Book

DEVELOPMENT OF THE PjBL-GeoGAI LEARNING MODEL FOR CALCULUS INSTRUCTION presents an innovative instructional approach that integrates Project-Based Learning (PjBL), GeoGebra, and Artificial Intelligence (AI) to transform Calculus learning into a more interactive, contextual, and meaningful experience. The book responds to the persistent challenges faced by mathematics students in understanding abstract Calculus concepts and connecting symbolic procedures with graphical and real-world representations. At the heart of the book is the PjBL-GeoGAI learning model, which combines project-based activities with the dynamic visualization capabilities of GeoGebra and the adaptive support of Artificial Intelligence. Through this integration, students are encouraged to explore concepts, formulate problems, develop mathematical models, test solutions, and communicate their findings. GeoGebra enables learners to visualize limits, derivatives, integrals, functions, and other mathematical relationships dynamically, while AI provides additional guidance, explanations, feedback, and technical assistance. The book systematically presents the development of the PjBL-GeoGAI model using the ADDIE framework, covering the stages of Analysis, Design, Development, Implementation, and Evaluation. The developed model consists of six major learning stages: orientation and essential questions, project planning, schedule development, exploration with GeoGebra and AI, product development and presentation, and evaluation and reflection. These stages provide lecturers and students with a structured framework for implementing technology-supported project-based Calculus instruction. Empirical findings presented in the book demonstrate the feasibility and effectiveness of the developed model. Expert validation resulted in an overall validity score of 88.5%, while student responses produced a mean practicality score of 4.5 on a five-point scale, with 93% of the learning stages successfully implemented. Students' mean conceptual understanding scores increased from 56.7 in the pretest to 84.3 in the posttest, accompanied by an N-Gain of 0.62 in the moderate category.