Examining the impact of Generative AI on social sustainability by integrating the information system success model and technology-environmental, economic, and social sustainability theory

Al-Emran, Mostafa and Alsewari, AbdulRahman (2024) Examining the impact of Generative AI on social sustainability by integrating the information system success model and technology-environmental, economic, and social sustainability theory. Education and Information Technologies. ISSN 1360-2357

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Abstract

Generative Artificial Intelligence (AI) refers to advanced systems capable of creating new content by learning from vast datasets, including text, images, and code. These AI tools are increasingly being integrated into various sectors, including education, where they have the potential to enhance learning experiences. While the existing literature has primarily focused on the immediate educational benefits of these tools, such as enhanced learning and efficiency, less attention has been given to how these tools influence broader social sustainability goals, including equitable access and inclusive learning environments. Therefore, this study aims to fill this gap by developing a theoretical research model that combines the information system (IS) success model, technology-environmental, economic, and social sustainability theory (T-EESST), and privacy concerns. To evaluate the developed model, data were collected from 773 university students who were active users of Generative AI and analyzed using the PLS-SEM technique. The findings showed that service quality, system quality, and information quality have a significant positive effect on user satisfaction. Using Generative AI tools is found to be positively affected by user satisfaction. Interestingly, the findings supported the positive role of Generative AI in promoting social sustainability. However, no significant negative correlation was found between privacy concerns and Generative AI use. The findings provide several theoretical contributions and offer insights for various stakeholders in developing, implementing, and managing Generative AI tools in educational settings.

Item Type:	Article
Identification Number:	10.1007/s10639-024-13201-0
Dates:	Date Event 26 November 2024 Accepted 3 December 2024 Published Online
Uncontrolled Keywords:	Generative AI, IS success model, Privacy concerns, Social sustainability, T-EESST
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science CAH11 - computing > CAH11-01 - computing > CAH11-01-02 - information technology CAH11 - computing > CAH11-01 - computing > CAH11-01-03 - information systems CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence CAH22 - education and teaching > CAH22-01 - education and teaching > CAH22-01-01 - education
Divisions:	Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Abdulrahman Alsewari
Date Deposited:	03 Dec 2024 11:20
Last Modified:	03 Dec 2024 11:21
URI:	https://www.open-access.bcu.ac.uk/id/eprint/16008

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