Systematic Literature Review on Students’ Perceptions of Ethical AI: Fairness, and Regulatory Understanding

Abstract

The increasing demand of Artificial Intelligence (AI) in academia has intensified debate over ethical implications and establishment of regulations. While extensive research focuses on AI technicalities such as algorithm development, algorithmic accountability, and regulatory frameworks, insufficient attention has been given to how students perceive these ethical dimensions. This study presents a systematic literature review (SLR) on students’ perceptions of ethical AI, focusing on awareness, fairness, and knowledge of regulatory systems. This SLR adheres to the PRISMA 2020 guidelines and consolidates findings from four academic databases: Scopus, ScienceDirect, IEEE Xplore, and ERIC. The findings reveal that while students generally acknowledge the importance of using AI tools ethically, their understandings are influenced by the Perceived of Usefulness (PoU), Perceived Of Ease of Use (PoEU) and Social and Cultural Perceptions. Students are concerned about the fairness of evaluations, as well as the potential of bias and discrimination, when AI techniques are employed without appropriate regulations or policies. This review highlights the importance of transparency in AI systems, the mitigation of prejudice, the assurance of justice, the reform of the educational paradigm, and the establishment of a comprehensive ethical AI framework and policy. The paper signifies the need to revolutionize teaching and learning while empowering learners with robust ethical foundations, which is critical in developing responsible AI practitioners.