The AI Tutor's Dilemma: An Ethical Framework for Personalized Education Systems to Mitigate Data Privacy Risks and Algorithmic Bias
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Background. The integration of Artificial Intelligence (AI) into personalized education systems has the potential to revolutionize learning by providing tailored experiences for students. However, this shift raises significant ethical concerns, particularly regarding data privacy risks and algorithmic bias. AI-driven education systems collect vast amounts of personal data to adapt learning materials to individual needs, but this data usage often comes with risks related to student privacy, security, and the unintended reinforcement of biases.
Purpose. This research aims to develop an ethical framework for personalized AI-based education systems, focusing on strategies to mitigate data privacy risks and prevent algorithmic bias.
Method. A qualitative research approach was employed, combining a review of existing literature, case studies of AI in education, and expert interviews.
Results. The results highlight the critical need for robust data protection measures, transparency in algorithmic decision-making, and continuous monitoring of AI systems to ensure fairness. The study proposes a set of ethical guidelines for designing AI tutors that prioritize student privacy, fairness, and accountability.
Conclusion. In conclusion, this research contributes to the ongoing discourse on the ethical implications of AI in education, offering a framework to guide the development of more equitable and secure AI-powered educational tools.
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