Integración y competencia en inteligencia artificial: predictores del rendimiento académico en estudiantes universitarios.
Resumen
Esta investigación analiza la relación entre la integración de herramientas de Inteligencia Artificial en las prácticas de estudio, la competencia en su uso, y el Rendimiento académico percibido por estudiantes universitarios. Se aplicó un cuestionario a 331 estudiantes universitarios, evaluando tres constructos mediante escalas Likert. El análisis factorial confirmó tres factores que explican el 75.4% de la varianza total. Los resultados del análisis de regresión múltiple revelan que la integración de IA y la competencia en su uso predicen significativamente el Rendimiento académico, explicando el 65.3% de la varianza. Se identificó una relación sinérgica entre ambas variables, sugiriendo un "círculo virtuoso" donde el desarrollo de competencias facilita mayor integración y viceversa con implicaciones para políticas educativas y prácticas pedagógicas.
Citas
Anderson, L.W. y D. Krathwohl (Eds.) (2001). A Taxonomy for Learning, Teaching and Assessing: a Revision of Bloom's Taxonomy of Educational Objectives. New York: Longman.
Asad, M. M., Shahzad, S., Shah, S. H., Sherwani, F., & Almusharraf, N. M. (2024). ChatGPT as artificial intelligence-based generative multimedia for English writing pedagogy: Challenges and opportunities from an educator's perspective. International Journal of Information and Learning Technology, 41(5), 490-506. https://doi.org/10.1108/ijilt-02-2024-0021
Bancoro, J. C. (2024). The relationship between artificial intelligence (AI) usage and academic performance of business administration students. International Journal of Asian Business and Management, 3(1), 27-48. https://doi.org/10.55927/ijabm.v3i1.7876
Bates, A. W. (2015). Teaching in a digital age: Guidelines for designing teaching and learning. BCcampus. https://pressbooks.bccampus.ca/teachinginadigitalagev3/
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901. https://doi.org/10.18653/v1/2021.mrl-1.1
Cai, L., Msafiri, M. M., & Kangwa, D. (2025). Exploring the impact of integrating AI tools in higher education using the Zone of Proximal Development. Education and Information Technologies, 30(6), 7191-7264. https://doi.org/10.1007/s10639-024-13112-0
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264-75278. https://doi.org/10.1109/ACCESS.2020.2988510
Dabbagh, N., & Castañeda, L. (2020). The PLE as a framework for developing agency in lifelong learning. Educational Technology Research and Development, 68, 3041-3055. https://doi.org/10.1007/s11423-020-09787-9
Engeström, Y. (2001). Expansive learning at work: Toward an activity-theoretical reconceptualization. Journal of Education and Work, 14(1), 133–156. https://doi.org/10.1080/13639080020028747
Field, A. (2013). Discovering statistics using IBM SPSS statistics. SAGE.
Garrison, D. R., & Kanuka, H. (2004). Blended learning: Uncovering its transformative potential in higher education. The Internet and Higher Education, 7(2), 95-105. https://doi.org/10.1016/j.iheduc.2004.02.001
Hair Jr., J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate Data Analysis (5th ed.). Upper Saddle River, NJ: Prentice Hall.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. https://curriculumredesign.org/wp-content/uploads/AI-in-Education-Promises-and-Implications_for-Teaching-and-Learning-Holmes-Bialik-Fadel.pdf
Lee, Y. F., Hwang, G. J., & Chen, P. Y. (2022). Impacts of an AI-based chatbot 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
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-16). https://doi.org/10.1145/3313831.3376727
Piedra-Castro, W. I., Cajamarca-Correa, M. A., Burbano-Buñay, E. S., & Moreira-Alcívar, E. F. (2024). Integración de la inteligencia artificial en la enseñanza de las Ciencias Sociales en la educación superior. Journal of Economic and Social Science Research, 4(3), 105-126. https://doi.org/10.55813/gaea/jessr/v4/n3/123
Pike, G. R. (1995). The relationship between selfreports of college experiences and achievement test scores. Research in Higher Education, 36, 1-21. https://doi.org/10.1007/BF02331961
Puentedura, R.R. (2009). As we may teach: educational technology, from theory into practice. Apple.
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development and well-being. American Psychologist, 55, 68-78. https://doi.org/10.1037/0003-066X.55.1.68
Salem, I. (2024). Integrating artificial intelligence in personalized learning: A future-oriented approach to enhance student engagement and achievement. International Journal of Post Axial: Futuristic Teaching and Learning, 111-119. https://journal.amorfati.id/index.php/postaxial/article/view/299
Sampieri, R. H. (2018). Metodología de la investigación: Las rutas cuantitativa, cualitativa y mixta. McGraw Hill. https://books.google.com/books?vid=ISBN9781456260965
Schneider, M., & Preckel, F. (2017). Variables associated with achievement in higher education: A systematic review of meta-analyses. Psychological Bulletin, 143(6), 565-600. https://doi.org/10.1037/bul0000098
Vieriu, A. M., & Petrea, G. (2025). The impact of artificial intelligence (AI) on students' academic development. Education Sciences, 15(3), 343. https://doi.org/10.3390/educsci15030343
Williamson, B. (2024). The social life of AI in education. International Journal of Artificial Intelligence in Education, 34(1), 97-104. https://doi.org/10.1007/s40593-023-00342-5
Wood, P. N., Chaves, V. E., & Esteche, A. R. (2024). El análisis de la percepción de los profesores respecto al uso de la Inteligencia Artificial. Revista EDUCA UMCH, 24, 66. https://doi.org/10.35756/educaumch.202424.293
Yue, M., Jong, M. S., & Dai, Y. (2022). Pedagogical design of K-12 artificial intelligence education: A systematic review. Sustainability, 14(23), 15620. Disponible en: https://doi.org/10.3390/su142315620
