Akademik Veri Yönetim Sistemi
Transforming Education with Singularity Technologies Lifelong Learning from Childhood to Adulthood, Serap Uğur, Editör, Taylor & Francis Ltd, Florida, ss.14-30, 2026
This chapter explores the role of artificial intelligence (AI) in emotional learning analytics within online learning environments. Emotions significantly influence learner motivation, engagement, and academic performance, making their analysis especially relevant in online contexts where direct interaction is limited. Advances in AI enable the analysis of emotional states through methods such as self-reports, behavioral indicators, and physiological signals.
The chapter reviews key emotion theories and AI-based approaches including facial, vocal, textual, physiological, and multimodal emotion recognition. Recent research highlights the potential of sentiment analysis and multimodal data integration for real-time monitoring and adaptive support in online learning.
However, implementing AI-driven emotional learning analytics presents notable challenges, including intrusive measurement methods, data accuracy, model complexity, cultural and contextual sensitivity, and integration into educational practice. Ethical and privacy concerns are also critical, particularly regarding continuous monitoring, data security, algorithmic bias, transparency, and informed consent.
Overall, while emotional learning analytics offers promising opportunities for enhancing online education, its use must prioritize pedagogical value and ethical responsibility. Future research should focus on improving model reliability while ensuring transparent and privacy-conscious use of emotional data.