AI-Enhanced Learning Ecosystems: A Review of Emotional Analytics, EDM, and Personalized Education Models

The rapid growth of digital learning ecosystems has enabled the integration of Artificial Intelligence (AI), Educational Data Mining (EDM), and emotional analytics to enhance personalised and student-centric education. Emerging research highlights that AI-driven systems improve learning outcomes through adaptive feedback, predictive modelling, and emotion-aware interventions, while also supporting educational management processes. However, disparities persist between traditional teaching methods and modern skill requirements, especially in rigid systems like the Indian higher education context. Recent studies demonstrate strong links between students’ emotional states, engagement levels, and academic performance, showing the need for emotionally intelligent AI tools. This review, based on a rigorous selection of 22 relevant studies, reveals advancements in deep learning, multimodal analytics, and generative AI-supported learning environments. Despite these developments, gaps remain in real-time classroom integration, ethical considerations, and multimodal emotional detection. The study concludes that Emotional AI has high potential to transform personalised learning, but further empirical validation and ethical frameworks are essential for its effective large-scale adoption.