Emotional AI in the Workplace: Systematic Review of Effects on Employee Well-Being, Productivity, and Organizational Performance
DOI:
https://doi.org/10.61467/2007.1558.2026.v17i3.1398Keywords:
Emotional Artificial Intelligence, Affective Computing, Employee Well-Being, Organizational Productivity, Workplace Analytics, Human–AI Interaction, Algorithmic Bias and Ethics, Digital Transformation in Organizations, Inteligencia artificial emocional, informática afectiva, bienestar de los empleados, productividad organizacional, interacción entre humanos e IA, sesgos algorítmicos y ética, transformación digital en las organizacionesAbstract
The rapid integration of Artificial Intelligence (AI) and affective computing technologies into organisational environments is transforming workplace dynamics. However, their impact on employee well-being and productivity remains fragmented and insufficiently synthesised. This study addresses this research gap through a systematic review of AI-powered emotional intelligence systems. It focuses on their effects across three core dimensions: employee attitudes (job satisfaction, motivation, adaptability), workplace behaviours (performance, creativity, technology adoption), and organisational dynamics (leadership, trust, team cohesion). Following the PRISMA framework, this study conducts a systematic evaluation and comparative analysis of the state-of-the-art literature. It identifies key patterns, methodological trends, and underexplored areas. The findings suggest that emotional AI systems may enhance employee engagement and organisational productivity when implemented within ethically grounded and transparent frameworks. However, the review also highlights critical challenges related to privacy, emotional surveillance, algorithmic bias, and employee trust. This study contributes a structured framework that clarifies the role of emotional AI in organisational contexts and outlines actionable, scalable strategies for real-world application. By consolidating dispersed evidence and proposing directions for future research, this study is intended to provide a benchmark for subsequent investigations into AI-driven emotional intelligence and its implications for sustainable, human-centred workplaces.
Spanish-language metadata / Metadatos en español
Título en español:
La IA emocional en el lugar de trabajo: revisión sistemática de sus efectos sobre el bienestar de los empleados, la productividad y el desempeño organizacional
Resumen:
La rápida integración de la inteligencia artificial (IA) y las tecnologías de computación afectiva en los entornos organizacionales está transformando la dinámica del lugar de trabajo. Sin embargo, su impacto en el bienestar y la productividad de los empleados sigue siendo fragmentado y no se ha sintetizado lo suficiente. Este estudio aborda esta laguna de investigación mediante una revisión sistemática de los sistemas de inteligencia emocional basados en la IA. Se centra en sus efectos en tres dimensiones fundamentales: las actitudes de los empleados (satisfacción laboral, motivación, adaptabilidad), los comportamientos en el lugar de trabajo (rendimiento, creatividad, adopción de tecnología) y la dinámica organizacional (liderazgo, confianza, cohesión del equipo). Siguiendo el marco PRISMA, este estudio lleva a cabo una evaluación sistemática y un análisis comparativo de la literatura más reciente. Identifica patrones clave, tendencias metodológicas y áreas poco exploradas. Los resultados sugieren que los sistemas de IA emocional pueden mejorar el compromiso de los empleados y la productividad de la organización cuando se implementan dentro de marcos éticos y transparentes. Sin embargo, el análisis también pone de relieve retos fundamentales relacionados con la privacidad, la vigilancia emocional, el sesgo algorítmico y la confianza de los empleados. Este estudio aporta un marco estructurado que aclara el papel de la IA emocional en los contextos organizacionales y esboza estrategias viables y escalables para su aplicación en la vida real. Al consolidar la evidencia dispersa y proponer orientaciones para futuras investigaciones, este estudio pretende servir de referencia para estudios posteriores sobre la inteligencia emocional impulsada por la IA y sus implicaciones para lugares de trabajo sostenibles y centrados en las personas.
Palabras Claves:
Inteligencia artificial emocional, informática afectiva, bienestar de los empleados, productividad organizacional, análisis del lugar de trabajo, interacción entre humanos e IA, sesgos algorítmicos y ética, transformación digital en las organizaciones
Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i3.1398
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References
Abdul-Qawy, A. S., Pramod, P. J., Magesh, E., & Srinivasulu, T. (2015). The Internet of Things (IoT): An overview. International Journal of Engineering Research and Applications, 5(12), 71–82.
Acheampong, F. A., Nunoo-Mensah, H., & Chen, W. (2021). Transformer models for text-based emotion detection: A review of BERT-based approaches. Artificial Intelligence Review, 54, 5789–5829. https://doi.org/10.1007/s10462-021-09958-2
Adhikari, K. (2020). Correlation of gamification with blend of emotional intelligence (EI) and artificial intelligence (AI). International Journal of Advanced Science and Technology, 29(5), 2976–2986.
Alnagrat, A., Che Ismail, R., Syed Idrus, S. Z., & Abdulhafith Alfaqi, R. M. (2022). A review of extended reality (XR) technologies in the future of human education: Current trend and future opportunity. Journal of Human Centered Technology, 1(2), 81–96. https://doi.org/10.11113/humentech.v1n2.27
Amar, J., Parikh, N., Arora, S., McConnell, S., & Cornwall, T. (2024, September 20). AI can transform workforce planning for travel and logistics companies. McKinsey & Company.
Bengesi, S., El-Sayed, H., Sarker, M. K., Houkpati, Y., Irungu, J., & Oladunni, T. (2023). Advancements in generative AI: A comprehensive review of GANs, GPT, autoencoders, diffusion model, and transformers [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2311.10242
Fora Soft. (2024). 7 leading real-time AI emotion recognition software solutions. Fora Soft Blog. https://www.forasoft.com/blog/article/real-time-ai-emotion-software
Hong, K. (2020). Spatial–spectral–temporal framework for emotion recognition. IEEE Access, 8, 104303–104315. https://doi.org/10.1109/ACCESS.2020.3000066
Liu, H., Zhang, Y., Li, Y., & Kong, X. (2021). Review on emotion recognition based on electroencephalography. Frontiers in Computational Neuroscience, 15, 758212. https://doi.org/10.3389/fncom.2021.758212
Mantello, P., Ho, M.-T., Nguyen, M.-H., & Vuong, Q.-H. (2021). My boss the computer: A Bayesian analysis of socio-demographic and cross-cultural determinants of attitude toward non-human resource management. SSRN. https://doi.org/10.2139/ssrn.3772076
McGrath, M. J., & Ní Scanaill, C. (2014). Environmental monitoring for health and wellness. En Sensor technologies (pp. 249–282). Springer. https://doi.org/10.1007/978-1-4302-6014-1_11
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097
Patwardhan, A., & Knapp, G. (2016). EmoFit: Affect monitoring system for sedentary jobs. arXiv. https://arxiv.org/abs/1607.01077
Pinaya, W. H. L., Vieira, S., Garcia-Dias, R., & Mechelli, A. (2020). Convolutional neural networks. In Machine learning (pp. 173–191). Academic Press.
Poria, S., Majumder, N., Hazarika, D., & Mihalcea, R. (2019). DialogueRNN: An attentive RNN for emotion detection in conversations. Proceedings of the AAAI Conference on Artificial Intelligence, 33(1), 6818–6825. https://doi.org/10.1609/aaai.v33i01.33016818
Qian, L., Luo, Z., Du, Y., & Guo, L. (2009). Cloud computing: An overview. In CloudCom 2009 (pp. 626–631). Springer.
Rincon, J. A., Costa, Â., Novais, P., Julian, V., & Carrascosa, C. (2017). Using non-invasive wearables for detecting emotions with intelligent agents. In SOCO-CISIS-ICEUTE 2016 (pp. 73–84). Springer.
Sahoo, A. K., & Kavya, R. V. (2024). Text-based emotion detection: A deep learning approach using big data and semantic text analysis. SSRN. https://doi.org/10.2139/ssrn.5079858
Schmidt, R. M. (2019). Recurrent neural networks (RNNs): A gentle introduction and overview. arXiv. https://arxiv.org/abs/1912.05911
Şemsioğlu, S., Karaturhan, P., & Yantaç, A. E. (2022). EMOTE: An interactive online tool for designing real-time emotional AR visualizations. Proceedings of AH2022. https://doi.org/10.1145/3532525.3532527
Singh, M., & Kaur, S. (2023). Combining speech, text, and facial expressions for real-time workplace emotion analysis. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-022-13363-4
Ślósarz, L., Jurczyk-Romanowska, E., Rosińczuk, J., & Kazimierska-Zając, M. (2022). Virtual reality as a teaching resource which reinforces emotions in the teaching process. SAGE Open, 12(3). https://doi.org/10.1177/21582440221118083
Swingler, G. H., Volmink, J., & Ioannidis, J. P. A. (2003). Number of reviews per systematic review in medical research. BMJ, 326(7396), 529–531. https://doi.org/10.1136/bmj.326.7396.529
Tiwari, P., Gahlaut, V., Kaushik, M., Rani, P., Shastri, A., & Singh, B. (2023). Advancing 5G connectivity: A comprehensive review of MIMO antennas for 5G applications. International Journal of Antennas and Propagation, 2023, 5906721. https://doi.org/10.1155/2023/5906721
Urquhart, L., Laffer, A., & Miranda, D. (2022). Working with affective computing: Exploring UK public perceptions of AI-enabled workplace surveillance. arXiv. https://arxiv.org/abs/2205.08264
Xiang, Z., & Li, X. (2023). Fusion of transformer and ML-CNN-BiLSTM for network intrusion detection. EURASIP Journal on Wireless Communications and Networking, 2023, Article 79. https://doi.org/10.1186/s13638-023-02279-8
Yang, H., Rahmanti, A. R., Huang, C., & Li, Y. (2022). How can research on artificial empathy be enhanced by applying deepfakes? Journal of Medical Internet Research, 24(3), e29506. https://doi.org/10.2196/29506
Zhou, F., & Li, Q. (2021). Multimodal emotion recognition using deep learning-based fusion methods. En 2021 IEEE International Conference on Multimedia & Expo (ICME). IEEE. https://doi.org/10.1109/ICME51207.2021.9428219
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