Integrating Artificial Intelligence in Education: A Systematic Review of Literature

Abstract

This study investigates the ethical integration of Artificial Intelligence (AI) in academic settings, focusing on its implementation in education and its subsequent impact on learning experiences. Employing a systematic scoping review methodology guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, the research synthesises existing literature to identify critical factors influencing AI adoption. These factors include cost, data privacy, algorithmic bias, job displacement, and safety concerns. The study further explores effective strategies for mitigating these challenges, such as developing institutional policies, utilising plagiarism detection tools, providing comprehensive training for students and staff, fostering critical thinking, and conducting ethical AI workshops. Relevant literature was sourced from scholarly databases, including Google Scholar, Emerald Insights, Research4Life, and EBSCOhost, using Boolean operators (AND, OR) to refine the search. The findings underscore the necessity of continuous monitoring and evaluation of AI systems in educational environments to ensure their responsible and productive use. The study concludes that while there is a pressing need to integrate AI to enhance educational processes, it is imperative to proactively address associated ethical issues to harness the AI technology's benefits responsibly and equitably.