Toward Human-Centered Artificial Intelligence in Education: Adaptive Learning Models for Personalized and Equitable Academic Outcomes
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This research paper examines the integration of human-centered principles into adaptive learning systems with the objective of achieving personalized and equitable academic outcomes. The study addresses technical, ethical, and pedagogical aspects of utilizing artificial intelligence (AI) in education. Through a comprehensive literature review and mixed-methods research—including quantitative surveys, qualitative interviews, and analysis of system logs—the paper demonstrates how adaptive learning models can enhance student engagement, improve academic performance, and reduce disparities in learning access. The ethical challenges of privacy, algorithmic transparency, digital inequity, and potential biases are carefully analyzed, and mitigation strategies are proposed. Our findings indicate that while AI-based educational tools offer substantial benefits in personalization and efficiency, a human-centered approach that integrates ethical oversight and stakeholder engagement is essential for ensuring equitable outcomes. The paper concludes with recommendations for policymakers and educators, proposing a framework for future research that centers human values and equitable resource distribution.
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