IT Management Strategies for Scaling Artificial Intelligence-Powered Educational Systems in American Schools and Universities

Jannatul Ferdousmou; Md Samiun; Nur Mohammad; Md Zikar Hossan; Srabani Das; Mahafuj Hassan; Abdul Quddus Mozumder; Samia Hasan Suha

doi:10.63332/joph.v5i2.433

Authors

Jannatul Ferdousmou Department of Business Administration, International American University, United States
Md Samiun Department of Business Administration, International American University, United States
Nur Mohammad Department of Information Technology, Westcliff University, United States
Md Zikar Hossan Department of Business Administration, International American University, United States
Srabani Das Department of Education, Westcliff University, United States
Mahafuj Hassan Department of Business Administration, International American University, United States
Abdul Quddus Mozumder Department of Information System Management, Stanton University, California, United States
Samia Hasan Suha Department of Business Administration, International American University, United States

DOI:

https://doi.org/10.63332/joph.v5i2.433

Keywords:

Artificial Intelligence, IT Management, Data Privacy, Institutional Readiness

Abstract

Both the adoption and AI integration of education means that there are significant possibilities for education through scalable systems of AI education. The following research focuses on the utilization of IT management strategies in dealing with the issue, and enhancing scalability of institutions in America, namely schools and universities. This cross-sectional survey study employs a quantitative research approach and a sample of about 400 participants to test the impact of institutional readiness, educator technical proficiency, and data privacy on scalability, mediated by government funding and policy. Outcomes confirm that data protection and governmental grants are the key factors for trust enhancement, resources’ equalization, and extensive changes. Moreover, the findings show that the measures of institutional readiness and external support are not independent variables; instead, they should be discussed as related, yet relatively separate constructs that should be combined and optimized properly. This research advances the understanding of multifaceted practice-based frameworks for the fair and effective implementation of AI solutions so that varied needs are fulfilled, learning and teaching are enhanced, and accessibility is achieved.