Leveraging AI for Data-Driven Decision Making and Automation in the USA Education Sector

Abstract

This research explores how artificial intelligence (AI) can be applied to the U.S. education sector to improve decision-making and automation using data. It aims to examine the impact of AI on institutional efficiency, pedagogical effectiveness, and personalized learning, while also discussing the challenges associated with its adoption, such as cost, technological capabilities, and ethical concerns. The quantitative research design was used, where survey was conducted through a survey questionnaire which was delivered through Google Forms and hard copy questionnaires. The sample was made up of 350 respondents who were teachers, administrators, IT personnel, policymakers, and students from various educational institutions. Descriptive statistics were used to process the data and were presented as tables, bar charts, pie chart, donut charts. Internal consistency of the instrument was established by way of Cronbach s Alpha, which is used in establishing reliability. The findings indicate that 60 percent of institutions have already implemented AI applications, and the most popular are student performance analytics (33.3) and administrative automation (26.2). Most of the respondents supported the idea that AI enhanced decision-making (62.9%), learning outcomes (68.5%), and decreased the organizational workload (60%). However, barriers such as high implementation costs (28.6%), lack of technical expertise (25.7%), and concerns over data privacy (20%) remain significant. Algorithms bias and fairness were also considered highly in terms of ethics. Still, when it comes to how AI ought to be used, 71.4 percent of respondents agreed that more institutional resources should be directed toward AI, with personalized learning (34.3 percent) becoming the area that would most benefit over the next five years. Because of the gap between the potential of AI application in the education field and the actual implementation of AI in education, the present study can be added to the existing literature. It provides empirical data concerning stakeholder perception of opportunities and challenges. The findings offer significant implications for policymakers, administration, and educators who seek to use AI in an ethical and inclusive way to improve the efficiency of the institution, student experience, and educational equity.