Ethical and Explainable Pedagogical Interventions: Emotion-Aware Transparent AI for Psychological Well-Being

This paper introduces an educational intervention model that integrates multimodal emotional analysis and Explainable Artificial Intelligence (XAI) to foster both cognitive engagement and psychological well-being in AI-assisted learning environments. The proposed model is composed of four interdependent components: an emotional state detection module, a transparency-focused ex-plainability engine, an ethical adaptation framework, and a dynamic intervention logic. Using natural language processing, acoustic analysis, and behavioral cues, the model identifies stress, anxiety, or demotivation in real time and adapts pedagogical feedback accordingly. The approach emphasizes transparency through a student-facing dashboard and autonomy by allowing learners to opt out of emotion tracking. A large-scale evaluation involving 320 students demonstrates that our model significantly improves learning outcomes while reducing algorithmic bias and fostering emotional resilience. These findings underline the value of emotion-aware, ethically grounded AI systems in future educational settings.