AI-Driven Decision-Making in Educational Leadership: Ethical Implications and Policy Challenges

Abstract

The wide spread adoption of Artificial Intelligence (AI) in educational leadership has transformed the way decisions are made and brought about a level of efficiency never before seen leaving the decisions to data analytics, predictive models, and administrative systems that are largely done automatically. AI in education includes the prediction of student performance and resource distribution, teacher assessments, and created policy by institutions. Such technological change, however, introduces new significant ethical and policy questions such as data risk and bias, exposed accountability penalties, and the possibilities of marginalizing human judgment concerning important educational choices. This paper examines the relevant ethical consequences and policy issues surround AI-powered decision-making in educational, leadership with a emphasis on equity, transparency, and governance. This research is identifying trends in the adoption of AI in schools and universities using a mixed-methods research design that integrates both a quantitative analysis of trends in the research field using survey data and a qualitative survey of educators, policymakers and AI developers. Research questions of interest: (1) What impact does AI have on the leadership decision making process in recruiting, curriculum, and student discipline?  (2) What direction does the research evidence on bias within an AI system indicate, and what effect does this have on the marginalized populations of students?  (3) What current regulatory structures are there and are they sufficient to deal with AI related ethical issues?  It is still in the early days of the research where the researchers claim that although AI improves operational efficiency and data-driven decision-making, the unregulated use of AI threatens to entrench systemic inequities, minimize transparency, and lose trust in education institutions. Case studies indicate that algorithmic decisions have disproportionately impacted minority students and the policy gaps enable third party AI vendors to exploit the data without any control.

Keywords: AI Driven Decision Making, Educational Leadership, AI Policy Challenges, Ethical Implications.