A Statistical Analysis to Investigate the Factors Affecting Generative AI Use in Education and Its Impacts on Social Sustainability Using SPSS

The emergence of generative AI, as a relatively new technological advancement in education, presents new opportunities, but its integration and effects on society remain uncertain. This research focuses on the key factors that affect generative AI in students’ learning integration and its impact on social sustainability. The central problem is the lack of understanding regarding integrating generative AI in education and its broader social sustainability implications. It further employs online surveys targeting higher education students to gather quantitative data. The survey is designed to be composed of closed-ended questions that will focus on the factors of the IDT, SCT, and SDT theories as presented in the theoretical model. This will be done using descriptive analysis, internal consistency reliability, exploratory factor analysis, and multiple regression analysis using SPSS to find out the factors that define the use and adoption of generative AI and its effects on other social sustainability factors like education, diversity, and readiness. The study therefore assists in filling gaps within the literature on AI in education and is beneficial for students, policymakers, educators, and academics. It will highlight and shed light on policies and standards in the use of generative AI in education for the betterment of society. Besides, as the concept of generative AI is advancing in the future, this work will help to form the basis for further research and applications in the field of education while emphasizing the values of technological, pedagogical, and social responsibility.