Generative AI-Empowered Problem-Based Learning in Trigonometry: A Systematic Literature Review of Student Motivation and Analytical Skills - Mokhamad Zazuli Irwanto; Mustaji Mustaji; Lamijan Hadi Susarno (FINGER : Jurnal Ilmiah Teknologi Pendidikan, 2026)
Background: The abstract nature of trigonometry often presents significant visualization barriers for students, hindering the development of spatial reasoning and analytical thinking skills. While conventional multimedia has been widely used, it lacks the adaptability required to visualize dynamic mathematical concepts effectively.Aims: This study aims to analyze the transformation of mathematical visualization through Generative AI features and evaluate its impact on students' learning motivation and analytical thinking skills within a Problem-Based Learning (PBL) framework.Methods: Employing a Systematic Literature Review (SLR) method adhering to PRISMA 2020 protocols, this study analyzed 41 rigorously selected articles (17 international and 24 national) published between 2021 and 2026. Quality assessment was enforced using a standardized protocol, and data were analyzed using the Miles, Huberman, and Saldaña interactive model.Results: The findings reveal a significant paradigm shift from static representation to AI-empowered generative visualization. Advanced text-to-image generation and dynamic animation features successfully concretize abstract concepts, thereby facilitating mathematical modeling and spatial reasoning aligned with the Analyzing (C4) level of Bloom’s Taxonomy.Conclusion: The synthesis demonstrates that the synergy between AI-driven visuals and the inquiry structure of PBL acts as an essential visual scaffold. This integration effectively fosters intrinsic learning motivation and significantly improves analytical thinking skills in trigonometry, offering a strategic pedagogical framework for evidence-based AIEd implementation.