Artificial intelligence in education: a bibliometric analysis of emerging trends

Abstract

This study investigates the transformative potential of artificial intelligence (AI) in education through a bibliometric analysis of 291 scholarly works retrieved from the Web of Science (WoS) database. Traditional methods of instruction are under threat from the growing demand for individualized and equitable education, particularly in underserved communities. This study looks at how AI innovations, like virtual assistants and adaptive learning platforms, can enhance learning outcomes and the efficacy of instruction in order to address these concerns. The methodology used co-occurrence and co-citation analyses to map research trends and find educational and AI thematic clusters. Pedagogical frameworks, medical education innovations, ethical governance, generative AI applications, and AI acceptance are the five main research areas highlighted in the findings. With 5,246 citations and an H-index of 42, the data show how widely used AI is in both academia and industry. Adaptive learning models, moral dilemmas, and AI literacy are emerging themes. According to this research, AI has the potential to improve accessibility, equity, and quality in education while tackling issues like algorithmic bias and digital divides. This is in line with sustainable development goal 4 (quality education). Teachers, legislators, and technologists can use this study’s thorough intellectual landscape to gain practical insights on how to responsibly incorporate AI into educational systems for more sustainable innovation.