Análisis sobre el uso de las herramientas de inteligencia artificial interactiva en el entorno universitario

  1. Castro-López, Adrián 1
  2. Cervero, Antonio 1
  3. Álvarez-Blanco, Lucía 1
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

Journal:
Revista Tecnología, Ciencia y Educación

ISSN: 2444-250X,  2444-2887

Year of publication: 2025

Issue: 30

Pages: 37-66

Type: Article

DOI: 10.51302/TCE.2025.22219 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Revista Tecnología, Ciencia y Educación

Abstract

Technological tools based on artificial intelligence have been a great advance in terms of knowledge generation, but they have also posed difficulties for the educational system. In this context, the present study aims to identify the factors that influence the use of interactive artificial intelligence tools by university students (men and women) and to analyse their impact on academic performance. For this purpose, an ad hoc questionnaire was designed and answered by a sample of 306 university students. Descriptive analysis, reliability and discriminant validity of the scales, and apparently unrelated regression were carried out. The results show that four factors influence the use of interactive artificial intelligence tools (performance expectations, hedonic motivation, price value and habit) and that the use of such tools leads to poorer academic performance of students. This could be due to poor pedagogical planning or to students feeling free to use these tools.

Bibliographic References

  • Alshammari, S. H. y Alshammari, M. H. (2024). Factors affecting the adoption and use of ChatGPT in higher education. International Journal of Information and Communication Technology Education, 20(1), 1-16. http://dx.doi.org/10.4018/IJICTE.339557
  • Altememy, H. A., Mohammed, B. A., Hsony, M. K., Hassan, A. Y., Mazhair, R., Dawood, I. I., Al Jouani, I. S., Zearah, S. A. y Sharif, H. R. (2023). The influence of the artificial intelligence capabilities of higher education institutions in Iraq on students' academic performance: the role of AI-based technology application as a mediator. Eurasian Journal of Educational, 124, 267-282.
  • An, X., Chai, C. S., Li, Y., Zhou, Y. y Yang, B. (2023). Modeling students' perceptions of artificial intelligence assisted language learning. Computer Assisted Language Learning, 1-22. https://doi.org/10.1080/09588221.2023.2246519
  • Anderson, J. C. y Gerbing, D. W. (1988). Structural equation modelling in practice: a review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423. https://doi.org/10.1037/0033-2909.103.3.411
  • Astigarraga, E. y Carrera Farran, X. (2018). Necesidades a futuro y situación actual de las competencias en educación superior en el contexto de España. Revista Digital de Investigación en Docencia Universitaria, 12(2), 35-58. https://doi.org/10.19083/ridu.2018.731
  • Baabdullah, A. M. (2018). Consumer adoption of mobile social network games (M-SNGs) in Saudi Arabia: the role of social influence, hedonic motivation and trust. Technology in Society, 53, 91-102. https://doi.org/10.1016/j.techsoc.2018.01.004
  • Bilquise, G., Ibrahim, S. y Salhieh, S. M. (2024). Investigating student acceptance of an academic advising chatbot in higher education institutions. Education and Information Technologies, 29, 6.357-6.382. https://doi.org/10.1007/s10639-023-12076-x
  • Boden, M. (2016). AI. It's Nature and Future. OUP Oxford.
  • Chatterjee, S. y Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: a quantitative analysis using structural equation modelling. Education and Information Technologies, 25, 3.443-3.463. https://doi.org/10.1007/s10639-020-10159-7
  • Chen, H. L., Widaros, G. V. y Sutrisno, H. (2020). A ChatBot for learning Chinese: learning achievement and technology acceptance. Journal of Educational Computing Research, 58(6), 1.161-1.189. https://doi.org/10.1177/0735633120929622
  • Dahri, N. A., Yahaya, N., Al-Rahmi, W. M., Vighio, M. S., Alblehai, F., Soomro, R. B. y Shutaleva, A. (2024). Investigating AI-based academic support acceptance and its impact on students' performance in Malaysian and Pakistani higher education institutions. Education and Information Technologies. https://doi.org/10.1007/s10639-024-12599-x
  • Delcker, J., Heil, J., Ifenthaler, D., Seufert, S. y Spirgi, L. (2024). First-year students AI-competence as a predictor for intended and de facto use of AI-tools for supporting learning processes in higher education. International Journal of Educational Technology in Higher Education, 21, 1-13. https://doi.org/0.1186/s41239-024-00452-7
  • Deng, X. y Yu, Z. (2023). A meta-analysis and systematic review of the effect of chatbot technology use in sustainable education. Sustainability, 15(4), 1-19. https://doi.org/10.3390/su15042940
  • Fontes de Gracia, S., García Gallego, C., Garriga Trillo, A. J., Pérez-Llantada Rueda, M.ª C. y Sarriá Sánchez, E. (2005). Diseños de investigación en psicología. UNED.
  • Fornell, C. y Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: algebra and statistics. Journal of Marketing Research, 18(3), 382-388. https://doi.org/10.1177/002224378101800313
  • Foroughi, B., Senali, M. G., Iranmanesh, M., Khanfar, A., Ghobakhloo, M., Annamalai, N. y Naghmeh-Abbaspour, B. (2023). Determinants of intention to use ChatGPT for educational purposes: findings from PLS-SEM and fsQCA. International Journal of Human-Computer Interaction, 40(17), 4.501-4.520. https://doi.org/10.1080/10447318.2023.2226495
  • Gado, S., Kempen, R., Lingelbach, K. y Bipp, T. (2022). Artificial intelligence in psychology: how can we enable psychology students to accept and use artificial intelligence? Psychology Learning & Teaching, 21(1), 37-56. https://doi.org/10.1177/14757257211037149
  • Gao, Z., Chea, J.-H., Lim, X.-J. y Luo, X. (2024). Enhancing academic performance of business students using generative AI: an interactive-constructive-active-passive (ICAP) self-determination perspective. The International Journal of Management Education, 22(2). https://doi.org/10.1016/j.ijme.2024.100958
  • Guevara Alban, G. P., Verdesoto Arguello, A. E. y Castro Molina, N. E. (2020). Metodologías de investigación educativa (descriptivas, experimentales, participativas y de investigación-acción). Revista Científica Mundo de la Investigación y el Conocimiento, 4(3), 163-173.
  • Hair, J. F., Babin, B. J., Black, W. C. y Anderson, R. E. (2019). Multivariate Data Analysis (8.ª ed.). Cengage.
  • Han, S. H. y Liu, M. (2022). Developing an inclusive Q&A chatbot in massive open online courses. En M. M. Rodrigo, N. Matsuda, A. I. Cristea y V. Dimitrova (Eds.), Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners' and Doctoral Consortium. AIED 2022 (pp. 10-15). Cham. https://doi.org/10.1007/978-3-031-11647-6_2
  • Henseler, J., Ringle, C. M. y Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115-135. https://doi.org/10.1007/s11747-014-0403-8
  • Horodyski, P. (2023). Recruiter's perception of artificial intelligence (AI)-based tools in recruitment. Computers in Human Behavior Reports, 10. https://doi.org/10.1016/j.chbr.2023.100298
  • Jovanovic, M. y Campbell, M. (2022). Generative artificial intelligence: trends and prospects. Computer, 55(19), 107-112. https://doi.org/10.1109/MC.2022.3192720
  • Lai, C.-L. (2021). Exploring university students' preferences for ai-assisted learning environment: a drawing analysis with activity theory framework. Educational Technology & Society, 24(4), 1-15. https://www.jstor.org/stabe/48629241
  • Li, L., Peng, W. y Rheu, M. (2023). Factors predicting intentions of adoption and continued use of artificial intelligence chatbots for mental health: examining the role of UTAUT Model, stigma, privacy concerns, and artificial intelligence hesitancy. Telemedicine and E-Health, 30(3), 722-730. https://doi.org/10.1089/tmj.2023.0313
  • Nawaz, S. S., Sanjeetha, M. B., Al Murshidi, G., Riyath, M. I., Yamin, F. B. y Mohamed, R. (2024). Acceptance of ChatGPT by undergraduates in Sri Lanka: a hybrid approach of SEM-ANN. Interactive Technology and Smart Education. https://doi.org/10.1108/ITSE-11-2023-0227
  • Ni, A. y Cheung, A. (2023). Understanding secondary students' continuance intention to adopt AI-powered intelligent tutoring system for English learning. Education and Information Technologies, 28(3), 3.191-3.216. https://doi.org/10.1007/s10639-022-11305-z
  • O'Dea, X. y O'Dea, M. (2023). Is artificial intelligence really the next big thing in learning and teaching in higher education? A conceptual paper. Journal of University Teaching and Learning Practice, 20(5). https://doi.org/10.53761/1.20.5.05
  • Pacheco-Mendoza, S., Guevara, C., Mayorga-Albán, A. y Fernández-Escobar, J. (2023). artificial intelligence in higher education: a predictive model for academic performance. Education Sciences, 13. https://doi.org/10.3390/educsci13100990
  • Peremans, K. y Van Aelst, S. (2018). Robust inference for seemingly unrelated regression models. Journal of Multivariate Analysis, 167, 212-224. https://doi.org/10.1016/j.jmva.2018.05.002
  • Pfeuffer, N., Baum, L., Stammer, W., Abdel-Karim, B. M., Schramowski, P., Bucher, A. M., Hügel, C., Rohde, G., Kersting, K. y Hinz, O. (2023). Explanatory interactive machine learning. Business & Information Systems Engineering, 65(6), 677-701. https://doi.org/10.1007/s12599-023-00806-x
  • Priyadarshini, I., Mohanty, P. R. y Cotton, C. (2021). Analyzing some elements of technological singularity using regression methods. Computers, Materials & Continua, 67(3), 3.229-3.247. https://doi.org/10.32604/cmc.2021.015250
  • Qu, J., Zhao, Y. y Xie, Y. (2022). Artificial intelligence leads the reform of education models. Systems Research and Behavioral Science, 39(3), 581-588. https://doi.org/10.1002/sres.2864
  • Rodríguez-Sabiote, C., Úbeda-Sánchez, Á. M., Olmedo-Moreno, E. M.ª y Álvarez-Rodríguez, J. (2022). Importancia de los indicadores metodológicos-analíticos en el nivel de concreción de los diseños de investigación de los artículos científicos en educación. Revista de Investigación Educativa, 40(2), 365-383. https://doi.org/10.6018/rie.441741
  • Romero-Rodríguez, J. M.ª, Ramírez-Montoya, M.ª S., Buenestado-Fernández, M. y Lara-Lara, F. (2023). Use of ChatGPT at university as a tool for complex thinking: students' perceived usefulness. Journal of New Approaches in Educational Research, 12(2), 323-339. https://doi.org/10.7821/naer.2023.7.1458
  • Rouhiainen, L. (2018). Artificial Intelligence: 101 Things You Must Know Today about Our Future. Createspace.
  • Sharma, S., Singh, G., Gaur, L. y Afaq, A. (2022). Exploring customer adoption of autonomous shopping systems. Telematics and Informatics, 73. https://doi.org/10.1016/j.tele.2022.101861
  • Sobaih, A. E. E., Elshaer, I. y Hasanein, A. M. (2024). Examining students' acceptance and use of ChatGPT in Saudi Arabian higher education. European Journal of Investigation in Health, Psychology and Education, 14(3), 709-721. https://doi.org/10.3390/ejihpe14030047
  • Strzelecki, A. (2024). Students' acceptance of ChatGPT in higher education: an extended unified theory of acceptance and use of technology. Innovative Higher Education, 49, 223-245. https://doi.org/10.1007/s10755-023-09686-1
  • Tamilmani, K., Rana, N. P., Wamba, S. F. y Dwivedi, R. (2021). The extended unified theory of acceptance and use of technology (UTAUT2): a systematic literature review and theory evaluation. International Journal of Information Management, 57. https://doi.org/10.1016/j.ijinfomgt.2020.102269
  • Teng, Z., Cai, Y., Gao, Y., Zhang, X. y Li, X. (2022). Factors affecting learners' adoption of an educational metaverse platform: an empirical study based on an extended UTAUT model. Mobile Information Systems, 2022(1). https://doi.org/10.1155/2022/5479215
  • Tian, W., Ge, J., Zhao, Y. y Zheng, X. (2024). AI chatbots in Chinese higher education: adoption, perception, and influence among graduate students-an integrated analysis utilizing UTAUT and ECM models. Frontiers of Psychology, 15, 1-16. https://doi.org/10.3389/fpsyg.2024.1268549
  • Tiwari, C. K., Bhat, M. A., Khan, S. T., Subramaniam, R. y Khan, M. A. I. (2023). What drives students toward ChatGPT? An investigation of the factors influencing adoption and usage of ChatGPT. Interactive Technology and Smart Education, 21(3). https://doi.org/10.1108/ITSE-04-2023-0061
  • Van Slyke, C., Johnson, R. D. y Sarabadani, J. (2023). Generative artificial intelligence in information systems education: challenges, consequences, and responses. Communications of the Association for Information Systems, 53, 1-21. https://doi.org/10.17705/1CAIS.05301
  • Venkatesh, V., Morris, M. G., Davis, G. B. y Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
  • Venkatesh, V., Thong, J. Y. L. y Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412
  • Venkatesh, V., Thong, J. Y. L. y Xu, X. (2016). Unified theory of acceptance and use of technology: a synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328-376. https://doi.org/10.17705/1jais.00428
  • Wu, W., Zhang, B., Li, S. y Liu, H. (2022). Exploring factors of the willingness to accept AI-assisted learning environments: an empirical investigation based on the UTAUT Model and perceived risk theory. Frontiers in Psychology, 13, 1-10. https://doi.org/10.3389/fpsyg.2022.870777
  • Yu, H. y Guo, Y. (2023). Generative artificial intelligence empowers educational reform: current status, issues, and prospects. Frontiers in Education, 8, 1-10. https://doi.org/10.3389/feduc.2023.1183162
  • Zhang, S. A., Shan, C., Lee, J. S., Che, S. P. y Kim, J. H. (2023). Effect of chatbot-assisted language learning: a meta-analysis. Education and Information Technologies, 28, 15.223-15.243. https://doi.org/10.1007/s10639-023-11805-6
  • Zou, M. y Huang, L. (2023). To use or not to use? Understanding doctoral students' acceptance of ChatGPT in writing through technology acceptance model. Frontiers in Psychology, 14, 1-9. https://doi.org/10.3389/fpsyg.2023.1259531
Data source: Dialnet