Self-perception and usefulness of generative artificial intelligence among pre-service teachers
DOI:
https://doi.org/10.5944/ried.45480Keywords:
generative artificial intelligence, ChatGPT, teacher training, self-perception, usefulness, educationAbstract
The advent of Generative Artificial Intelligence (GenAI) in education presents opportunities, but it also raises ethical and pedagogical challenges. In this context, it is imperative to comprehend how pre-service teachers perceive this technology. The present study analysed the self-perception of 174 pre-service teachers regarding the application of GenAI in education. Seven dimensions (Familiarity, Relevance, Practical Skills, Barriers, Confidence, Ethical-Social Impact, and Expectations) were measured in relation to GenAI. In addition, the usefulness of ChatGPT as a tool for designing Learning Situations (LSs) was assessed after a training experience with this system. Descriptive statistics and Spearman correlations were calculated, and a network of correlations between the seven dimensions was visualised. Differences between degrees were also explored. The findings indicated medium-to-high levels of self-perception, suggesting a very positive evaluation of ChatGPT's usefulness and a high level of satisfaction with its use. Confidence emerged as a central node in the correlation network, exhibiting close associations with Relevance, Barriers, Ethical-social impact, and Expectations. This underscores its pivotal role in the adoption of these technologies. Similarly, most participants adopted a critical stance towards GenAI, checking the responses generated by ChatGPT rather than passively accepting them. In conclusion, while there is a favourable attitude towards integrating GenAI into education, future teachers demand specific training to use it pedagogically and express concern about the ethical implications of such integration.
Downloads
References
Al-Abdullatif, A. M. (2024). Modeling teachers’ acceptance of generative artificial intelligence use in higher education: The role of AI literacy, intelligent TPACK, and perceived trust. Education Sciences, 14(11), 1209. https://doi.org/10.3390/educsci14111209
Al-Abdullatif, A. M., & Alsubaie, M. A. (2024). ChatGPT in learning: Assessing students’ use intentions through the lens of perceived value and the influence of AI literacy. Behavioral Sciences, 14(9), 845. https://doi.org/10.3390/bs14090845
Alasadi, E. A., & Baiz, C. R. (2023). Generative AI in education and research: Opportunities, concerns, and solutions. Journal of Chemical Education, 100(8), 2965-2971. https://doi.org/10.1021/acs.jchemed.3c00323
Alba Pastor, C. (2022). Enseñar pensando en todos los estudiantes: El modelo de diseño universal para el aprendizaje (DUA). Ediciones SM.
Aravena Castillo, F. (2013). Developing the collaborative model in the initial formation: The autoperception of the professional performance of the beginner teacher in action. Estudios Pedagógicos, 39(1), 27-44. https://doi.org/10.4067/S0718-07052013000100002
Arias, F. G. (2012). El proyecto de investigación. Episteme.
Bayly-Castaneda, K., Ramírez-Montoya, M.-S., & Morita-Alexander, A. (2024). Crafting personalized learning paths with AI for lifelong learning: A systematic literature review. Frontiers in Education, 9, Article 1424386. https://doi.org/10.3389/feduc.2024.1424386
Bearman, M., & Ajjawi, R. (2023). Learning to work with the black box: Pedagogy for a world with artificial intelligence. British Journal of Educational Technology, 54(5), 1160-1173. https://doi.org/10.1111/bjet.13337
Buyakova, K. I., Dmitriev, Ya. A., Ivanova, A. S., Feshchenko, A. V., & Yakovleva, K. I. (2024). Students’ and teachers’ attitudes towards the use of tools with generative artificial intelligence at the university. The Education and Science Journal, 26(7), 160-193. https://doi.org/10.17853/1994-5639-2024-7-160-193
Carranza Alcántar, M. del R., Macías González, G. G., Gómez Rodríguez, H., Jiménez Padilla, A. A., & Jacobo Montes, F. M. (2024). Percepciones docentes sobre la integración de aplicaciones de IA generativa en el proceso de enseñanza universitario. REDU. Revista de Docencia Universitaria, 22(2), 21-40. https://doi.org/10.4995/redu.2024.22027
Currie, G. M. (2025). Generative artificial intelligence in nuclear medicine education. Journal of Nuclear Medicine Technology, 53(1), 72-79. https://doi.org/10.2967/jnmt.124.268323
Denny, P., Prather, J., Becker, B. A., Finnie-Ansley, J., Hellas, A., Leinonen, J., Luxton-Reilly, A., Reeves, B. N., Santos, E. A., & Sarsa, S. (2024). Computing education in the era of generative AI. Communications of the ACM, 67(2), 56-67. https://doi.org/10.1145/3624720
Espinoza-San Juan, J., Raby, M. D., & Sagredo-Lillo, E. (2024). Validación de un cuestionario sobre las percepciones y usos de la IA-Gen entre estudiantes de pedagogía. RISTI: Revista Ibérica de Sistemas e Tecnologias de Informação, (70), 574-585.
Gozalo-Brizuela, R., & Garrido-Merchán, E. E. (2024). A survey of generative AI applications. Journal of Computer Science, 20(8), 801-818. https://doi.org/10.3844/jcssp.2024.801.818
Haroud, S., & Saqri, N. (2025). Generative AI in higher education: Teachers’ and students’ perspectives on support, replacement, and digital literacy. Education Sciences, 15(4), 396. https://doi.org/10.3390/educsci15040396
Hernández-Sampieri, R., & Mendoza-Torres, C. P. (2023). Metodología de la investigación. McGraw-Hill.
Ishmuradova, I. I., Zhdanov, S. P., Kondrashev, S. V., Erokhova, N. S., Grishnova, E. E., & Volosova, N. Y. (2025). Pre-service science teachers’ perception on using generative artificial intelligence in science education. Contemporary Educational Technology, 17(3), ep579. https://doi.org/10.30935/cedtech/16207
Islam, G., & Greenwood, M. (2024). Generative artificial intelligence as hypercommons: Ethics of authorship and ownership. Journal of Business Ethics, 192(3), 659-663. https://doi.org/10.1007/s10551-024-05741-9
Ivanov, S., Soliman, M., Tuomi, A., Alkathiri, N. A., & Al-Alawi, A. N. (2024). Drivers of generative AI adoption in higher education through the lens of the theory of planned behaviour. Technology in Society, 77, 102521. https://doi.org/10.1016/j.techsoc.2024.102521
Jo, H. (2024). From concerns to benefits: A comprehensive study of ChatGPT usage in education. International Journal of Educational Technology in Higher Education, 21, 35. https://doi.org/10.1186/s41239-024-00471-4
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
Kelly, A., Sullivan, M., & Strampel, K. (2023). Generative artificial intelligence: University student awareness, experience, and confidence in use across disciplines. Journal of University Teaching & Learning Practice, 20(6). https://doi.org/10.53761/1.20.6.12
Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022). Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence, 3, 100101. https://doi.org/10.1016/j.caeai.2022.100101
Lozano, A., & Blanco Fontao, C. (2023). Is the education system prepared for the irruption of artificial intelligence? A study on the perceptions of students of primary education degree from a dual perspective: Current pupils and future teachers. Education Sciences, 13(7), 733. https://doi.org/10.3390/educsci13070733
Markos, A., Prentzas, J., & Sidiropoulou, M. (2024). Pre-service teachers’ assessment of ChatGPT’s utility in higher education: SWOT and content analysis. Electronics, 13(10), 1985. https://doi.org/10.3390/electronics13101985
Miao, F., & Cukurova, M. (2024). AI competency framework for teachers. UNESCO. https://doi.org/10.54675/ZJTE2084
Moorhouse, B. L., & Kohnke, L. (2024). The effects of generative AI on initial language teacher education: The perceptions of teacher educators. System, 122, 103290. https://doi.org/10.1016/j.system.2024.103290
Moorhouse, B. L., Wan, Y., Wu, C., Kohnke, L., Ho, T. Y., & Kwong, T. (2024). Developing language teachers’ professional generative AI competence: An intervention study in an initial language teacher education course. System, 125, 103399. https://doi.org/10.1016/j/system.2024.103399
Mouta, A., Torrecilla-Sánchez, E. M., & Pinto-Llorente, A. M. (2025). Comprehensive professional learning for teacher agency in addressing ethical challenges of AIED: Insights from educational design research. Education and Information Technologies, 30, 3343-3387. https://doi.org/10.1007/s10639-024-12946-y
Ng, D. T. K., Leung, J. K. L., Su, J., Ng, R. C. W., & Chu, S. K. W. (2023). Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. Educational Technology Research and Development, 71(1), 137-161. https://doi.org/10.1007/s11423-023-10203-6
Nyaaba, M., & Zhai, X. (2024). Generative AI professional development needs for teacher educators. Journal of AI, 8(1), 1-13. https://doi.org/10.61969/jai.1385915
Ogunleye, B., Zakariyyah, K. I., Ajao, O., Olayinka, O., & Sharma, H. (2024). A systematic review of generative AI for teaching and learning practice. Education Sciences, 14(6), 636. https://doi.org/10.3390/educsci14060636
Okunade, A. I. (2024). The role of artificial intelligence in teaching of science education in secondary schools in Nigeria. European Journal of Computer Science and Information Technology, 12(1), 57-67. https://doi.org/10.37745/ejcsit2013/vol12n15767
Omar, A., Shaqour, A. Z., & Khlaif, Z. N. (2024). Attitudes of faculty members in Palestinian universities toward employing artificial intelligence applications in higher education: Opportunities and challenges. Frontiers in Education, 9, 1414606. https://doi.org/10.3389/feduc.2024.1414606
Pinto-Llorente, A. M., Izquierdo-Álvarez, V., & Dolcet-Negre, M. M. (2025). Autopercepción y utilidad de la inteligencia artificial generativa en docentes en formación [Open Science Framework]. https://osf.io/c83dh
R Core Team. (2024). R: A language and environment for statistical computing (Version 4.4.3) [Computer software]. R Foundation for Statistical Computing. https://www.r-project.org/
Ruiz-Rojas, L. I., Acosta-Vargas, P., De-Moreta-Llovet, J., & Gonzalez-Rodriguez, M. (2023). Empowering education with generative artificial intelligence tools: Approach with an instructional design matrix. Sustainability, 15(15), Article 11524. https://doi.org/10.3390/su151511524
Saihi, A., Ben-Daya, M., & Hariga, M. (2025). The moderating role of technology proficiency and academic discipline in AI-chatbot adoption within higher education: Insights from a PLS-SEM analysis. Education and Information Technologies, 30, 5843-5881. https://doi.org/10.1007/s10639-024-13023-0
Sánchez Vera, M. del M. (2024). La inteligencia artificial como recurso docente: Usos y posibilidades para el profesorado. Educar, 60(1), 33-47. https://doi.org/10.5565/rev/educar.1810
Sánchez-Prieto, J. C., Izquierdo-Álvarez, V., del Moral-Marcos, M. T., & Martínez-Abad, F. (2025). Inteligencia artificial generativa para autoaprendizaje en educación superior: Diseño y validación de una máquina de ejemplos. RIED-Revista Iberoamericana de Educación a Distancia, 28(1), 59-81. https://doi.org/10.5944/ried.28.1.41548
Storey, V. C., Yue, W. T., Zhao, J. L., & Lukyanenko, R. (2025). Generative artificial intelligence: Evolving technology, growing societal impact, and opportunities for information systems research. Information Systems Frontiers. https://doi.org/10.1007/s10796-025-10581-7
Tai-Han, L., Hsing-Yi, C., Ming-Jr, J., Chih-Kai, C., Cherng-Lih, P., Guo-Shiou, L., Jyh-Cherng, Y., Ming-Shen, D., Cheng-Ping, Y., & Hung-Sheng, S. (2024). An advanced machine learning model for a web-based artificial intelligence-based clinical decision support system application: Model development and validation study. Journal of Medical Internet Research, 26, e56022. https://doi.org/10.2196/56022
UNESCO. (2023). Education 2030 agenda. https://www.unesco.org/en/digital-education/artificial-intelligence
Ventura-León, J. L., & Caycho-Rodríguez, T. (2017). El coeficiente omega: Un método alternativo para la estimación de la confiabilidad. Revista Latinoamericana de Ciencias Sociales, Niñez y Juventud, 15(1), 625-627.
Wang, K., Ruan, Q., Zhang, X., Fu, C., & Duan, B. (2024). Pre-service teachers’ GenAI anxiety, technology self-efficacy, and TPACK: Their structural relations with behavioral intention to design GenAI-assisted teaching. Behavioral Sciences, 14(5), 373. https://doi.org/10.3390/bs14050373
Wang, Y. Y., & Wang, Y. S. (2022). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619-634. https://doi.org/10.1080/10494820.2019.1674887
Whitbread, M., Hayes, C., Prabhakar, S., & Upsher, R. (2025). Exploring university staff’s perceptions of using generative artificial intelligence at university. Education Sciences, 15(3), 367. https://doi.org/10.3390/educsci15030367
Zhang, C., & Villanueva, L. E. (2023). Generative artificial intelligence preparedness and technological competence. International Journal of Education and Humanities, 11(2), 164-170. https://doi.org/10.54097/ijeh.v11i2.13753
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Ana María Pinto-Llorente, Vanessa Izquierdo-Álvarez, Marta M. Dolcet-Negre
This work is licensed under a Creative Commons Attribution 4.0 International License.
The articles that are published in this journal are subject to the following terms:
1. The authors grant the exploitation rights of the work accepted for publication to RIED, guarantee to the journal the right to be the first publication of research understaken and permit the journal to distribute the work published under the license indicated in point 2.
2. The articles are published in the electronic edition of the journal under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. You can copy and redistribute the material in any medium or format, adapt, remix, transform, and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
3. Conditions for self-archiving. Authors are encouraged to disseminate electronically the OnlineFirst version (assessed version and accepted for publication) of its articles before publication, always with reference to its publication by RIED, favoring its circulation and dissemination earlier and with this a possible increase in its citation and reach among the academic community.
