Emoções e Tecnologia na Educação: uma Revisão com Foco na Motivação
Emotions and Technology in Education: a Review Focused on Motivation
DOI:
https://doi.org/10.33936/cognosis.v10iEE(1).7684Resumo
Este estudo analisa o impacto da inteligência emocional na motivação, no bem-estar subjetivo, no esgotamento acadêmico e no uso de tecnologias afetivas na interação humano-computador (IHC) nesses contextos. Por meio de uma revisão sistemática da literatura, o estudo explora as relações entre emoções, tecnologias educacionais e desempenho acadêmico, destacando a importância de considerar a dimensão emocional no design de ambientes de aprendizagem. O autor baseia-se em fortes postulados em descobertas recentes no âmbito educacional e psicológico, que demonstraram que alunos com maiores níveis de inteligência emocional apresentam maior motivação intrínseca, menor esgotamento acadêmico e maiores níveis de bem-estar subjetivo. Ao mesmo tempo, analisa como as tecnologias permitem a adaptação de estratégias pedagógicas e psicológicas, melhorando a personalização da aprendizagem e, portanto, contribuindo para o desempenho educacional desejado.
PALAVRAS-CHAVE: Inteligência emocional; tecnologias afetivas; interação humano-computador; motivação; ética educacional.
Downloads
Referências
Alenezi, A. (2024). The effect of emotional intelligence on higher education: A pilot study on the interplay between artificial intelligence, emotional intelligence, and e-learning. Multidisciplinary Journal for Education, Social and Technological Sciences, 11(2), 51-77.https://doi.org/10.4995/muse.2024.21367
Al-Saadawi, H. F. T., Das, B., & Das, R. (2024). A systematic review of trimodal affective computing approaches: Text, audio, and visual integration in emotion recognition and sentiment analysis. Expert Systems with Applications, 124852. https://doi.org/10.1016/j.eswa.2024.124852
Aly, M. (2024). Revolutionizing online education: Advanced facial expression recognition for real-time student progress tracking via deep learning model. Multimedia Tools and Applications, 1-40. https://doi.org/10.1007/s11042-024-19392-5
Arksey, H., & O’Malley, L. (2005). Scoping studies: towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19–32. https://doi.org/10.1080/1364557032000119616
Arockia, V. J., Vettriselvan, R., Rajesh, D., Velmurugan, P. R. R., & Cheelo, C. (2025). Leveraging AI and Learning Analytics for Enhanced Distance Learning: Transformation in Education. In AI and Learning Analytics in Distance Learning (pp. 179-206). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-7195-4.ch008
Bazurto Palma, C. J., & Ocaña Garzón, M. (2023). The use of a virtual environment to improve students’ Listening skills: a learning analytics approach. Ciencia Latina Revista Científica Multidisciplinar, 7(2), 2937-2953. https://doi.org/10.37811/cl_rcm.v7i2.5537
Chaudhary, G. (2024). Unveiling the black box: Bringing algorithmic transparency to AI. Masaryk University Journal of Law and Technology, 18(1), 93-122.
Chen, J., Mokmin, N. A. M., & Qi, S. (2025). Generative AI-powered arts-based learning in middle school history: Impact on achievement, motivation, and cognitive load. The Journal of Educational Research, 1-13. https://doi.org/10.1080/00220671.2025.2510395
Chen, S., Cheng, H., & Huang, Y. (2024). Emotion Recognition in Self-Regulated Learning: Advancing Metacognition through AI-Assisted Reflections. In Trust and Inclusion in AI-Mediated Education: Where Human Learning Meets Learning Machines (pp. 185-212). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-64487-0_9
Ding, L., & Zou, D. (2024). Automated writing evaluation systems: A systematic review of Grammarly, Pigai, and Criterion with a perspective on future directions in the age of generative artificial intelligence. Education and Information Technologies, 29(11), 14151-14203. https://doi.org/10.1007/s10639-023-12402-3
Fernández-Herrero, J. (2024). Evaluating recent advances in affective intelligent tutoring systems: A scoping review of educational impacts and future prospects. https://doi.org/10.3390/educsci14080839
Flores, H. I. O. (2023). Human computer interaction’s insights into the recognition of love: a comprehensive framework. Tierra Infinita, 9(1), 228-245. https://doi.org/10.32645/26028131.1254
Flores, H. I. O. (2025). Creating an Adaptive Voice and Language Model Capable of Emotional Response and Self-Profiling to Emulate User Personality. Ciencia Latina Revista Científica Multidisciplinar, 9(1), 3454-3471. https://doi.org/10.37811/cl_rcm.v9i1.16093
Flores, H. I. O., & Luna, A. (2024). AI for Psychological Profiles: Advances, Challenges, and Future Directions. Ciencia Latina Revista Científica Multidisciplinar, 8(3), 10592-10609. https://doi.org/10.37811/cl_rcm.v8i3.12221
Glickman, M., & Sharot, T. (2025). How human–AI feedback loops alter human perceptual, emotional and social judgements. Nature Human Behaviour, 9(2), 345-359. https://doi.org/10.1038/s41562-024-02077-2
Guallichico, G., Ocaña, M., Tejada, C., Bautista, C. (2023). Assessing Digital Competence Through Teacher Training in Early Education Teachers. In: Botto-Tobar, M., Zambrano Vizuete, M., Montes León, S., Torres-Carrión, P., Durakovic, B. (eds) Applied Technologies. ICAT 2022. Communications in Computer and Information Science, vol 1757. Springer, Cham. https://doi.org/10.1007/978-3-031-24978-5_6
Härting, R. C., Schmidt, S., & Krum, D. (2021). Potentials of Emotionally Sensitive Applications Using Machine Learning. In Human Centred Intelligent Systems: Proceedings of KES-HCIS 2020 Conference (pp. 207-219). Springer Singapore. https://doi.org/10.1007/978-981-15-5784-2_17
Kaklauskas, A., Abraham, A., Ubarte, I., Kliukas, R., Luksaite, V., Binkyte-Veliene, A., ... & Kaklauskiene, L. (2022). A review of AI cloud and edge sensors, methods, and applications for the recognition of emotional, affective and physiological states. Sensors, 22(20), 7824-7824. https://doi.org/10.3390/s22207824
Khediri, N., Ben Ammar, M., & Kherallah, M. (2024). A Real-time Multimodal Intelligent Tutoring Emotion Recognition System (MITERS). Multimedia Tools and Applications, 83(19), 57759-57783. https://doi.org/10.1007/s11042-023-16424-4
Kim, J., Yang, K., Min, J., & White, B. (2022). Hope, fear, and consumer behavioral change amid COVID‐19: Application of protection motivation theory. International Journal of Consumer Studies, 46(2), 558-574. https://doi.org/10.1111/ijcs.12700
Kivijärvi, H., & Pärnänen, K. (2023). Instrumental usability and effective user experience: Interwoven drivers and outcomes of Human-Computer interaction. International Journal of Human–Computer Interaction, 39(1), 34-51. https://doi.org/10.1080/10447318.2021.2016236
Le, U. P. N., Nguyen, A. T. T., Van Nguyen, A., Huynh, V. K., Le Bui, C. T., & Nguyen, A. L. T. (2024, August). How Do Emotional Support and Emotional Exhaustion Affect the Relationship Between Incivility and Students’ Subjective Well-Being?. In Disruptive Technology and Business Continuity: Proceedings of The 5th International Conference on Business (ICB 2023) (pp. 237-248). Singapore: Springer Nature Singapore.
Lee, Y. F., Hwang, G. J., & Chen, P. Y. (2022). Impacts of an AI-based cha bot on college students’ after-class review, academic performance, self-efficacy, learning attitude, and motivation. Educational technology research and development, 70(5), 1843-1865.https://doi.org/10.1007/s11423-022-10142-8
Lin, C. C., Huang, A. Y., & Lu, O. H. (2023). Artificial intelligence in intelligent tutoring systems toward sustainable education: a systematic review. Smart Learning Environments, 10(1), 1-22. https://doi.org/10.1186/s40561-023-00260-y
Liu, C. Y., & fShi, B. (2024). Affective foundations in AI-human interactions: Insights from evolutionary continuity and interspecies communications. Computers in Human Behavior, 161, 108406. https://doi.org/10.1016/j.chb.2024.108406
Luna, A., Ocaña, M., Rodríguez-Moreno, J., & Ortíz-Colón, A. M. (2025). Establishing the foundation for technology adoption: profiles of military students in the digital age. Frontiers in Psychology, 16, 1593326. https://doi.org/10.3389/fpsyg.2025.1593326
Marey, A., Arjmand, P., Alerab, A. D. S., Eslami, M. J., Saad, A. M., Sanchez, N., & Umair, M. (2024). Explainability, transparency and black box challenges of AI in radiology: Impact on patient care in cardiovascular radiology. Egyptian Journal of Radiology and Nuclear Medicine, 55(1), 183. https://doi.org/10.1186/s43055-024-01356-2
Mattioli, M., & Cabitza, F. (2024). Not in my face: Challenges and ethical considerations in automatic face emotion recognition technology. Machine Learning and Knowledge Extraction, 6(4), 2201-2231. https://doi.org/10.3390/make6040109
McStay, A. (2020). Emotional AI, soft biometrics and the surveillance of emotional life: An unusual consensus on privacy. Big Data & Society, 7(1), 2053951720904386. https://doi.org/10.1177/2053951720904386
Namaziandost, E., & Rezai, A. (2024). Interplay of academic emotion regulation, academic mindfulness, L2 learning experience, academic motivation, and learner autonomy in intelligent computer-assisted language learning: A study of EFL learners. System, 125, 103419. https://doi.org/10.1016/j.system.2024.103419
Ngo, D., Nguyen, A., Dang, B., & Ngo, H. (2024). Facial expression recognition for examining emotional regulation in synchronous online collaborative learning. International Journal of Artificial Intelligence in Education, 34(3), 650-669. https://doi.org/10.1007/s40593-023-00378-7
Nicolescu, L., & Tudorache, M. T. (2022). Human-computer interaction in customer service: the experience with AI chatbots—a systematic literature review. Electronics, 11(10), 1579. https://doi.org/10.3390/electronics11101579
Ocaña, M., García-Cañarte, A., & Rosales, M. (2024). Enhancing EFL Vocabulary Through Short Stories: Insights from Learning Analytics. In International Conference on Intelligent Information Technology (pp. 438-449). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-87701-8_32
Ocaña, M., Almeida, E., Albán, S. (2022). How Did Children Learn in an Online Course During Lockdown?: A Piagetian Approximation. In: Botto-Tobar, M., Cruz, H., Díaz Cadena, A., Durakovic, B. (eds) Emerging Research in Intelligent Systems. CIT 2021. Lecture Notes in Networks and Systems, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-030-96046-9_20
Pacheco-Mendoza, S., Guevara, C., Mayorga-Albán, A., & Fernández-Escobar, J. (2023). Artificial intelligence in higher education: A predictive model for academic performance. Education Sciences, 13(10), 990.https://doi.org/10.3390/educsci13100990
Pekrun, R. (2024). Control-value theory: From achievement emotion to a general theory of human emotions. Educational Psychology Review, 36(3), 1-36. https://doi.org/10.1007/s10648-024-09909-7
Roemmich, K., & Andalibi, N. (2021). Data subjects' conceptualizations of and attitudes toward automatic emotion recognition-enabled wellbeing interventions on social media. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2), 1-34. https://doi.org/10.1145/3476049
Salloum, S. A. (2024). Trustworthiness of the AI. In Artificial intelligence in education: The power and dangers of ChatGPT in the classroom (pp. 643-650). Cham: Springer Nature Switzerland.
Sarnou, D., & Sarnou, H. (2023). Highlighting the Consequences of Ignoring Children's Emotions in Schools: Case of 30 pupils in 3 Algerian Primary Schools. Academy Journal of Educational Sciences, 6(1), 44-51.https://doi.org/10.31805/acjes.1091001
Schmitz-Hübsch, A., Stasch, S. M., Becker, R., Fuchs, S., & Wirzberger, M. (2022). Affective Response Categories-Toward Personalized Reactions in Affect-Adaptive Tutoring Systems. Frontiers in artificial intelligence, 5, 873056. https://doi.org/10.3389/frai.2022.873056
Shah, M., & Sureja, N. (2025). A comprehensive review of bias in deep learning models: Methods, impacts, and future directions. Archives of Computational Methods in Engineering, 32(1), 255-267. https://doi.org/10.1007/s11831-024-10134-2
Shi, L. (2025). The integration of advanced AI-enabled emotion detection and adaptive learning systems for improved emotional regulation. Journal of Educational Computing Research, 63(1), 173-201. https://doi.org/10.1177/07356331241296890
Sirisha, N., Mageswari, P., Raj, V. M., Kumar, S., Priya, R. V., & Ananthi, S. (2025, May). Emotion Centric Artificial Intelligence Driven Engagement Systems for Adaptive Learning Environments Personalized Knowledge Acquisition and Cognitive Skill Enhancement. In International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024) (pp. 528-540). Atlantis Press. https://doi.org/10.2991/978-94-6463-718-2_46
Villamarín, A., Chumaña, J., Narváez, M., Guallichico, G., Ocaña, M., & Luna, A. (2023, November). Artificial Intelligence in the Detection of Autism Spectrum Disorders (ASD): a Systematic Review. In International Conference on Intelligent Vision and Computing (pp. 21-32). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-71388-0_3
Yin, J., Goh, T. T., & Hu, Y. (2024). Interactions with educational chatbots: the impact of induced emotions and students’ learning motivation. International Journal of Educational Technology in Higher Education, 21(1), 1-23. https://doi.org/10.1186/s41239-024-00480-3
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), 1-47. https://doi.org/10.3390/educsci15010065
Zhao, G., Li, Y., & Xu, Q. (2022). From emotion AI to cognitive AI. International Journal of Network Dynamics and Intelligence, 65-72. https://doi.org/10.53941/ijndi0101006
Downloads
Publicado
Como Citar
Edição
Seção
Licença
Copyright (c) 2025 Hernan Isaac Ocana Flores

Este trabalho está licenciado sob uma licença Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.