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Protecting Privacy in AI-Enhanced Education: A Comprehensive Examination of Data Privacy Concerns and Solutions in AI-Based Learning
Abstract
Artificial Intelligence in education has revolutionized learning environments, but it brings significant challenges concerning data privacy and ethical considerations. Through a comprehensive review of contemporary research, the author investigates the types of data collected in AI-driven education, risks, ethical considerations, and potential solutions to address these issues. The chapter presents case studies, including the inBloom initiative and the implementation of learning analytics at the Open University UK, to illustrate real-world privacy challenges and solutions. Our analysis reveals a complex landscape where AI-enhanced education promises improved learning outcomes but also introduces risks related to data breaches, algorithmic bias, and misuse of sensitive information. In response to these challenges, we propose a multi-faceted approach. We recommend that educational institutions develop clear data usage policies, policymakers update regulations to address AI complexities, EdTech developers adopt a “privacy by design” approach, and educators improve their data literacy.
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