AI and Education: Pathways of Integration

Abstract This chapter traces the evolving pathways through which artificial intelligence (AI) has been integrated into education, from early intelligent tutoring systems to adaptive platforms and large language model (LLM)-driven agents. Anchored in the learning sciences, it emphasizes that emotional–cognitive dynamics are not peripheral but structural to effective learning. The chapter positions affective computing as a key integrator, aligning perception, inference, and intervention to support both academic outcomes and emotional resilience. It further argues that trustworthy integration requires embedding AI into teacher workflows, ensuring transparency, choice, and professional agency. Evidence must extend beyond test scores to include transfer, retention, self-regulation, and affective indicators, while fairness and auditability are treated as intrinsic to effectiveness. By reframing AI not as a control tower but as a trusted co-actor, the chapter outlines a human-centered vision where rigor, empathy, and accountability define the future of AI in education.