The integration of Artificial Intelligence (AI) tools in education represents a significant paradigm shift, holding the promise of enhancing learning experiences and outcomes. This study delves into a pivotal inquiry: What motivational effects does using AI in educational settings exert on student learning? This study, involving 64 students, utilizes a mixed-method approach, including online surveys and focus group discussions, to explore the intricate relationship between AI integration and student motivation in education. It employs robust methodological frameworks such as Grounded Theory and is guided by the Situated Learning Approach and constructivist learning theory.

Central findings illuminate AI's potential to foster personalized learning pathways, nurturing the development of critical metacognitive skills like critical thinking and problem-solving. Aligned with the Situated Learning paradigm, AI incorporation infuses educational experiences with contextual relevance, fostering genuine and impactful learning interactions. Analysis reveals six distinct motivational categories: Efficiency, Productivity, Autonomy, Competence Acquisition, Competitive Advantage, and Self-regulation. These categories align with the proposed theoretical model's action phases, from situation analysis to reflective consequences. This study reveals new insights through thorough data analysis, enhancing our understanding of education. Students use AI tools to improve information retrieval, productivity, self-directed learning, and content comprehension, gain competitive advantages, and apply knowledge effectively.

This study underscores AI's transformative potential and challenges in shaping personalized, learner-centered education globally. It stresses the importance of understanding motivation-related factors for the effective integration of AI in educational contexts, facilitating the creation of efficient, student-centric learning environments.