Fairness, Bias, and Ethics in AI: Exploring the Factors Affecting Student Performance

Abstract

The use of artificial intelligence (AI) as a data science tool for education has enormous potential for increasing student performance and course outcomes. However, the growing concern about fairness, bias, and ethics in AI systems requires a careful examination of these issues in an educational context. Using AI and predictive modelling tools, this paper explores the aspects influencing student performance and course success. The Open University Learning Analytics Dataset (OULAD) is analysed using several AI techniques (logistic regression and random forest) in this study to reveal insights about fairness, ethics, and potential biases. This dataset has been used by hundreds of studies to explore how educational data mining can provide information on students. However, potential bias or unfairness in that dataset could undermine the results and any conclusions made from them. To gain insights into the dataset's properties, this was analysed using a typical data science methodology, which included data collecting, cleaning, and exploratory data analysis using Python. By applying AI-based predictive models, this study aims to detect potential biases and their impact on student outcomes. Fairness and ethical considerations are central to the analysis as the representation of various demographic groups and any disparities are evaluated in course results. The goal is to provide useful insights on the proper use of AI in education, while also maintaining equitable and transparent decision-making procedures. The findings shed light on the complicated interplay between artificial intelligence, fairness, and ethics in the context of student performance and course success. As artificial intelligence continues to influence the educational landscape, this study will provide useful ideas for encouraging fairness and minimising biases, resulting in a more inclusive and equal learning environment.