Technostress and perception of Artificial Intelligence in adolescent schoolchildren from Peruvian public institutions
Tecnoestrés y percepción de la Inteligencia Artificial en adolescentes escolares de instituciones públicas peruanasMain Article Content
Background: The analysis of technological fatigue and representations of emerging cognitive technologies in high school students is fundamental for designing pedagogical interventions that mitigate digital barriers and promote a critical integration of innovations in the classroom. Objective: To determine the relationship between technostress and attitudes toward Artificial Intelligence (AI) in secondary students from public educational institutions in Metropolitan Lima, considering perceptions of both variables, their specific dimensions, and the association between them. Methodology: Through a quantitative, non-experimental, cross-sectional, descriptive, and correlational study, with a stratified probability sample of 1,109 students. The RED-Technostress Scale (α = 0.90-0.95) and the AI Attitudes Questionnaire (α = 0.943), both validated, were applied. Results: Technostress affects 54.2% of students at a moderate level and 13.5% at a high level. The most prevalent dimensions are addiction (40.4%), ineffectiveness (40.0%), and fatigue (37.2%). Neutral (58.6%) and low (30.0%) attitudes toward AI predominate, with only 11.4% being favorable. There is a significant inverse correlation (rs = -0.380); students with low technostress show more positive attitudes toward AI (M=3.58) than those with high levels (M=2.41). Conclusion: It is confirmed that technological discomfort constitutes a barrier to the acceptance of emerging innovations and the introduction of intelligent technologies in education. It is recommended to implement digital literacy through technology regulation workshops, strengthening competencies, and practical experiences that transform emotional affinity into solid knowledge.
Contexto: El análisis de la fatiga tecnológica y las representaciones sobre tecnologías cognitivas emergentes en estudiantes secundarios resulta fundamental para diseñar intervenciones pedagógicas que mitiguen barreras digitales y promuevan una integración crítica de innovaciones en el aula. Objetivo: determinar la relación entre el tecnoestrés y las actitudes hacia la Inteligencia Artificial en estudiantes de secundaria de instituciones educativas públicas de Lima Metropolitana, considerando las percepciones sobre ambas variables, sus dimensiones específicas y la asociación entre ellas. Metodología: mediante un estudio cuantitativo, no experimental, transversal, descriptivo y correlacional, con una muestra probabilística estratificada de 1109 estudiantes, a los que se aplicaron la Escala RED-Tecnoestrés (α = 0,90-0,95) y el Cuestionario de Actitudes hacia IA (α = 0,943), ambos válidos. Resultados: El tecnoestrés afecta al 54,2% de los estudiantes en nivel moderado y al 13,5% en nivel alto. Las dimensiones más prevalentes son la adicción (40,4%), la ineficacia (40,0%) y la fatiga (37,2%). Predominan las posturas regulares hacia la Inteligencia Artificial (58,6%) y poco positivas (30,0%), con solo 11,4% favorables. Existe correlación inversa significativa (rₛ = -0,380); estudiantes con bajo tecnoestrés muestran actitudes más positivas hacia la Inteligencia Artificial (M=3,58) que aquellos con nivel alto (M=2,41). Conclusión: Se comprueba que el malestar tecnológico constituye una barrera para la aceptación de innovaciones emergentes y la introducción de las tecnologías inteligentes en la educación. Se recomienda implementar la alfabetización digital con talleres de regulación tecnológica, fortalecimiento de competencias y experiencias prácticas que transformen la afinidad emocional en conocimientos sólidos.
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Araya, G., Armesto, M., Contreras, N., Vega, A., Salazar, G. y Lay, N. (2025). Contextual study of technostress in higher education: psychometric evidence for the TS4US Scale from Lima, Peru. Sustainability, 17(15), 6974. https://doi.org/10.3390/su17156974
Asensio, A., Aguilar, A., Masluk, B., Gascón, S., Sánchez, M. A. y Sánchez, R. (2023). Social support as a mediator in the relationship between technostress or academic stress and health: analysis by gender among university students. Frontiers in Psychology, 14, 1236825. https://doi.org/10.3389/fpsyg.2023.1236825
Bieńkowska, I., Przybysz, M., Konieczny, J., Góźdź, J., Kitlińska, M., Polok, K. y Nadelson, L. S. (2025). Analysis of the use of artificial intelligence (AI) by elementary and secondary school students in the context of completing school assignments. The Journal of Educational Research, 118(6), 643-662. https://doi.org/10.1080/00220671.2025.2510399
Bochniarz, K. T., Czerwiński, S. K., Sawicki, A. y Atroszko, P. A. (2022). Attitudes to AI among high school students: Understanding distrust towards humans will not help us understand distrust towards AI. Personality Individual Differences, 185, 111299. https://doi.org/10.1016/j.paid.2021.111299
Bondanini, G., Giorgi, G., Ariza, A., Vega, A. y Andreucci, P. (2020). Technostress dark side of technology in the workplace: A scientometric analysis. International journal of environmental research public health, 17(21), 8013. https://doi.org/10.3390/ijerph17218013
Calla, Y. R., Nomberto, V. R., Mendoza, B., Ortiz, R. y Rimascca, I. K. (2025). Actitudes hacia la inteligencia artificial y su relación con la satisfacción académica: el rol mediador de la comodidad en su uso educativo. Bordón: Revista de pedagogía, 77(4), 117-137. https://doi.org/10.13042/Bordon.2025.112199
Choi, J.-I., Yang, E. y Goo, E.-H. (2024). The effects of an ethics education program on artificial intelligence among middle school students: Analysis of perception and attitude changes. Applied Sciences, 14(4), 1588. https://doi.org/10.3390/app14041588
Cohen, A., Soffer, T. y Henderson, M. (2022). Students' use of technology and their perceptions of its usefulness in higher education: International comparison. Journal of Computer Assisted Learning, 38(5), 1321-1331. https://doi.org/10.1111/jcal.12678
Darwin, Rusdin, D., Mukminatien, N., Suryati, N., Laksmi, E. D. y %J, M. (2024). Critical thinking in the AI era: An exploration of EFL students’ perceptions, benefits, and limitations. 11(1), 2290342. https://doi.org/10.1080/2331186X.2023.2290342
Daud, N. M. (2025). From innovation to stress: analyzing hybrid technology adoption and its role in technostress among students. International Journal of Educational Technology in Higher Education, 22(1), 31. https://doi.org/10.1186/s41239-025-00529-x
Daud, N. M. (2026). Beyond ‘Good’or ‘Bad’: Investigating Trust and Techno-Resistance in Postgraduate Students’ Voluntary Use of AI Technologies. Technology in Society, 103213. https://doi.org/10.1016/j.techsoc.2026.103213
Estrada, E. G., Gallegos, N. A., Huaypar, K. H., Paredes, Y. y Quispe, R. (2021). Tecnoestrés en estudiantes de una universidad pública de la Amazonía peruana durante la pandemia COVID-19. Revista Brasileira De Educação Do Campo, 6(e12777), 1-19. https://doi.org/10.20873/uft.rbec.e12777
Gull, M., Kaur, N., Abuhasan, W. M., Kandi, S. y Nair, S. M. (2026). A comprehensive review of psychosocial, academic, and psychological issues faced by university students in India. Annals of Neurosciences, 33(1), 90-101. https://doi.org/10.1177/09727531241306571
Kozak, J. y Fel, S. (2024). How sociodemographic factors relate to trust in artificial intelligence among students in Poland and the United Kingdom. Scientific Reports, 14(1), 28776. https://doi.org/10.1038/s41598-024-80305-5
La Torre, G., De Leonardis, V. y Chiappetta, M. (2020). Technostress: how does it affect the productivity and life of an individual? Results of an observational study. Public health, 189, 60-65. https://doi.org/10.1016/j.puhe.2020.09.013
López, M. D., Contreras, A. y Peraza, L. A. (2025). Diferencias contextuales en el tecnoestrés de estudiantes de preparatorias públicas y privadas en Ciudad del Carmen, Campeche, México. RIDE. Revista Iberoamericana para la Investigación y el Desarrollo Educativo, 15(30). https://doi.org/10.23913/ride.v15i30.2369
Mallma, Á. W. (2025). Resiliencia académica y satisfacción en universitarios: modelo de mediación en entorno digital. Educación y Humanismo, 27(49). https://doi.org/10.17081/eduhum.27.49.7514
Mansfield, K. L., Ghai, S., Hakman, T., Ballou, N., Vuorre, M. y Przybylski, A. K. (2025). From social media to artificial intelligence: improving research on digital harms in youth. The Lancet Child Adolescent Health, 9(3), 194-204. https://doi.org/10.1016/S2352-4642(24)00332-8
Nascimento, L., Correia, M. F. y Califf, C. B. (2025). Techno-eustress under remote work: A longitudinal study in higher education teachers. Education Information Technologies, 30(12), 16633-16670. https://doi.org/10.1007/s10639-025-13459-y
Qi, C. y Yang, N. (2024). Digital resilience and technological stress in adolescents: A mixed-methods study of factors and interventions. Education and Information Technologies, 29(14), 19067-19113. https://doi.org/10.1007/s10639-024-12595-1
Rai, G. D. (2026). Quality of work life, job satisfaction, technostress and psychological well-being in blended learning: a moderated mediation model. European Journal of Training and Development, 50(1-2), 189-216. https://doi.org/10.1108/EJTD-09-2024-0128
Rizzo, R., Fusto, G., Marino, S., Castagnola, I., Parano, C., Pappalardo, X. G. y Parano, E. (2025). Molecular and Neurobiological Imbalance from the Use of Technological Devices During Early Child Development Stages. Children, 12(7), 909. https://doi.org/10.3390/children12070909
Romeu, T., Romero, M., Guitert, M. y Baztán, P. (2025). Desafíos de la Inteligencia Artificial generativa en educación superior: fomentando su uso crítico en el estudiantado. RIED-Revista Iberoamericana de Educación a Distancia, 28(2), 189-231. https://doi.org/10.5944/ried.28.2.43535
Sánchez, A., Flores, I. C., Veytia, M. G. y Azuara, V. (2021). Tecnoestrés y adicción a las tecnologías de la información y las comunicaciones (TIC) en universitarios mexicanos: diagnóstico y validación de instrumento. Formación universitaria, 14(4), 123-132. http://dx.doi.org/10.4067/S0718-50062021000400123
Sharma, S. y Gupta, B. (2023). Investigating the role of technostress, cognitive appraisal and coping strategies on students' learning performance in higher education: a multidimensional transactional theory of stress approach. Information Technology and People, 36(2), 626-660. https://doi.org/10.1108/ITP-06-2021-0505
Sok, S., Heng, K. y Pum, M. (2025). Investigating high school students’ attitudes toward the use of AI in education: Evidence from Cambodia. Sage Open, 15(3), 21582440251353575. https://doi.org/10.1177/21582440251353
Tomaylla, Y., Pérez, G., Gutiérrez, O., Chicaña, S. y Duche, A. (2025). Artificial intelligence on student satisfaction in higher education: the role of positive attitude, continuous use intention, and perceived usefulness. JOTSE: Journal of Technology and Science Education, 15(3), 629-646. https://doi.org/10.3926/jotse.3422
Wang, C., Boerman, S. C., Kroon, A. C., Möller, J. y H de Vreese, C. (2025). The artificial intelligence divide: Who is the most vulnerable? New Media and Society, 27(7), 3867-3889. https://doi.org/10.1177/14614448241232345
Ye, J.-H., Wu, Y.-F., Nong, W., Wu, Y.-T., Ye, J.-N. y Sun, Y. (2023). The association of short-video problematic use, learning engagement, and perceived learning ineffectiveness among Chinese vocational students. Healthcare, 11(2), 161. https://doi.org/10.3390/healthcare11020161
Zhai, C., Wibowo, S. y Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review. Smart learning environments, 11(1), 28. https://doi.org/10.1186/s40561-024-00316-7
Zivi, P., Malatesta, G., Mascia, M. L., Diana, M. G., Di Domenico, A., Penna, M. P. y Palmiero, M. (2025). Protective factors against technostress in secondary school teachers. Scientific Reports, 15(1), 35554. https://doi.org/10.1038/s41598-025-19604-4