¿Qué impulsa a los profesores de L2 a adoptar las tecnologías de IA? Una investigación fenomenológica sobre el papel de los recursos personales y laborales

Autores/as

Palabras clave:

inteligencia artificial, recursos laborales, recursos personales, investigación fenomenológica, profesores de L2
Agencias: This work was supported by the General Project of Guangdong Philosophy and Social Sciences Planning 2022 Empirical Research on the Impact of Government-sponsored Overseas Study on the Professional Development of University Teachers: A Case Study of Guangdong Province. (Grant number: GD22CJY10).

Resumen

Esta investigación fenomenológica profundizó en las contribuciones de los recursos personales y laborales a la adopción de la inteligencia artificial (IA) por parte de los profesores de L2 en sus prácticas docentes. Los datos se recopilaron de 27 profesores de L2 a través de un cuestionario abierto y un marco narrativo, y se analizaron mediante análisis de contenido. Los resultados revelaron que los recursos personales, como la autoeficacia tecnológica, la alfabetización en IA, la apertura a la innovación y la resiliencia, pueden afectar significativamente la adopción de la IA por parte de los profesores de L2. Los recursos laborales, que incluyen el apoyo institucional, la disponibilidad de herramientas eficientes y las oportunidades de desarrollo profesional, también pueden ejercer un impacto directo sobre la disposición de los profesores a adoptar la IA. El estudio contribuye a la comprensión de cómo interactúan estos recursos para moldear el compromiso de los profesores de L2 con la IA y ofrece implicaciones prácticas para las instituciones educativas que buscan mejorar la adopción de la IA entre sus educadores.

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Biografía del autor/a

Professor , School of English Education, Guangdong University of Foreign Studies, China

Yan Wu is Professor of English Education and Teaching at the School of  English Education, Guangdong University of Foreign Studies (GDUFS), Guangzhou, China. She is the dean of  School of English Education in GDUFS, a member of the Guangdong Provincial "New Normal Teaching Guidance Committee", an executive council member of the Higher Education Society of Guangdong Education Association, and a member of the TESOL Expert Committee in China, dissertation review expert for the Ministry of Education in China. Her research interests are English education, English teacher development, and higher education pedagogy.

Ali Derakhshan, Golestan University (Iran) (Corresponding author)

Ali Derakhshan is Professor of Applied Linguistics at the English Language and Literature Department, Golestan University, Gorgan, Iran. He is currently a Yunshan Chair Professor at the School of English Education, Guangdong University of Foreign Studies (GDUFS), Guangzhou, China. As authenticated by the Essential Science Indicators (ESI) Database, Clarivate Analytics shows his name among the world’s top 1% of scientists in 2024. His name appeared in Stanford University’s list of the world’s top 2% of most influential scientists in 2022, 2023, and 2024. His research interests are positive psychology, teacher education, learner individual differences, cross-cultural interpersonal factors in educational psychology, technology in language education, and intercultural communication.

Citas

Al-khresheh, M. H. (2024). Bridging technology and pedagogy from a global lens: Teachers’ perspectives on integrating ChatGPT in English language teaching. Computers and Education: Artificial Intelligence, 6, 100218. https://doi.org/10.1016/j.caeai.2024.100218

Bakker, A. B., & Demerouti, E. (2007). The job demands‐resources model: State of the art. Journal of Managerial Psychology, 22(3), 309–328. https://doi.org/10.1108/02683940710733115

Bakker, A. B., & Demerouti, E. (2017). Job demands-resources theory: Taking stock and looking forward. Journal of Occupational Health Psychology, 22(3), 273–285. https://doi.org/10.1037/ocp00 00056

Bakker, A. B., Demerouti, E., & Sanz-Vergel, A. (2023). Job demands–resources theory: Ten years later. Annual Review of Organizational Psychology and Organizational Behavior, 10(1), 25–53. https://doi.org/10.1146/annurev-orgpsych-120920-053933

Bakker, A. B., Hakanen, J. J., Demerouti, E., & Xanthopoulou, D. (2007). Job resources boost work engagement, particularly when job demands are high. Journal of Educational Psychology, 99(2), 274–284. https://doi.org/10.1037/0022-0663.99.2.274

Barrett, A., & Pack, A. (2023). Not quite eye to AI: Student and teacher perspectives on the use of generative artificial intelligence in the writing process. International Journal of Educational Technology in Higher Education, 20(1), 59. https://doi.org/10.1186/s41239-023-00427-0

Barrios-Beltran, D. (2024). Enriching the teaching-learning experience by using AI tools in the L2 classroom. In F. Pan (Ed.), AI in language teaching, learning, and assessment (pp. 61–77). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-0872-1

Belda-Medina, J., & Kokošková, V. (2024). ChatGPT for language learning: Assessing teacher candidates’ skills and perceptions using the Technology Acceptance Model (TAM). Innovation in Language Learning and Teaching. https://doi.org/10.1080/17501229.2024.2435900

Chen, X., Zou, D., Xie, H., Cheng, G., & Liu, C. (2022). Two decades of artificial intelligence in education. Educational Technology & Society, 25(1), 28–47.

Chocarro, R., Cortiñas, M., & Marcos-Matás, G. (2023). Teachers’ attitudes towards chatbots in education: A technology acceptance model approach considering the effect of social language, bot proactiveness, and users’ characteristics. Educational Studies, 49(2), 295–313. https://doi.org/10.1080/03055698.2020.1850426

Choi, S., Jang, Y., & Kim, H. (2023). Influence of pedagogical beliefs and perceived trust on teachers’ acceptance of educational artificial intelligence tools. International Journal of Human–Computer Interaction, 39(4), 910–922. https://doi.org/10.1080/10447318.2022.2049145

Christina, R., & Panagiotidis, P. (2024). Teachers’ attitudes towards AI integration in foreign language learning: Supporting differentiated instruction and flipped classroom. European Journal of Education, 7(2), 88–104.

Creswell, J. W., & Poth, C. N. (Eds.) (2016). Qualitative inquiry and research design: Choosing among five approaches. Sage publications.

Dai, C. P., & Ke, F. (2022). Educational applications of artificial intelligence in simulation-based learning: A systematic mapping review. Computers and Education: Artificial Intelligence, 3, 100087. https://doi.org/10.1016/j.caeai.2022.100087

Dai, K., & Liu, Q. (2024). Leveraging artificial intelligence (AI) in English as a foreign language (EFL) classes: Challenges and opportunities in the spotlight. Computers in Human Behavior, 108354. https://doi.org/10.1016/j.chb.2024.108354

Derakhshan, A. (2025). EFL students’ perceptions about the role of generative artificial intelligence (GAI)-mediated instruction in their emotional engagement and goal orientation: A motivational climate theory (MCT) perspective in focus. Learning and Motivation, 90, 102114. https://doi.org/10.1016/j.lmot.2025.102114

Derakhshan, A., & Ghiasvand, F. (2024). Is ChatGPT an evil or an angel for second language education and research? A phenomenographic study of research‐active EFL teachers’ perceptions. International Journal of Applied Linguistics, 34(4), 1246–1264. https://doi.org/10.1111/ijal.12561

Derakhshan, A., Teo, T., & Khazaie, S. (2025). Investigating the usefulness of artificial intelligence-driven robots in developing empathy for English for medical purposes communication: The role-play of Asian and African students. Computers in Human Behavior, 162, 108416. https://doi.org/10.1016/j.chb.2024.108416

Divekar, R. R., Drozdal, J., Chabot, S., Zhou, Y., Su, H., Chen, Y., ... & Braasch, J. (2022). Foreign language acquisition via artificial intelligence and extended reality: Design and evaluation. Computer Assisted Language Learning, 35(9), 2332–2360. https://doi.org/10.1080/09588221.2021.1879162

Fathi, J., Rahimi, M., & Derakhshan, A. (2024). Improving EFL learners’ speaking skills and willingness to communicate via artificial intelligence-mediated interactions. System, 121, 103254. https://doi.org/10.1016/j.system.2024.103254

Heidegger, M. (Ed.) (2005). Introduction to phenomenological research. Indiana University Press.

Hsieh, H. F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277–1288. https://doi.org/10.1177/ 1049732305276687

Huang, X., Zou, D., Cheng, G., Chen, X., & Xie, H. (2023). Trends, research issues and applications of artificial intelligence in language education. Educational Technology & Society, 26(1), 112–131.

Ilgun Dibek, M., Sahin Kursad, M., & Erdogan, T. (2024). Influence of artificial intelligence tools on higher order thinking skills: A meta-analysis. Interactive Learning Environments. https://doi.org/10.1080/10494820.2024.2402028

Korucu-Kış, S. (2024). Zone of proximal creativity: An empirical study on EFL teachers’ use of ChatGPT for enhanced practice. Thinking Skills and Creativity, 54, 101639. https://doi.org/10.1016/j.tsc.2024.101639

Lan, Y. (2024). Through tensions to identity-based motivations: Exploring teacher professional identity in Artificial Intelligence-enhanced teacher training. Teaching and Teacher Education, 151, 104736. https://doi.org/10.1016/j.tate.2024.104736

Law, L. (2024). Application of generative artificial intelligence (GenAI) in language teaching and learning: A scoping literature review. Computers and Education Open, 6, 100174. https://doi.org/10.1016/j.caeo.2024.100174

Liu, W., & Wang, Y. (2024). The effects of using AI tools on critical thinking in English literature classes among EFL learners: An intervention study. European Journal of Education. https://doi.org/https://doi.org/10.1111/ejed.12804

Liu, Y., & Chang, P. (2024). Exploring EFL teachers’ emotional experiences and adaptive expertise in the context of AI advancements: A positive psychology perspective. System, 126, 103463. https://doi.org/10.1016/j.system.2024.103463

Lu, L., Wang, C., & Wang, Y. (2024). The contribution of teacher self-efficacy, resilience and emotion regulation to teachers’ well-being: Technology-enhanced teaching context. European Journal of Education. e12755. https://doi.org/https://doi.org/10.1111/ejed.12755

Nassaji, H. (2020). Good qualitative research. Language Teaching Research, 24(4), 427–431. https://doi.org/10.1177/1362168820941288

Shafiee Rad, H. (2024). Revolutionizing L2 speaking proficiency, willingness to communicate, and perceptions through artificial intelligence: A case of Speeko application. Innovation in Language Learning and Teaching, 18(4), 364–379. https://doi.org/10.1080/17501229.2024.2309539

Shen, Y., & Guo, H. (2024). “I feel AI is neither too good nor too bad”: Unveiling Chinese EFL teachers’ perceived emotions in generative AI-mediated L2 classes. Computers in Human Behavior, 161, 108429. https://doi.org/10.1016/j.chb.2024.108429

Sun, P. P., & Mei, B. (2022). Modeling preservice Chinese-as-a-second/foreign-language teachers’ adoption of educational technology: A technology acceptance perspective. Computer Assisted Language Learning, 35(4), 816–839. https://doi.org/10.1080/09588221.2020.1750430

Tram, N. H. M. (2024). Unveiling the drivers of AI integration among language teachers: Integrating UTAUT and AI-TPACK. Computers in the Schools. https://doi.org/10.1080/07380569.2024.2441155

Tram, N. H. M., & Tran-Thanh, V. (2024). The role of supportive environments in shaping EFL teachers’ adoption of ChatGPT. In H. P. Bui & E. Namaziandost (Eds.), Innovations in technologies for language teaching and learning (pp. 55–78). Springer Nature Switzerland.

Tummers, L. G., & Bakker, A. B. (2021). Leadership and job demands-resources theory: A systematic review. Frontiers in Psychology, 12, 722080.

Wei, L. (2023). Artificial intelligence in language instruction: Impact on English learning achievement, L2 motivation, and self-regulated learning. Frontiers in Psychology, 14, 1261955. https://doi.org/10.3389/fpsyg.2023.1261955

Xanthopoulou, D., Bakker, A. B., Demerouti, E., & Schaufeli, W. B. (2009). Reciprocal relationships between job resources, personal resources, and work engagement. Journal of Vocational behavior, 74(3), 235–244. https://doi.org/10.1016/j.jvb.2008.11.003

Xiao, J., Yang, Y., & Li, M. (2025). Empirical study on the feasibility of hybrid-flexible training model for developing teachers’ artificial intelligence competence. Education and Information Technologies. https://doi.org/10.1007/s10639-025-13460-5

Xin, Z., & Derakhshan, A. (2025). From excitement to anxiety: Exploring English as a foreign language learners' emotional experiences in the artificial intelligence‐powered classrooms. European Journal of Education, 60(1), e12845. https://doi.org/10.1111/ejed.12845

Yang, J., & Lou, K. (2024). Exploring the nexus of self-efficacy in digital literacy and technology acceptance: Insights from L2 Chinese teachers. Asia Pacific Journal of Education. https://doi.org/10.1080/02188791.2024.2336247

Yang, Y. F., Tseng, C. C., & Lai, S. C. (2024). Enhancing teachers’ self-efficacy beliefs in AI-based technology integration into English speaking teaching through a professional development program. Teaching and Teacher Education, 144, 104582. https://doi.org/10.1016/j.tate.2024.104582

Yuan, Y. (2024). An empirical study of the efficacy of AI chatbots for English as a foreign language learning in primary education. Interactive Learning Environments, 32(10), 6774–6789. https://doi.org/10.1080/10494820.2023.2282112

Zhang, C., Hu, M., Wu, W., Kamran, F., & Wang, X. (2025). Unpacking perceived risks and AI trust influences pre-service teachers’ AI acceptance: A structural equation modeling-based multi-group analysis. Education and Information Technologies, 30(2), 2645–2672. https://doi.org/10.1007/s10639-024-12905-7

Zhang, C., Schießl, J., Plößl, L., Hofmann, F., & Gläser-Zikuda, M. (2023). Acceptance of artificial intelligence among pre-service teachers: A multigroup analysis. International Journal of Educational Technology in Higher Education, 20(1), 49. https://doi.org/10.1186/s41239-023-00420-7

Zhi, R., & Wang, Y. (2024). On the relationship between EFL students’ attitudes toward artificial intelligence, teachers’ immediacy and teacher-student rapport, and their willingness to communicate. System, 124, 103341. https://doi.org/https://doi.org/10.1016/j.system.2024.103341

Zhou, C., & Hou, F. (2024). Can AI empower L2 education? Exploring its influence on the behavioural, cognitive and emotional engagement of EFL teachers and language learners. European Journal of Education, 59(4), e12750. https://doi.org/10.1111/ejed.12750

FUNDING INFORMATION:

This work was supported by the General Project of Guangdong Philosophy and Social Sciences Planning 2022, entitled ‘Empirical Research on the Impact of Government-sponsored Overseas Study on the Professional Development of University Teachers: A Case Study of Guangdong Province’ (Grant number: GD22CJY10).

Publicado

29-09-2025

Cómo citar

Wu, Y., & Derakhshan, A. (2025). ¿Qué impulsa a los profesores de L2 a adoptar las tecnologías de IA? Una investigación fenomenológica sobre el papel de los recursos personales y laborales. Porta Linguarum Revista Interuniversitaria De Didáctica De Las Lenguas Extranjeras, (XIII). Recuperado a partir de https://revistaseug.ugr.es/index.php/portalin/article/view/33117

Número

Sección

XIII Número Especial "Integración de tecnologías innovadoras en entornos de aprendizaje de idiomas asistido por tecnología (TALL): Perspectivas, aplicaciones y repercusiones"