The shadow of the algorithm: the ethical blind spot of artificial intelligence education

Abstract

This paper explores the ethical blind spots of artificial intelligence (AI) in the field of education, with a focus on algorithmic opacity, privacy issues, and societal biases. Regarding algorithmic opacity, we analyze its impact on the transparency and fairness of educational systems, advocating for the establishment of transparent algorithmic assessment standards. Subsequently, addressing privacy issues, the paper delves into aspects such as the collection and utilization of students' personal information, privacy breaches, protection of student rights, and data security and system vulnerabilities. When discussing societal biases, we focus on the potential inequalities reflected in algorithmic decision-making and propose strategies and methods to establish diverse and inclusive algorithm development teams and to break societal biases. Ethical review and regulatory recommendations are then presented, including transparent algorithmic assessment, privacy protection, diversity in team building, and interdisciplinary research. Finally, looking ahead, the paper calls for the introduction of advanced ethical review mechanisms, interdisciplinary research, public participation, and digital literacy cultivation to promote the sustainable development of AI in education. Through in-depth research and addressing ethical blind spots, we aim to establish a more just, transparent, and trustworthy AI education system, better serving students, educators, and society as a whole.