Asian Journal of Current Research

This review investigates the drawbacks of AI-driven personalization on student outcomes in higher education. The primary objective is to understand how AI implementation affects equity and learning, addressing gaps in ethical and practical implications. Previous research highlights AI’s potential to tailor learning through adaptive platforms and predictive analytics. However, there is limited synthesis of its downsides, such as privacy breaches and biased outcomes. This review examines these challenges to inform equitable AI use. A literature review approach was used by selecting studies certain based on their direct relevance to AI personalization in higher education and their coverage of data privacy, algorithmic bias, human interaction, and access inequity. Data were analyzed thematically to identify trends and challenges. Findings reveal significant drawbacks such as data privacy concerns stemming from inadequate governance, algorithmic bias favoring privileged groups such as lower STEM recommendations for females, reduced human interaction in resulting low satisfaction, while inequitable access exacerbating the digital divide for low-income students. Corresponding ethical solutions include adopting transparent data governance frameworks implementing diverse training datasets with regular bias audits developing hybrid AI-instructor teaching models and investing in inclusive infrastructure to ensure equitable access. AI-driven personalization significantly impacts equity and engagement in higher education, necessitating ethical frameworks and inclusive practices.