Ethics And Fairness In AI-Powered Personalized Learning Systems: Bias, Transparency, And Equity Issues in Adaptive Learning

Artificial Intelligence (AI) is already changing modern education at a rapid pace by introducing personalized learning experiences that change instructional materials and speed and personalized feedback to suit the needs of individual learners. With these benefits, AI continues to be used in education, leading to severe concerns regarding ethical issues, including algorithmic bias, the absence of transparency, and unequal access that jeopardize fairness and inclusivity in adaptive learning systems (Suchithra and Arya, 2025; Zhou et al., 2025). The current literature suggests that AI-based products trained on biased, incomplete, or socially unrepresentative data can lead to unintended discrimination of the existing educational disparities and generate biased learning results (Chinta et al., 2024). Furthermore, opaque black-box algorithms restrict the knowledge of learners and educators about the decision-making process, which undermines the issues of trust, responsibility, and informed involvement in AI-based learning procedures (Holmes et al., 2019; Du and Wang, 2025). The paper is a critical analysis of ethical issues related to AI-based personalized learning based on the review of secondary literature, with the emphasis on the aspects of bias, transparency, and equity. The paper highlights the value of explainable AI, frequent bias audits, and inclusive system design as critical measures that can be taken to enhance fairness. It draws a conclusion that ethical governance systems are required to make sure that AI-enhanced education helps to provide equal learning opportunities instead of increasing existing inequalities