Emotion Recognition in Smart Education: Pathways, 
Challenges, and System Design

Yazhou Du*; Yue Wang; Qiao Qiao; Tianyou Zhao; Yongying Xia

doi:10.70711/neet.v3i4.6764

Emotion Recognition in Smart Education: Pathways, Challenges, and System Design

Yazhou Du*, Yue Wang, Qiao Qiao, Tianyou Zhao, Yongying Xia

Abstract

With the rapid advancement of artificial intelligence (AI) technology, the value of emotion recognition technology in smart education has become increasingly prominent. This paper systematically explores the application pathways of emotion recognition technology in
smart education and proposes a framework for an emotion recognition-based intelligent education system. By reviewing existing emotion
models, relevant datasets, and methodological approaches, this study analyzes the current state and challenges of emotion recognition technology in smart education. Furthermore, it envisions future development directions, aiming to provide insights for the design and optimization of
smart education systems and promote the broader application of emotion recognition technology in the educational field.

Keywords

Emotion recognition; Smart education; Deep learning; AI application development

Full Text:

PDF

Included Database

References

[1] Gong Jin. Multi-modal learning state research and analysis empowered by artificial intelligence. Information Systems Engineering,

11:137140, 2023.

[2] Tianpeng Qin, Hui Sheng, Lu Yue, et al. A review of emotion recognition research based on eeg signals. Computer Engineering and Applications, 59(15):3854, 2023.

[3] S. Yu, A. Androsov, H. Yan, et al. Bridging computer and education sciences: A systematic review of automated emotion recognition in

online learning environments. Computers & Education, 220:105111, 2024.

[4] A. Gupta, R. Jaiswal, S. Adhikari, et al. Daisee: Dataset for affective states in e-learning environments, 2016.

[5] P. Chakradhar and K. Praveen. An intelligent system for monitoring students engagement in large classroom teaching through facial expression recognition. Expert Systems, 39(1), 2021.

[6] Jianfeng Li. Multi-modal emotion recognition technology applications and future prospects. Chinese Technology Market Association,

pages 107108, 2024.

[7] Rong Zhu. Research on classroom teacher behavior analysis and detection based on deep learning, 2024.

[8] Pengtao Wang. Research on classroom teacher behavior analysis based on deep learning, 2023.

[9] Haoyuan Zhang. Research on face emotion recognition based on deep learning in smart education, 2021.

DOI: http://dx.doi.org/10.70711/neet.v3i4.6764

Refbacks

There are currently no refbacks.