Machine Learning-Driven Educational Ethics Considerations: Striking A Balance Between Privacy And Personalization
Ayasha Siddiqua, Shaista Sabeer, R. Suvarna Rao, Suniana Ahuja, Shikha Aggarwal, Melanie Lourens · 2023
This empirical study investigates the complex trade-off between personalization and privacy in the context of education powered by machine learning. Machine learning is emerging as a transformative force in the rapidly changing field of education, offering personalized as well as adaptive learning experiences. The incorporation of algorithms that can analyze large datasets together with produce insights presents a crucial contradiction that calls for a careful analysis of ethical issues. The study explores the revolutionary possibilities of machine learning, highlighting the manner in which it can be used to customize learning environments to meet the needs of each student. It does, nonetheless, also address privacy issues resulting from large-scale data collection, archiving, and possible abuse. Using a mixed-methods approach, the analysis combines stakeholder insights and quantitative data on customized interventions. Safeguarding privacy, being transparent, as well as obtaining informed consent are essential elements in the moral application of machine learning in education. Legal alongside regulatory measures are examined, with a focus on the necessity of flexible frameworks to meet changing technological challenges. The human element offers important practical insights, as demonstrated by the viewpoints of teachers and students. In the end, this paper emphasizes the importance that it is to work together, conduct continuous ethical analysis, and be dedicated to striking a balance between innovations, privacy, alongside personalization in the constantly changing field of education.