A systematic review of generative artificial intelligence in education: Pedagogical impacts, ethical risks, and future directions

Abstract

The development of Generative Artificial Intelligence within learning institutions has presented both groundbreaking prospects and both serious issues on teaching quality, academic integrity, ethical responsibility, and student learning outcomes. Even though the use of Large Language Models, adaptive learning tools, intelligent tutoring systems, and assessment automation platforms continues to grow, it is still unclear how these tools can be used in pedagogy and whether they jeopardize ethical concerns and the future of education. The review has analyzed articles on Artificial Intelligence in Education, personalized learning, digital pedagogy, learning analytics, Human-AI Collaboration, and AI governance. The specific focus was made on the engagement of the students, reduction of the workload of the teachers, academic integrity, digital literacy, and data privacy issues. The results show that Generative Artificial Intelligence holds a great potential to enhance the personalized learning experience, automation of repetitive academic elements, aid in the design of assessments, improve content-generation, and create adaptable learning settings. Teachers are finding it easier to manage their administrations and access to teaching materials, and learners are receiving more personalized feedback and interactive learning opportunities. Nevertheless, the review also reveals significant risks connected with bias, misinformation, the opacities of the algorithms, plagiarism, unfair access, and the deterioration of critical thinking abilities. The paper concludes that the future adoption of Generative Artificial Intelligence in education requires a solid framework of AI ethics, explicit policies governing AI usage, enhanced digital literacy, and balanced Human-AI Collaboration designs that do not undermine the main role of educators in instruction.