A Deep Learning-Based Emotion Detection Framework for Real-Time Educational Environments

DOI

10.2991/978-94-6239-644-9_2How to use a DOI?

Keywords

Deep Learning in Education; Adaptive Learning; Personalized Learning Systems; Student Engagement; E-learning Platforms; Gamified Learning Strategies Educational Technology Integration

Abstract

The inclusion of emotional intelligence into educational systems is essential for cultivating tailored and engaging learning environments in the advancing realm of smart learning. The paper outlines the development and execution of a deep learning framework for real-time emotion detection in educational settings. The proposed system utilizes sophisticated neural architectures— namely Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformer- based models—to precisely categorize students’ emotions from multimodal data sources, including facial expressions, speech signals, and behavioral patterns of contact.

The framework improves the emotional context- awareness of intelligent educational systems by integrating visual and auditory cues, facilitating adaptive responses to learners’ requirements. The architecture is trained and validated with benchmark emotion recognition datasets and data obtained from simulated classroom environments. Initial findings indicate significant precision and reliability in identifying primary emotional states, including engagement, frustration, bewilderment, and motivation. This foundational study establishes the groundwork for incorporating emotion-sensitive reactions in forthcoming adaptive learning environments and gamified educational platforms, with the ultimate goal of enhancing student outcomes and emotional well-being.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Neethu Narayanan
AU  - S. Daniel Madan Raja
PY  - 2026
DA  - 2026/04/19
TI  - A Deep Learning-Based Emotion Detection Framework for Real-Time Educational Environments
BT  - Proceedings of the Global Innovation and Technology Summit “AAROHAN 3.0”_Engineering track (GITS-EAS 2025)
PB  - Atlantis Press
SP  - 3
EP  - 10
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-644-9_2
DO  - 10.2991/978-94-6239-644-9_2
ID  - Narayanan2026
ER  -