Document Type

Article

Publication Title

International Journal of Multidisciplinary and Current Educational Research

Abstract

The escalating integration of machine learning (ML) in higher education necessitates a critical examination of its ethical implications. This article conducts a comprehensive review of the application of ML for predictive analytics within higher education institutions (HEIs), emphasizing the technology's potential to enhance student outcomes and operational efficiency. The study identifies significant ethical concerns, such as data privacy, informed consent, transparency, and accountability, that arise from the use of ML. Through a detailed analysis of current practices, this review underscores the need for HEIs to develop robust ethical frameworks and technological infrastructures to navigate these challenges effectively. The findings reveal that while ML offers substantial benefits for predictive analytics, such as identifying at-risk students and tailoring educational experiences, it also poses risks that could undermine ethical standards and student trust. The study advocates for a balanced approach to innovation and ethical compliance, suggesting that HEIs must remain vigilant in their ongoing assessment of ML applications. By focusing on these aspects, the review contributes significantly to the discourse on ethical machine learning implementation in higher education, offering actionable recommendations for institutions aiming to leverage technology responsibly.

Publication Date

5-2024

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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