ALGORITHMIC CURATION IN EDUCATION: BALANCING PERSONALIZATION, EQUITY, AND TRANSPARENCY IN DATA-DRIVEN LEARNING SYSTEMS
DOI:
https://doi.org/10.5281/zenodo.19762306Abstract
This thesis analyzes the role, opportunities, and challenges of algorithmic curation in digital learning environments. The research highlights the complex balance between ensuring personalized learning, maintaining educational equity, and ensuring algorithmic transparency in data-driven learning systems. It also examines the adaptation of learning platforms developed based on artificial intelligence and data analytics to student needs, as well as issues such as social justice, the risk of discrimination, and data privacy.
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