Ethical Challenges of Artificial Intelligence in Education: A Systematic Review (2020–2025)
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Abstract
Artificial Intelligence (AI) has recently emerged as a key driver of educational innovation, supporting personalized learning and optimizing teaching processes. However, its integration raises major ethical challenges, including data privacy, equitable access, algorithmic transparency, and accountability. To examine this issue rigorously, a systematic review of literature published between 2020 and 2025 was conducted in high-impact databases (Scopus, Web of Science, ERIC, Dialnet, and Google Scholar), following PRISMA guidelines. After applying predefined inclusion and exclusion criteria, 28 studies were selected for analysis. The results reveal six recurrent concerns: data protection, algorithmic bias, lack of transparency, unclear accountability, the risk of educational dehumanization, and the widening digital divide. In addition, the review highlights the lack of shared ethical frameworks and the scarcity of validated empirical tools to evaluate these challenges. The study concludes that it is necessary to develop educational policies, teacher training programs, and assessment instruments that ensure the ethical, inclusive, and socially responsible use of artificial intelligence in education.
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