Abstract
Affective artificial intelligence (AI) is rapidly transforming modern learning environments by enabling systems to perceive, interpret, and respond to student emotions. This chapter explores a hybrid AI framework that integrates symbolic logic with neural networks to enhance emotional understanding and adaptive instruction in the classroom. The symbolic reasoning component provides transparency, rule based interpretability, and structured decision-making, while neural networks contribute perceptual accuracy through multimodal emotion recognition. By combining these complementary strengths, the hybrid model supports more reliable, context aware affective responses that improve student engagement and personalize learning pathways. The chapter presents the conceptual foundations of such hybrid systems, examines their relevance for real-time classroom interaction, and discusses implementation considerations, such as data processing, model integration, and ethical challenges.