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This interdisciplinary study examines AI-driven Facial Emotion Recognition (FER) integration into e-learning systems to tackle engagement deficits and promote sustainable educational markets. FER enables adaptive learning via real-time emotional feedback, potentially boosting engagement and reducing attrition. However, challenges such as algorithmic biases, privacy concerns, cultural misalignments, and infrastructural barriers hinder adoption. A PRISMA-guided systematic review of 140 high-impact studies reveals gaps in scalability, ethical governance, and cross-cultural validation. We propose a sustainable framework with four pillars: pedagogically grounded adaptive learning, transparent ethical protocols (GDPR/FERPA-compliant), culturally inclusive emotion modeling, and cost-efficient cloud infrastructure. This framework mitigates risks like automation bias and surveillance capitalism through interdisciplinary collaboration in computer science, education, and ethics. Practical recommendations include public–private partnerships and decolonized AI design, while policy advocacy emphasizes equitable funding and explainable AI standards. Responsibly implemented FER could yield equitable learning outcomes and economic savings, fostering long-term EdTech viability (Dhawan in J. Educ. Technol. Syst. 49:5–22, 2020;Zawacki-Richter et al. in Article 39, 2019;).

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Integrating AI-Driven Facial Emotion Recognition into E-Learning Systems: Sustainable Educational Markets Through Interdisciplinary Innovations

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  • 发表日期：

    2026年

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  • Anirban Ghatak,
  • Miss Setavi Purushottam Thoke

摘要

This interdisciplinary study examines AI-driven Facial Emotion Recognition (FER) integration into e-learning systems to tackle engagement deficits and promote sustainable educational markets. FER enables adaptive learning via real-time emotional feedback, potentially boosting engagement and reducing attrition. However, challenges such as algorithmic biases, privacy concerns, cultural misalignments, and infrastructural barriers hinder adoption. A PRISMA-guided systematic review of 140 high-impact studies reveals gaps in scalability, ethical governance, and cross-cultural validation. We propose a sustainable framework with four pillars: pedagogically grounded adaptive learning, transparent ethical protocols (GDPR/FERPA-compliant), culturally inclusive emotion modeling, and cost-efficient cloud infrastructure. This framework mitigates risks like automation bias and surveillance capitalism through interdisciplinary collaboration in computer science, education, and ethics. Practical recommendations include public–private partnerships and decolonized AI design, while policy advocacy emphasizes equitable funding and explainable AI standards. Responsibly implemented FER could yield equitable learning outcomes and economic savings, fostering long-term EdTech viability (Dhawan in J. Educ. Technol. Syst. 49:5–22, 2020;Zawacki-Richter et al. in Article 39, 2019;).