ARTIFICIAL INTELLIGENCE IN ENHANCING LEARNING MOTIVATION AND SELF-DIRECTED LEARNING AMONG PRE-SERVICE TEACHERS: A SYSTEMATIC REVIEW - Nova Syafira Ariyanti; Maisyaroh Maisyaroh; Indra Lesmana; Maulana Paramaditya Ananta (Faculty of Economics and Business, Universitas Negeri Jakarta, 2025)
This study aims to analyse the use of Artificial Intelligence (AI) in improving student teacher motivation and learning independence with a Systematic Literature Review (SLR) approach. The study followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines with data sources from the Scopus database for the period 2019–2024. The process of article selection was done through the phases of identification, screening, testing of feasibility and selection of final article. This process resulted in 32 articles being retrieved that met the inclusion criteria and were analysed using thematic analysis techniques. Results of this research indicate that the use of AI, especially generative artificial intelligence such as ChatGPT, learning chatbots, and intelligent tutoring systems, could improve student learning motivation through more flexible, interactive, and personalised learning. In addition, AI also enables enhanced self-directed learning through easy access to information, self-management of the learning process, and the development of problem-solving skills. This study contributes to the growing body of research on the implementation of AI in higher education and specifically the digital learning transformation for student teachers in the era of modern education.