AI-Driven Educational Policy Design: Enhancing Equity and Access through Intelligent Data Analytics
Abstract
Artificial intelligence (AI) is transforming educational policy by enhancing decision-making, optimizing resource distribution, and supporting personalized learning. However, while AI holds the potential to improve accessibility and efficiency, it also raises concerns about algorithmic bias, ethical governance, and the reinforcement of systemic inequities. This study employs a qualitative approach based on secondary data analysis to critically examine AI- driven educational policies and their implications for equity and inclusion. Using the AI-Equity Nexus Model as a conceptual framework, the study explores how AI can serve as both an enabler of educational reform and a potential contributor to disparities in learning opportunities. The findings highlight key challenges, including data privacy concerns, digital divides, and the lack of regulatory oversight, while also identifying best practices for ethical AI integration in education. Policy recommendations emphasize the need for transparent AI governance, equitable digital infrastructure, and AI literacy training for educators and policymakers. This study contributes to the growing discourse on AI in education, offering insights into how AI-driven policies can be structured to promote fairness and accessibility while mitigating unintended biases and exclusionary effects.
Keywords
Artificial intelligence in educationEducational equityAi governance
References
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