THE ETHICAL DILEMMAS OF AI-DRIVEN DECISION-MAKING IN EDUCATIONAL MANAGEMENT: A FRAMEWORK OF ACCOUNTABILITY

Abstract

Artificial intelligence (AI) is rapidly reshaping educational management from admissions and student-support triage to predictive analytics and automated assessment. These AI-driven decisions promise efficiency, personalization, and scalability but also raise significant ethical dilemmas: bias and discrimination, opacity and explainability, data privacy, erosion of professional autonomy, and weak redress mechanisms. This paper maps the major ethical problems that arise when educational institutions deploy AI for decisions affecting learners and staff, reviews relevant principles and regulations, and proposes a practical, multi-layered accountability framework that institutions can adopt. The proposed framework combines governance, technical measures (explainability and audits), human oversight, impact assessment and remediation channels, and stakeholder participation to ensure responsible AI use that protects rights and supports educational aims. Key recommendations include mandatory impact assessments, transparent documentation, independent audit trails, clear responsibility lines, and accessible remedies for affected individuals.