Integration Artificial Intelligence in Video Media to Improve Student Motivation: Systematic Literature Review - Shalehani Shalehani; Pakhrur Razi; Hamdi Hamdi; Hayyu Yumna (Universitas Negeri Padang in collaboration with Association of Mathematics and Natural Sciences Institution for Education of Teaching Staff Indonesia (AMLI), 2025)
This study aims to systematically analyze the integration of Artificial Intelligence (AI) in video-based learning media and its impact on enhancing students’ learning motivation through a Systematic Literature Review (SLR) approach. Fifteen national and international journal articles published between 2015 and 2025 were systematically reviewed through the stages of identification, selection, eligibility, and synthesis. The analysis focused on the configuration of AI integration in educational videos, its contribution to students’ learning motivation, and the challenges of implementation in diverse educational contexts. The findings reveal that the integration of AI in video learning effectively increases students’ motivation, engagement, and interest by providing adaptive feedback, personalized content, and interactive learning experiences. Furthermore, AI-based videos promote self-regulated learning and enhance learners’ confidence in managing their study processes. However, successful implementation remains dependent on teachers’ digital competence, technological infrastructure, and ethical data management. Overall, integrating AI into video-based learning holds strong potential to create engaging, adaptive, and learner-centered environments that foster sustained learning motivation.