The integration of artificial intelligence (AI) into education reshapes how teaching, learning, and assessment are approached. AI in education uses foundational concepts from computer science, cognitive psychology, and pedagogy, personalizing learning experiences, automating administrative tasks, and providing real-time feedback. Frameworks guiding AI implementation enhance educational outcomes by making instruction more responsive and data driven. However, these frameworks must also account for ethical considerations, such as data privacy, algorithmic bias, and equitable access. By grounding AI technologies in educational theory and responsible design principles, organizations can create systems that support learners and educators in meaningful, inclusive ways. Foundations and Frameworks for AI in Education explores the transformative potential of AI in reshaping educational practices across all levels of learning. It provides a comprehensive examination of AI-driven tools and methodologies, including adaptive learning platforms, intelligent tutoring systems, and data-driven decision-making processes in education. This book covers topics such as data privacy, digital technology, and special education, and is a useful resource for educators, computer engineers, sociologists, academicians, researchers, and scientists.