Impacto y Perspectivas de la Inteligencia Artificial Generativa en la Educación Superior: Un Estudio sobre la Percepción y Adopción Docente usando el modelo AETGE/GATE

Juana María Padilla Piernas; María del Mar Martín-García

doi:10.31637/epsir-2024-595

Authors

Juana María Padilla Piernas Universidad Católica de Murcia https://orcid.org/0000-0002-6075-3114
María del Mar Martín-García Universidad Isabel I https://orcid.org/0000-0002-1824-7023

DOI:

https://doi.org/10.31637/epsir-2024-595

Keywords:

Generative Artificial Intelligence, UTAUT, TAM, VAM, Higher Education, professors, AETGE, New Technologies

Abstract

Introduction: Generative artificial intelligence (AI) is transforming higher education, offering the opportunity to improve both teaching and learning. This technology enables personalised learning and offers advanced tools for tutoring and predictive analysis of academic outcomes. Methodology: This study utilises the AETGE/GATE model to assess the perceptions of Spanish university lecturers regarding the usefulness, ease of use, perceived value, expectations, social influence, facilitating conditions, and ethical concerns of generative AI. Data were collected through a questionnaire and analysed using SPSS version 29.0.1.0. Results: The analyses reveal no significant differences between men and women in their perceptions of usefulness, ease of use, and perceived value. However, women exhibited greater social influences, facilitating conditions, and ethical concerns. Discussion: The results suggest that, while the overall perception of generative AI is positive, there are gender differences in certain aspects, such as social influence and ethical concerns. This indicates the need for training and support programmes tailored to different demographic groups. Conclusions: This study highlights the perception and adoption of generative AI among university professors, underscoring the necessity to overcome barriers for effective implementation in higher education.

Downloads

Download data is not yet available.

Author Biographies

Juana María Padilla Piernas, Universidad Católica de Murcia

Professor of Business Administration and Management at UCAM, PhD in Social Sciences. Degree in Advertising and Public Relations, Diploma in Business Studies. Master's Degree in Business and Marketing Management and in Hotel Management. Specialised in social networks, consumer behaviour, digital and tourism marketing. She has presented research at national and international conferences. Co-author of articles in academic journals such as International Journal of Scientific Management and Tourism, IJIST, and ‘Digital and Social Media Marketing’ (Springer), RIED, Cuadernos de Turismo among others.

María del Mar Martín-García, Universidad Isabel I

PhD in Economics and Business Studies from the University of Almeria, with an international mention. She researches consumer behaviour, sports tourism and tourism management. She has published in journals such as Healthcare and PASOS, addressing topics such as golf tourism, sports health and digital marketing. He contributes chapters to books on sports tourism and social media marketing. His research includes bibliometric analysis of golf and health, golf image studies and the impact of sporting events on tourism. He is a frequent contributor to academic publications and books specialising in tourism and marketing.

References

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T DOI: https://doi.org/10.1016/0749-5978(91)90020-T

Anusha, L. y Nagaraja, G. S. (2021). Outlier Detection in High Dimensional Data. International Journal of Engineering and Advanced Technology, 10(5), 128-130. https://doi.org/10.35940/ijeat.E2675.0610521 DOI: https://doi.org/10.35940/ijeat.E2675.0610521

Baker, R. S. (2016). Stupid Tutoring Systems, Intelligent Humans. International Journal of Artificial Intelligence in Education, 26(2), 600-614. https://acortar.link/WDpvIH

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I. y Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901. https://doi.org/10.48550/arXiv.2005.14165

Bryson, J. J. (2018). La última década y el futuro del impacto de la IA en la sociedad. En BBVA (Ed.), ¿Hacia una nueva Ilustración? Una década trascendente (pp. 127-159). Turner.

Bryson, J. J. y Winfield, A. F. T. (2017). Standardizing ethical design for artificial intelligence and autonomous systems. Computer, 50(5), 116-119. https://doi.org/10.1109/MC.2017.154 DOI: https://doi.org/10.1109/MC.2017.154

Bryson, J. J. y Theodorou, A. (2019). How society can maintain human-centric artificial intelligence. In M. Toivonen y E. Saari (Eds.), Human-centered digitalization and services (pp. 305-323). Springer. https://doi.org/10.1007/978-981-13-7725-9_16 DOI: https://doi.org/10.1007/978-981-13-7725-9_16

Bryson, J. J., Diamantis, M. E. y Grant, T. D. (2017). Of, for, and by the people: the legal lacuna of synthetic persons. Artificial Intelligence and Law, 25(3), 273-291. https://doi.org/10.1007/s10506-017-9214-9 DOI: https://doi.org/10.1007/s10506-017-9214-9

Bryson, J. J. (2015). Artificial Intelligence and Pro-Social Behaviour. En C. Misselhorn (Ed.), Collective Agency and Cooperation in Natural and Artificial Systems. Philosophical Studies Series (pp. 305-323). Springer, Cham. https://doi.org/10.1007/978-3-319-15515-9_15 DOI: https://doi.org/10.1007/978-3-319-15515-9_15

Cheng, E. W. (2019). Choosing between the theory of planned behavior (TPB) and the technology acceptance model (TAM). Educational Technology Research and Development, 67(1), 21-37. https://doi.org/10.1007/s11423-018-9598-6 DOI: https://doi.org/10.1007/s11423-018-9598-6

Cheng, Y. M. (2019). How does task-technology fit influence cloud-based e-learning continuance and impact? Education + Training, 61(4), 480-499. https://doi.org/10.1108/ET-09-2018-0203 DOI: https://doi.org/10.1108/ET-09-2018-0203

Dans, E. (11 de julio de 2024). China y el interesantísimo proceso de adopción de la inteligencia artificial generativa. Enrique Dans Blog. https://bit.ly/3W0ABIB

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 DOI: https://doi.org/10.2307/249008

Devlin, J., Chang, M. W., Lee, K. y Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. https://doi.org/10.48550/arXiv.1810.04805

Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M. y Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21(3), 719-734. https://doi.org/10.1007/s10796-017-9774-y DOI: https://doi.org/10.1007/s10796-017-9774-y

Gasimova, C. (2023). Privacy and Transparency in an AI-driven World: Does Algorithmic Transparency Fit on Data Privacy Under GDPR? SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1234567 DOI: https://doi.org/10.2139/ssrn.4482889

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. y Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27, 1-9. https://doi.org/10.48550/arXiv.1406.2661

Goodman, B. y Flaxman, S. (2016). EU regulations on algorithmic decision-making and a "right to explanation". En B. Kim, D. M. Malioutov y K. R. Varshney (Eds.), Proceedings of the ICML Workshop on Human Interpretability in Machine Learning (WHI 2016) (pp. 26-30). arXiv. https://doi.org/10.48550/arXiv.1606.08813

Hair, J. F., Black, W. C., Babin, B. J. y Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson.

Hemachandran, K., Verma, P., Pareek, P., Arora, N., Rajesh Kumar, K. V., Ahanger, T. A., Audumbar Pise, A. y Ratna, R. (2022). Artificial intelligence: A universal virtual tool to augment tutoring in higher education. Computational Intelligence and Neuroscience, 2022(1), 1410448. https://doi.org/10.1155/2022/1410448 DOI: https://doi.org/10.1155/2022/1410448

Hochreiter, S. y Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735 DOI: https://doi.org/10.1162/neco.1997.9.8.1735

Humphry, T. y Fuller, A. L. (2023). Potential ChatGPT use in undergraduate chemistry laboratories. Journal of Chemical Education, 100(4), 1434-1436. https://doi.org/10.1021/acs.jchemed.2c0123 DOI: https://doi.org/10.1021/acs.jchemed.3c00006

Ifenthaler, D. y Schumacher, C. (2023). Reciprocal issues of artificial and human intelligence in education. Journal of Research on Technology in Education, 55(1), 1-6. https://doi.org/10.1080/15391523.2022.2040086 DOI: https://doi.org/10.1080/15391523.2022.2154511

Irfan, M., Aldulaylan, F. y Alqahtani, Y. (2023). Ethics and Privacy in Irish Higher Education: A Comprehensive Study of Artificial Intelligence (AI) Tools Implementation at University of Limerick. Global Social Sciences Review, VIII(II), 201-210. https://doi.org/10.31703/gssr.2023(VIII-II).22 DOI: https://doi.org/10.31703/gssr.2023(VIII-II).19

Johanson, G. A. y Brooks, G. P. (2010). Initial scale development: Sample size for pilot studies. Educational and Psychological Measurement, 70(3), 394-400. https://doi.org/10.1177/0013164409355692 DOI: https://doi.org/10.1177/0013164409355692

Kadaruddin. (2023). Empowering Education through Generative AI: Innovative Instructional Strategies for Tomorrow's Learners. International Journal of Business, Law, and Education, 4(2), 618-625. https://doi.org/10.56442/ijble.v4i2.215 DOI: https://doi.org/10.56442/ijble.v4i2.215

Kim, H. W., Chan, H. C. y Gupta, S. (2007). Value-based adoption of mobile internet: an empirical investigation. Decision Support Systems, 43(1), 111-126. https://doi.org/10.1016/j.dss.2005.05.009 DOI: https://doi.org/10.1016/j.dss.2005.05.009

Lee, I. y Yoon, Y. J. (2017). Ethical issues and concerns in artificial intelligence in education. Journal of Intelligence Systems, 27(3), 355-370. https://doi.org/10.1515/jisys-2017-0033 DOI: https://doi.org/10.1515/jisys-2017-0033

Lee, J. G. y Yoon, W. (2017). Big Data Meets Artificial Intelligence: Challenges and Opportunities. Journal of Big Data, 4(1), 12-23. https://acortar.link/uhq1bR

Limayem, M., Hirt, S. G. y Cheung, C. M. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4), 705-737. https://doi.org/10.2307/25148817 DOI: https://doi.org/10.2307/25148817

Márquez, R., Barrios, N., Vera, R. E., Mendez, M. E., Tolosa, L., Zambrano, F. y Li, Y. (2023). A perspective on the synergistic potential of artificial intelligence and product-based learning strategies in biobased materials education. Education for Chemical Engineers, 44, 164-180. https://doi.org/10.1016/j.ece.2023.05.005 DOI: https://doi.org/10.1016/j.ece.2023.05.005

McKinsey & Company. (2024). The state of AI in 2024: Adoption and value creation across industries. McKinsey Global Institute. https://l1nq.com/IiBlw

National Geographic España. (2020). Breve historia visual de la inteligencia artificial. https://bit.ly/3WosQgY

Ongsulee, P. (2017). Artificial Intelligence, Machine Learning and Deep Learning. 2017 Fifteenth International Conference on ICT and Knowledge Engineering, 1-6. https://doi.org/10.1109/ICTKE.2017.8259629 DOI: https://doi.org/10.1109/ICTKE.2017.8259629

OpenAI. (2023). GPT-4 Technical Report. arXiv preprint arXiv:2303.08774. https://doi.org/10.48550/arXiv.2303.08774

Pareto. (s.f.). Historia de la inteligencia artificial: del origen al futuro de la tecnología. Pareto Blog. https://blog.pareto.io/es/historia-de-la-inteligencia-artificial/

Petty, R. E. y Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. Advances in Experimental Social Psychology, 19, 123-205. https://acortar.link/F0Q3Ks DOI: https://doi.org/10.1016/S0065-2601(08)60214-2

Radford, A., Narasimhan, K., Salimans, T. y Sutskever, I. (2018). Improving language understanding by generative pre-training. arXiv preprint arXiv:1810.04805. https://doi.org/10.48550/arXiv.1810.04805

Radford, A., Wu, J., Child, R., Luan, D., Amodei, D. y Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI blog, 1(8), 9. https://bit.ly/3WosQgY

Raghupathi, W. y Raghupathi, V. (2014). Big Data Analytics in Healthcare: Promise and Potential. Health Information Science and Systems, 2(3), 1-10. https://doi.org/10.1186/2047-2501-2-3 DOI: https://doi.org/10.1186/2047-2501-2-3

Sánchez-Ruiz, L. M., Moll-López, S., Nuñez-Pérez, A., Moraño-Fernández, J. A. y Vega-Fleitas, E. (2023). ChatGPT challenges blended learning methodologies in engineering education: A case study in mathematics. Applied Sciences, 13(10), 6039. https://doi.org/10.3390/app13106039 DOI: https://doi.org/10.3390/app13106039

Scherer, R., Siddiq, F. y Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers' adoption of digital technology in education. Computers & Education, 128, 13-35. https://doi.org/10.1016/j.compedu.2018.09.009 DOI: https://doi.org/10.1016/j.compedu.2018.09.009

Stewart, A. J., McCarty, N. y Bryson, J. J. (2018). Explaining parochialism: A causal account for political polarization in changing economic environments. arXiv. https://doi.org/10.1126/sciadv.abd4201 DOI: https://doi.org/10.1126/sciadv.abd4201

Taylor, S. y Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176. https://doi.org/10.1287/isre.6.2.144 DOI: https://doi.org/10.1287/isre.6.2.144

Teo, T. (2011). Factors influencing teachers' intention to use technology: Model development and test. Computers & Education, 57(4), 2432-2440. https://doi.org/10.1016/j.compedu.2011.06.008 DOI: https://doi.org/10.1016/j.compedu.2011.06.008

Thoppilan, R., De Freitas, D., Hall, J., Shazeer, N., Kulshreshtha, A., Cheng, H. T., Jin, A., Bos, T., Baker, L., Du, Y., Li, Y., Lee, H., Zheng, H. S., Ghafouri, A., Menegali, M., Huang, Y., Krikun, M., Lepikhin, D., Qin, J., Chen, D., Xu, Y., Chen, Z., Roberts, A., Bosma, M., Zhao, V., Zhou, Y., Chang. C. C., Krivokon I., Rusch, W., Pickett, M., Srinivasan, P., Man, L., Meier-Hellstern, K., Ringel Morris, M., Doshi, T., Delos Santos, R., Duke, T., Soraker, J., Zevenbergen, B., Prabhakaran, V., Diaz, M., Hutchinson, B., Olson, K., Molina, A., Hoffman-John, E., Lee, J., Aroyo, L., Rajakumar, R., Butryna, A., Lamm, M., Kuzmina, V., Fenton, J., Cohen, A., Bernstein, R., Kurzweil, R., Aguera-Arcas, B., Cui, C., Croak, M., Chi, E. y Le, Q. (2022). LaMDA: Language models for dialog applications. arXiv preprint arXiv:2201.08239. https://doi.org/10.48550/arXiv.2201.08239

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gómez, A. N., Kaiser, Ł. y Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30. https://doi.org/10.48550/arXiv.1706.03762

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 DOI: https://doi.org/10.2307/30036540

Venkatesh, V., Thong, J. Y. 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 DOI: https://doi.org/10.2307/41410412

Whittaker, M., Crawford, K., Dobbe, R., Fried, G., Kaziunas, L., Mathur, V., West, S. M. y Zevenbergen, B. (2023). AI and ethics: A review of global initiatives. Journal of Ethical AI, 5(2), 112-136. https://doi.org/10.1007/s40593-016-0105-0 DOI: https://doi.org/10.1007/s40593-016-0105-0

Yeralan, S. y Lee, L. (2023). Generative AI: Challenges to higher education. Sustainable Engineering and Innovation. https://doi.org/10.37868/sei.v5i2.id196 DOI: https://doi.org/10.37868/sei.v5i2.id196

Zawacki-Richter, O., Marín, V. I., Bond, M. y Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0 DOI: https://doi.org/10.1186/s41239-019-0171-0

Zhai, X., Chu, X., Chai, C., Jong, M., Istenič, A., Spector, M., Liu, J., Yuan, J. y Li, Y. (2021). A Review of Artificial Intelligence (AI) in Education from 2010 to 2020. Complexity, 2021, 8812542. https://doi.org/10.1155/2021/8812542

Zhang, J., Gao, M., Holmes, W., Mavrikis, M. y Ma, N. (2021). Interaction patterns in exploratory learning environments for mathematics: A sequential analysis of feedback and external representations in Chinese schools. Interactive Learning Environments, 29(7), 1211-1228. https://doi.org/10.1080/10494820.2019.1620290 DOI: https://doi.org/10.1080/10494820.2019.1620290

Zhang, K. y Aslan, A. B. (2021). AI technologies for education: Recent research & future directions. Computers and Education: Artificial Intelligence, 2, 100025. https://doi.org/10.1016/j.caeai.2021.100025 DOI: https://doi.org/10.1016/j.caeai.2021.100025

Zhu, Y., Wang, V. L., Wang, Y. A. y Nastos, J. (2018). The relationship between instructors' technology readiness and their intention to use e-learning systems: A case of a technical college in Taiwan. International Journal of E-Learning & Distance Education, 33(1), 1-18. https://doi.org/10.1108/INTR-01-2018-0043 DOI: https://doi.org/10.1108/INTR-01-2018-0043

Zohny, H., McMillan, J. y King, M. (2023). Ethics of generative AI. Journal of Medical Ethics, 49(2), 79-80. https://doi.org/10.1136/medethics-2022-108079 DOI: https://doi.org/10.1136/jme-2023-108909