The Ethical Imperative: Addressing Bias and Discrimination in AI-Driven Education

Abstract

In the burgeoning realm of AI-driven education, the promise of enhanced learning experiences is accompanied by pressing ethical concerns. This study delves into the pervasive issue of bias and discrimination embedded within AI algorithms and their potential ramifications on educational equity. With AI increasingly shaping learning environments, there's a heightened risk of these algorithms perpetuating societal prejudices, thereby exacerbating existing disparities in education. The paper underscores the urgency of recognizing and mitigating these biases, advocating for transparent AI models, diverse dataset integration, and interdisciplinary collaborations. Drawing from a mixed-methods analysis encompassing literature reviews and stakeholder interviews, the research highlights specific instances of bias in AI educational tools, from gender and cultural insensitivities to performance predictions. The findings emphasize that addressing bias in AI education is not merely a technical challenge but a moral obligation, necessitating concerted efforts from academia, industry, and policymakers to ensure a more inclusive and equitable educational future.