Affective Computing and Emotional Education

A Systematic Review of Emotional Response Inference from Visual Content

Authors

DOI:

https://doi.org/10.62161/revvisual.v18.6405

Keywords:

Affective computing, Emotional inference, Visual content, Emotional education, Artificial Intelligence

Abstract

Affective AI has established itself as a field focused on inferring emotional responses from visual stimuli, while concern for emotional and affective development occupies a central place in current educational approaches. The aim of this article is to conduct a systematic review focused on understanding how affective computing is applied to infer emotional responses to visual content and to what extent these approaches contribute to students' emotional education. The results highlight the need to develop approaches that take into account not only technological development but also the emotional well-being of the subjects. This consideration is key to ensuring that automated emotional inference will contribute to emotional development in educational contexts.

Downloads

Download data is not yet available.

Global Statistics ℹ️

Cumulative totals since publication
23
Views
8
Downloads
31
Total
Downloads by format:
PDF (Español (España)) 7 PDF 1

References

Araya, S., & Espinoza, L. (2020). Contributions from neuroscience to the understanding of learning processes in educational contexts. Purposes and representations, 8 (1), e312.

Bosquez, V., Sanz, C., Baldassarri, S. S., Ribadeneira, E., Valencia, G., Barragán Arnal, R., Camacho, Á., Shauri Romero, J., & Camacho Castillo, L. A. (2018). Affective computing: Emotions, technologies and their relationship with virtual education. Talents Research Journal, 5(1), 94–103.

Das, A. & Malaviya, S. (2024). AI (Artificial Intelligence)-Enabled Affective Computing in Adaptive Learning: An Integrated Approach to Learning. 20. 30.

Devillers, L., & Cowie, R. (2023). Ethical considerations on affective computing: An overview. Proceedings of the IEEE, 111(10), 1445–1458 DOI: https://doi.org/10.1109/JPROC.2023.3315217

Dias S.B., Jelinek H.F., & Hadjileontiadis L.J. (2024). Wearable neurofeedback acceptance model for students' stress and anxiety management in academic settings. PLoS One, 24 October;19(10):e0304932. doi: 10.1371/journal.pone.0304932. DOI: https://doi.org/10.1371/journal.pone.0304932

Durlak, J. A., Weissberg, R. P., Dymnicki, A. B., Taylor, R. D., & Schellinger, K. B. (2011). The impact of enhancing students’ social and emotional learning: A meta-analysis of school-based universal interventions. Child Development, 82(1), 405–432. https://doi.org/10.1111/j.1467-8624.2010.01564.x DOI: https://doi.org/10.1111/j.1467-8624.2010.01564.x

Esfandbod, A., Rokhi, Z., Meghdari, AF et al. (2023). Use of an emotional robot capable of lip synchronisation in robot-assisted speech therapy sessions for children with language disorders. Int J of Soc Robotics 15, 165–183 (2023). https://doi.org/10.1007/s12369-022-00946-2 DOI: https://doi.org/10.1007/s12369-022-00946-2

Gross, J. J. (2015). Emotion regulation: Current status and future prospects. Psychological Inquiry, 26(1), 1–26. https://doi.org/10.1080/1047840X.2014.940781 DOI: https://doi.org/10.1080/1047840X.2014.940781

Jeong, S., Aymerich-Franch, L., Arias, K. et al. (2023). Implementation of a positive psychology robotic coach to improve the psychological well-being of university students. User Model User-Adap Inter 33, 571–615. https://doi.org/10.1007/s11257-022-09337-8 DOI: https://doi.org/10.1007/s11257-022-09337-8

Joseph, C., Maheswari, P.U. (2025). Detection and prevention of smartphone addiction based on facial emotions using deep learning and video-based learning. Sci Rep 15, 18025. https://doi.org/10.1038/s41598-025-99681-7 DOI: https://doi.org/10.1038/s41598-025-99681-7

Ladas, A. I., Katsoridou, C., Gravalas, T., Klados, M. A., Stravoravdi, A. S., Tsompanidou, N., Fragkedaki, A., Bista, E., Chorafa, T., Petrovic, K., Vlotinou, P., Tsiakiri, A., Papazisis, G., & Frantzidis, C. A. (2025). Creativity and Resilience Through Arts, Technology and Emotions: A Pilot Study on the Feasibility and Validity of the CREATE Platform. Brain Sciences, 15(11), 1171. https://doi.org/10.3390/brainsci15111171 DOI: https://doi.org/10.3390/brainsci15111171

Laraib, U., Shaukat, A., Khan, R. A., Mustansar, Z., Akram, M. U., & Asgher, U. (2023). Recognition of Children’s Facial Expressions Using Deep Learned Features. Electronics, 12(11), 2416. https://doi.org/10.3390/electronics12112416 DOI: https://doi.org/10.3390/electronics12112416

Mayer, R. E. (2002). Multimedia learning principles. Learning and Instruction. DOI: https://doi.org/10.1016/S0079-7421(02)80005-6

Mendieta, F. J., & Martínez, A. (2015). Emotional regulation and presence in adolescent relationships. Divulgare Scientific Bulletin of the Actopan Higher School, 2(4).

Molina, L., Guerrero, R., Bravo, B., & González, A. (2025). Artificial Intelligence: most widely used trends in education and medical simulations. Social Development Studies: Cuba and Latin America, 13(1), 121–133.

Pekrun, R. (2006). The control-value theory of achievement emotions. Educational Psychology Review, 18(4), 315–341. https://doi.org/10.1007/s10648-006-9029-9 DOI: https://doi.org/10.1007/s10648-006-9029-9

Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The Achievement Emotions Questionnaire (AEQ). Contemporary Educational Psychology, 36(1), 36–48. https://doi.org/10.1016/j.cedpsych.2010.10.002 DOI: https://doi.org/10.1016/j.cedpsych.2010.10.002

Raza Ur Rehman, H.M., Saleem, M., Jhandir, M. et al. (2025). Detecting hate in diversity: A multilingual mixed-code analysis study of images and videos. J Big Data 12, 109. https://doi.org/10.1186/s40537-025-01167-w DOI: https://doi.org/10.1186/s40537-025-01167-w

Rodríguez, V. M., Gallar, Y., & Barrios, E. A. (2017). Theoretical considerations on affective computing in the teaching-learning process in higher education. CienciAmérica, 6(3), 170–175.

Rodríguez-Arboleda, V., Gallar-Pérez, Y., & Barrios-Queipo, E. (2017). Theoretical considerations on affective computing in the teaching-learning process in higher education. CienciAmérica, 6(3).

Sarango, M. P., Calero, A.C., Ortiz, K. A., Morocho, M., & Ortiz, I. L. Á. (2025.). The socio-emotional approach to improving learning: An experience from innovation. Prospherus, 2, 1074-1093. DOI: https://doi.org/10.63535/4qz3mn77

Vistorte, A. O. R., Deroncele-Acosta, A., Ayala, J. L. M., Barrasa, A., López-Granero, C., & Martí-González, M. (2024). Integrating artificial intelligence to assess emotions in learning environments: A systematic literature review. Frontiers in Psychology, 15, Article 1387089. https://doi.org/10.3389/fpsyg.2024.1387089 DOI: https://doi.org/10.3389/fpsyg.2024.1387089

Wafa, A.A., Eldefrawi, M.M., & Farhan, M.S. (2025). Advances in multimodal emotion recognition in big data through rapid engineering and deep adaptive learning. J Big Data 12, 210 https://doi.org/10.1186/s40537-025-01264-w DOI: https://doi.org/10.1186/s40537-025-01264-w

Wang, C.M., & Lin, C.H. (2025). Design of an Interactive System by Combining Affective Computing Technology with Music for Stress Relief. Electronics, 14(15), 3087. https://doi.org/10.3390/electronics14153087 DOI: https://doi.org/10.3390/electronics14153087

Wang, Y. (2025). An AI-based personalised recommendation system for simulation-based digital media arts education. International Journal of Computer-Mediated Games and Simulations (IJGCMS), 17 (1), 1-15. https://doi.org/10.4018/IJGCMS.396267 DOI: https://doi.org/10.4018/IJGCMS.396267

Yu, S., Androsov, A., Yan, H., & Chen, Y. (2024). Bridging computer and education sciences: A systematic review of automated emotion recognition in online learning environments. Computers & Education, 220, 105111. https://doi.org/10.1016/j.compedu.2024.105111 DOI: https://doi.org/10.1016/j.compedu.2024.105111

Yuan, Q. (2022). Research on an emotion recognition algorithm in the classroom based on visual emotion classification. Computational Intelligence and Neuroscience, 6453499, 10 pp. https://doi.org/10.1155/2022/6453499 DOI: https://doi.org/10.1155/2022/6453499

Yuvaraj, R., Mittal, R., Prince, A. A., & Huang, J. S. (2025). Affective computing for learning in education: A systematic review and bibliometric analysis. Education Sciences, 15(1), 65. https://doi.org/10.3390/educsci15010065 DOI: https://doi.org/10.3390/educsci15010065

Published

2026-10-19

How to Cite

Ordóñez Jiménez, M. A. (2026). Affective Computing and Emotional Education: A Systematic Review of Emotional Response Inference from Visual Content. VISUAL REVIEW. International Visual Culture Review Revista Internacional De Cultura Visual, 18(5), 125–135. https://doi.org/10.62161/revvisual.v18.6405

Issue

Section

Research articles