improve teaching and learning processes. Researchers are looking into the potential for AI to customize education and increase student success rates, and institutions are already employing it to help jobs like student recruiting, academic counseling, and administrative procedures (Luan et al., 2020). AI applications, however, may face difficulties with conventional centralized computer models. Centralized approaches rely on sending massive amounts of data to a single place for processing, which can cause bottlenecks, excessive latency, and delayed response times. Furthermore, centralized models can present security and privacy risks because private information may be processed and stored on distant servers. These issues are particularly pressing when it comes to higher education, where data security and privacy are crucial factors.

Benefits Of Artificial Intelligence in Higher Institutions

The creation of individualized learning experiences is one of the key uses of AI in higher education. Institutions can examine student data and behavior using machine learning algorithms to offer individualized suggestions and interventions that encourage student achievement. For example, AI systems can review student performance data and suggest individualized learning paths, or they can identify students who are at risk and offer them focused interventions to increase their chances of success (Bonderud, 2021; Doshi et al., 2023).

The creation of intelligent campus systems is another area in which AI is being applied in higher education. To gather data on the campus surroundings, these systems use sensors and other data sources. They then use AI algorithms to optimize their operations and boost productivity. By analyzing data from sensors and other sources in real-time, intelligent campus systems can, for example, optimize energy use, save waste, and increase safety.

Research and innovation in higher education are also using AI technologies. For instance, machine learning algorithms can analyze huge datasets to find patterns and insights that might help study in a variety of sectors. The development of new apps and services that can improve teaching and learning and raise student engagement, like intelligent tutoring systems, virtual assistants, and chatbots, is another benefit of AI technologies.

Complete Chapter List

Search this Book:
Reset