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May 3, 2026Open Access

Evaluating Student Emotional Well-Being in Humanitarian Educational Contexts Using an AI-Based Sentiment Analysis System

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Randomized trial evaluates emotional well-being in humanitarian education, suggesting improved detection through AI analysis.

This study aims to evaluate student emotional well-being in humanitarian educational contexts using AI-driven sentiment analysis.
An AI-based sentiment analysis system was developed utilizing natural language processing and transformer models like BERT and RoBERTa.
The system supports multilingual inputs and incorporates real-time alerts and educator guidance for early intervention.
Performance was assessed based on its ability to accurately detect emotional states from textual data.
The system achieved emotion detection accuracy exceeding 90%, significantly enhancing educators' ability to identify at-risk students.
Real-time monitoring enabled timely interventions, contributing to improved student well-being in educational settings.

Mahmoud Galeb Saloum (2026) studied this question.

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