Keywords

Inteligencia Artificial Generativa, educación superior, percepciones estudiantiles, ChatGPT, análisis cualitativo, tecnología educativa

Abstract

La rápida integración de la Inteligencia Artificial Generativa (IAG) en el ámbito educativo pone de relieve la necesidad de obtener evidencias empíricas sobre las percepciones del estudiantado, con el fin de orientar políticas institucionales, estrategias pedagógicas y prácticas sociales. Este estudio exploró las percepciones de estudiantes universitarios italianos respecto a los beneficios y riesgos de la IAG en los ámbitos personal, social y académico. Un total de 1.347 estudiantes de 24 universidades italianas respondió un cuestionario en línea. Este artículo se centra en el análisis de dos preguntas abiertas dirigidas a indagar las ventajas y desventajas percibidas de ChatGPT y sistemas similares, mediante codificación manual con el software MaxQDA. Los resultados muestran que el estudiantado adopta principalmente una visión instrumental de la IAG: los beneficios operativos representaron el 41,6 % de las respuestas, seguidos de ventajas informativas (14,3 %) y educativas (10,9 %). Las desventajas más mencionadas fueron los riesgos cognitivos y de aprendizaje (29,6 %), comportamientos problemáticos (23,6 %) y limitaciones técnicas (15,4 %). Estos hallazgos revelan una postura ambivalente: si bien valoran la eficiencia y el apoyo al aprendizaje que ofrece la IAG, también expresan preocupaciones por la dependencia intelectual, la disminución del pensamiento crítico y las amenazas a la integridad académica. Este estudio aporta varias contribuciones clave: constituye la primera investigación cualitativa a gran escala (N = 1.347) sobre las percepciones de la GenAI en la educación superior italiana, al tender un puente entre la profundidad cualitativa y la generalizabilidad cuantitativa; ofrece perspectivas culturalmente situadas desde un contexto no anglófono, enriqueciendo así una literatura predominantemente anglocéntrica; y Comunicar, 84, XXXIV, 2026 proporciona recomendaciones fundamentadas empíricamente para el desarrollo de políticas institucionales, basadas en las experiencias reales del estudiantado más que en supuestos teóricos. Estos datos ofrecen orientaciones valiosas para desarrollar políticas éticas de uso, formación docente estructurada e iniciativas de acompañamiento estudiantil orientadas a promover competencias de gobernanza tecnológica.

References

Acosta-Enriquez, B. G., Arbulú Ballesteros, M. A., Huamaní Jordan, O., López Roca, C., & Saavedra Tirado, K. (2024). Analysis of college students’ attitudes toward the use of ChatGPT in their academic activities: effect of intent to use, verification of information and responsible use. BMC Psychology, 12(1), 255. https://doi.org/10.1186/s40359-024-01764-z
Ajlouni, A. O., Abd-Alkareem Wahba, F., & Salem Almahaireh , A. (2023). Students’ Attitudes Towards Using ChatGPT as a Learning Tool: The Case of the University of Jordan. International Journal of Interactive Mobile Technologies (iJIM), 17(18), 99-117. https://doi.org/10.3991/ijim.v17i18.41753
Al-Obaydi, L. H., Pikhart, M., & Klimova, B. (2023). ChatGPT and the general concepts of education: Can artificial intelligence-driven chatbots support the process of language learning? International Journal of Emerging Technologies in Learning (iJET), 18(21), 39-50. https://doi.org/10.3991/ijet.v18i21.42593
Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., et al. (2024). A meta systematic review of artificial intelligence in higher education: a call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21(1), 4. https://doi.org/10.1186/s41239-023-00436-z
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), 43. https://doi.org/10.1186/s41239-023-00411-8
Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, 100118. https://doi.org/10.1016/j.caeai.2022.100118
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228-239. https://doi.org/10.1080/14703297.2023.2190148
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2025). Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies. Computers & Education, 227, 105224. https://doi.org/10.1016/j.compedu.2024.105224
Erito, S. N. P. (2023). Exploring ESP Students’ Perception toward the Potential of Artificial Intelligence to Promote Students’ Self-Efficacy in English Writing Skill. Journal of English Language Learning (JELL), 7(2), 457-464. https://doi.org/10.31949/jell.v7i2.7598
European Commission. (2022). Ethical guidelines on the use of artificial intelligence (AI) and data in teaching and learning for educators. Publications Office of the European Union. https://doi.org/10.2766/153756
European Parliament & Council. (2024). Regulation (EU) 2024/1689 of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union [incl. Annex III]. https://bit.ly/4mOSKoT
Famaye, T., Bailey, C. S., Adisa, I., & Irgens, G. A. (2024). “What Makes ChatGPT Dangerous is Also What Makes It Special”: High-School Student Perspectives on the Integration or Ban of Artificial Intelligence in Educational Contexts. International Journal of Technology in Education, 7(2), 174-199. https://doi.org/10.46328/ijte.651
Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2024). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International, 61(3), 460-474. https://doi.org/10.1080/14703297.2023.2195846
Galloway, A. (2005). Non-Probability Sampling. In K. Kempf-Leonard (Ed.), Encyclopedia of Social Measurement (pp. 859-864). Elsevier. https://doi.org/10.1016/B0-12-369398-5/00382-0
Ghotbi, N., Ho, M. T., & Mantello, P. (2022). Attitude of college students towards ethical issues of artificial intelligence in an international university in Japan. AI & Society, 37(1), 283-290. https://doi.org/10.1007/s00146-021-01168-2
Hasanein, A. M., & Sobaih, A. E. E. (2023). Drivers and Consequences of ChatGPT Use in Higher Education: Key Stakeholder Perspectives. European Journal of Investigation in Health, Psychology and Education, 13(11), 2599-2614. https://doi.org/10.3390/ejihpe13110181
Holland, A., & Ciachir, C. (2025). A qualitative study of students’ lived experience and perceptions of using ChatGPT: immediacy, equity and integrity. Interactive Learning Environments, 33(1), 483-494. https://doi.org/10.1080/10494820.2024.2350655
Holmes, W., & Tuomi, I. (2022). State of the art and practice in AI in education. European Journal of Education, 57(4), 542-570. https://doi.org/10.1111/ejed.12533
Huedo-Martínez, S., Molina-Carmona, R., & Llorens-Largo, F. (2018). Study on the Attitude of Young People Towards Technology. In P. Zaphiris & A. Ioannou (Eds.), Learning and Collaboration Technologies. Learning and Teaching (pp. 26-43). Springer International Publishing. https://doi.org/10.1007/978-3-319-91152-6_3
Idroes, G. M., Noviandy, T. R., Maulana, A., Irvanizam, I., Jalil, Z., Lensoni, L., et al. (2023). Student Perspectives on the Role of Artificial Intelligence in Education: A Survey-Based Analysis. Journal of Educational Management and Learning, 1(1), 8-15. https://doi.org/10.60084/jeml.v1i1.58
Jisc. (2024, February 1). Generative AI – A Primer [updated ed.]. Jisc. https://bit.ly/47CQuMX
Karakose, T., & Tülübas, T. (2023). How Can ChatGPT Facilitate Teaching and Learning: Implications for Contemporary Education. Educational Process: International Journal, 12(4), 7-16. https://doi.org/10.22521/edupij.2023.124.1
Kayal?, B., Yavuz, M., Balat, ?., & Çal??an, M. (2023). Investigation of student experiences with ChatGPT-supported online learning applications in higher education. Australasian Journal of Educational Technology, 39(5), 20-39. https://doi.org/10.14742/ajet.8915
Kosmyna, N., Hauptmann, E., Yuan, Y. T., Situ, J., Liao, X.-H., Beresnitzky, A. V., et al. (2025). Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task. arXiv preprint arXiv:2506.08872. https://doi.org/10.48550/arXiv.2506.08872
Lai, C. Y., Cheung, K. Y., & Chan, C. S. (2023). Exploring the role of intrinsic motivation in ChatGPT adoption to support active learning: An extension of the technology acceptance model. Computers and Education: Artificial Intelligence, 5, 100178. https://doi.org/10.1016/j.caeai.2023.100178
Lee, Y.-F., Hwang, G.-J., & Chen, P.-Y. (2022). Impacts of an AI-based chabot 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
Liu, Z., & Zhang, W. (2024). A qualitative analysis of Chinese higher education students’ intentions and influencing factors in using ChatGPT: a grounded theory approach. Scientific Reports, 14(1), 18100. https://doi.org/10.1038/s41598-024-65226-7
Ma, J., Wang, P., Li, B., Wang, T., Pang, X. S., & Wang, D. (2025). Exploring User Adoption of ChatGPT: A Technology Acceptance Model Perspective. International Journal of Human–Computer Interaction, 41(2), 1431-1445. https://doi.org/1 0.1080/10447318.2024.2314358
Malmström, H., Stöhr, C., & Ou, A. W. (2023). Chatbots and other AI for learning: A survey of use and views among university students in Sweden (Chalmers Studies in Communication and Learning in Higher Education, 1). Chalmers University of Technology. https://doi.org/10.17196/cls.csclhe/2023/01
Montenegro-Rueda, M., Fernández-Cerero, J., Fernández-Batanero, J. M., & López-Meneses, E. (2023). Impact of the Implementation of ChatGPT in Education: A Systematic Review. Computers, 12(8), 153. https://doi.org/10.3390/computers12080153
MUR. (2024). Istituzioni universitarie accreditate. http://bit.ly/4mFuAxa
Ngo, T. T. A. (2023). The Perception by University Students of the Use of ChatGPT in Education. International Journal of Emerging Technologies in Learning (iJET), 18(17), 4-19. https://doi.org/10.3991/ijet.v18i17.39019
Pinho, C., Franco, M., & Mendes, L. (2021). Application of innovation diffusion theory to the E-learning process: higher education context. Education and Information Technologies, 26(1), 421-440. https://doi.org/10.1007/s10639-020-10269-2
Popenici, S. (2022). Artificial Intelligence and Learning Futures: Critical Narratives of Technology and Imagination in Higher Education. Routledge. https://doi.org/10.4324/9781003266563
Rahiman, H. U., & Kodikal, R. (2024). Revolutionizing education: Artificial intelligence empowered learning in higher education. Cogent Education, 11(1), 2293431. https://doi.org/10.1080/2331186X.2023.2293431
Rawas, S. (2024). ChatGPT: Empowering lifelong learning in the digital age of higher education. Education and Information Technologies, 29(6), 6895-6908. https://doi.org/10.1007/s10639-023-12114-8
Roberts, H., Babuta, A., Morley, J., Thomas, C., Taddeo, M., & Floridi, L. (2023). Artificial intelligence regulation in the United Kingdom: a path to good governance and global leadership? Internet Policy Review, 12(2), 1-31. https://doi.org/10.14763/2023.2.1709
Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit Spewer or the End of Traditional Assessments in Higher Education? Journal of Applied Learning & Teaching, 6(1), 342-363. https://doi.org/10.37074/jalt.2023.6.1.9
Saif, N., Khan, S. U., Shaheen, I., Alotaibi, F. A., Alnfiai, M. M., & Arif, M. (2024). Chat-GPT; validating Technology Acceptance Model (TAM) in education sector via ubiquitous learning mechanism. Computers in Human Behavior, 154, 108097. https://doi.org/10.1016/j.chb.2023.108097
Sullivan, M., Kelly, A., & McLaughlan, P. (2023). ChatGPT in higher education: Considerations for academic integrity and student learning. Journal of Applied Learning & Teaching, 6(1), 31-40. https://doi.org/10.37074/jalt.2023.6.1.17
Tierney, A., Peasey, P., & Gould, J. (2025). Student perceptions on the impact of AI on their teaching and learning experiences in higher education. Research and Practice in Technology Enhanced Learning, 20, 005. https://doi.org/10.58459/rptel.2025.20005
UNESCO. (2023). Guidance for Generative AI in Education and Research. UNESCO. https://doi.org/10.54675/EWZM9535
Xu, X., Su, Y., Zhang, Y., Wu, Y., & Xu, X. (2024). Understanding learners’ perceptions of ChatGPT: A thematic analysis of peer interviews among undergraduates and postgraduates in China. Heliyon, 10(4), e26239. https://doi.org/10.1016/j.heliyon.2024.e26239
Yilmaz, H., Maxutov, S., Baitekov, A., & Balta, N. (2023). Student Attitudes towards Chat GPT: A Technology Acceptance Model Survey. International Educational Review, 1(1), 57-83. https://doi.org/10.58693/ier.114
Yu, H. (2024). The application and challenges of ChatGPT in educational transformation: New demands for teachers’ roles. Heliyon, 10(2), e24289. https://doi.org/10.1016/j.heliyon.2024.e24289
Zhang, W., Li, A. W., & Wu, C. (2025). University students’ perceptions of using generative AI in translation practices. Instructional Science, 53(4), 633-655. https://doi.org/10.1007/s11251-025-09705-y

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This research was supported by the Interdepartmental Research Project ‘AI—Artificial Intelligence’ at the University of Udine.

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Received: 2025-07-20 | Reviewed: 2025-08-29 | Accepted: 2025-09-02 | Online First: 2026-01-02 | Published: 2026-01-04

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Claudio Melchior., Manuela Farinosi. (2026). Del apoyo a la dependencia: explorando las percepciones de los estudiantes sobre la IA generative. Comunicar, 34(84). 10.5281/zenodo.18115566

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