Exploring the Artificial Intelligence Era in Influencing Self-Paced Learning: Systematic and Bibliometric Review of Literature

Abstract

Background/purpose: Education continues to evolve along with technological advances, and one of the biggest changes is the application of artificial intelligence (AI) in the learning process. The main problem in this study is the lack of in-depth understanding of the factors that influence the effectiveness of using AI in self-paced learning models. The purpose of this study is to identify key factors that influence the success of AI-based self-paced learning and contribute to the development of more adaptive technology-based education policies. Materials/methods: This study uses a Systematic Literature Review (SLR) approach with the PRISMA method. SLR to collect and analyze related literature from various academic databases. Results: The results of the study indicate that factors such as technology and infrastructure that support flexible learning, AI's ability to detect and overcome learning challenges, and personalization of learning that suits students' needs, play a significant role in increasing learning effectiveness. Conclusion: AI in self-paced learning the use of chatbots, language support, and assessment automation were also found to contribute significantly to creating a more focused and efficient learning experience.