Alfabetización en inteligencia artificial en modalidades de estudio presencial y virtual entre estudiantes universitarios
DOI:
https://doi.org/10.56931/sel.2024.e4Palabras clave:
Inteligencia artificial, Aprendizaje, Estadísticas científicas, Agrupación educativaResumen
La incorporación de la inteligencia artificial en los procesos educativos ha transformado las dinámicas de enseñanza y aprendizaje en entornos presenciales y virtuales. No obstante, persisten interrogantes sobre las diferencias en el aprendizaje de esta tecnología según la modalidad educativa. El objetivo principal de esta investigación fue comparar el aprendizaje de inteligencia artificial en las modalidades de estudio presencial y virtual entre estudiantes universitarios ecuatorianos. La metodología utilizó un enfoque cuantitativo y explicativo para analizar variables observadas y latentes relacionadas con la IA. La muestra incluyó a 432 estudiantes de diversas instituciones de educación superior, distribuidos en un 56% en la modalidad virtual y un 44% en la modalidad presencial. Los análisis se realizaron con SPSS versión 25 y AMOS 24, empleando el Cuestionario de Alfabetización en IA, que abarca dimensiones afectivas, conductuales, cognitivas y éticas. El análisis multivariado de invarianza factorial, basado en análisis multigrupo, mostró una excelente fiabilidad del cuestionario (α=0.960 y Ω=0.959), con puntajes más altos entre los estudiantes de la modalidad virtual. Los índices de ajuste del modelo fueron altamente satisfactorios (X²=2.647, NFI=0.904, RFI=0.897, IFI=TLI=0.934, CFI=0.938, RMSEA=0.064). Se concluyó que las modalidades presencial y virtual son equivalentes en el uso de inteligencia artificial, evidenciado por la invarianza configural, métrica, escalar y estricta, sin diferencias significativas en las medias de las dimensiones analizadas. Estos hallazgos destacan la efectividad de ambas modalidades para el aprendizaje de IA en contextos educativos ecuatorianos.
Referencias
Abdullah, N. L., Ramdan, M. R., Ngah, N. S., Yin, K. Y., Shokory, S. M., Fuad, D. R. S. M., & Yonus, A. (2024). An Integrated Framework of Online Learning Effectiveness in Institutions of Higher Learning. European Journal of Educational Research, 13(3), 1321-1333. https://doi.org/10.12973/eu-jer.13.3.1321 DOI: https://doi.org/10.12973/eu-jer.13.3.1321
Brown, G. T. L., Harris, L. R., O’Quin, C., & Lane, K. E. (2017). Using multi-group confirmatory factor analysis to evaluate cross-cultural research: identifying and understanding non-invariance. International Journal of Research & Method in Education, 40(1), 66–90. https://doi.org/10.1080/1743727X.2015.1070823 DOI: https://doi.org/10.1080/1743727X.2015.1070823
Celik, I. (2023). Exploring the Determinants of Artificial Intelligence (AI) Literacy: Digital Divide, Computational Thinking, Cognitive Absorption. Telematics and Informatics, 83, 102026. https://doi.org/10.1016/j.tele.2023.102026 DOI: https://doi.org/10.1016/j.tele.2023.102026
Choi, H., Jung, I., & Lee, Y. (2023). The power of positive deviance behaviours: From panic-gogy to effective pedagogy in online teaching. Education and Information Technologies, 28(10), 12651–12669. https://doi.org/10.1007/s10639-023-11696-7 DOI: https://doi.org/10.1007/s10639-023-11696-7
Cilliers, J., Fleisch, B., Kotze, J., Mohohlwane, N., Taylor, S., & Thulare, T. (2022). Can virtual replace in-person coaching? Experimental evidence on teacher professional development and student learning. Journal of Development Economics, 155, 102815. https://doi.org/10.1016/j.jdeveco.2021.102815 DOI: https://doi.org/10.1016/j.jdeveco.2021.102815
Ellikkal, A., & Rajamohan, S. (2024). AI-enabled personalized learning: empowering management students for improving engagement and academic performance. Vilakshan - XIMB Journal of Management. https://doi.org/10.1108/XJM-02-2024-0023 DOI: https://doi.org/10.1108/XJM-02-2024-0023
Goetz, C., Coste, J., Lemetayer, F., Rat, A.-C., Montel, S., Recchia, S., Debouverie, M., Pouchot, J., Spitz, E., & Guillemin, F. (2013). Item reduction based on rigorous methodological guidelines is necessary to maintain validity when shortening composite measurement scales. Journal of Clinical Epidemiology, 66(7), 710–718. https://doi.org/10.1016/j.jclinepi.2012.12.015 DOI: https://doi.org/10.1016/j.jclinepi.2012.12.015
Kayal, A. (2024). Transformative Pedagogy: A Comprehensive Framework for AI Integration in Education. In T. Singh, S. Dutta, S. Vyas, & Á. Rocha (Eds.), Explainable AI for Education: Recent Trends and Challenges (Vol. 19, pp. 247–270). Springer, Cham. https://doi.org/10.1007/978-3-031-72410-7_14 DOI: https://doi.org/10.1007/978-3-031-72410-7_14
Kit Ng, D. T., Wu, W., Lok Leung, J. K., & Wah Chu, S. K. (2023). Artificial Intelligence (AI) Literacy Questionnaire with Confirmatory Factor Analysis. 2023 IEEE International Conference on Advanced Learning Technologies (ICALT), 233–235. https://doi.org/10.1109/ICALT58122.2023.00074
Lee, C. (2024). Online Learning versus Face to Face Learning toward Students: Which can be an effective way of Learning Methodology to our current Educational System? Mendeley Data, V1. https://doi.org/10.17632/M2PRRM7C3G.1 DOI: https://doi.org/10.31219/osf.io/twdy2
Lérias, E., Guerra, C., & Ferreira, P. (2024). Literacy in Artificial Intelligence as a Challenge for Teaching in Higher Education: A Case Study at Portalegre Polytechnic University. Information, 15(4), 205. https://doi.org/10.3390/info15040205 DOI: https://doi.org/10.3390/info15040205
Li, X., & Hu, W. (2024). Peer versus teacher corrections through electronic learning communities and face-to-face classroom interactions and EFL learners’ passion for learning, speaking fluency, and accuracy. Heliyon, 10(4), e25849. https://doi.org/10.1016/j.heliyon.2024.e25849 DOI: https://doi.org/10.1016/j.heliyon.2024.e25849
Looney, A., Cumming, J., van Der Kleij, F., & Harris, K. (2018). Reconceptualising the role of teachers as assessors: teacher assessment identity. Assessment in Education: Principles, Policy & Practice, 25(5), 442–467. https://doi.org/10.1080/0969594X.2016.1268090 DOI: https://doi.org/10.1080/0969594X.2016.1268090
Ma, S., & Chen, Z. (2024). The Development and Validation of the Artificial Intelligence Literacy Scale for Chinese College Students (AILS-CCS). IEEE Access, 12, 146419–146429. https://doi.org/10.1109/ACCESS.2024.3468378 DOI: https://doi.org/10.1109/ACCESS.2024.3468378
Marsh, H. W., Guo, J., Parker, P. D., Nagengast, B., Asparouhov, T., Muthén, B., & Dicke, T. (2018). What to do when scalar invariance fails: The extended alignment method for multi-group factor analysis comparison of latent means across many groups. Psychological Methods, 23(3), 524–545. https://doi.org/10.1037/met0000113 DOI: https://doi.org/10.1037/met0000113
McNeish, D. (2020). Should We Use F-Tests for Model Fit Instead of Chi-Square in Overidentified Structural Equation Models? Organizational Research Methods, 23(3), 487–510. https://doi.org/10.1177/1094428118809495 DOI: https://doi.org/10.1177/1094428118809495
Photopoulos, P., Tsonos, C., Stavrakas, I., & Triantis, D. (2022). Remote and In-Person Learning: Utility Versus Social Experience. SN Computer Science, 4(2), 116. https://doi.org/10.1007/s42979-022-01539-6 DOI: https://doi.org/10.1007/s42979-022-01539-6
Rof, A., Bikfalvi, A., & Marques, P. (2024). Exploring learner satisfaction and the effectiveness of microlearning in higher education. The Internet and Higher Education, 62, 100952. https://doi.org/10.1016/j.iheduc.2024.100952 DOI: https://doi.org/10.1016/j.iheduc.2024.100952
Suthakorn, W., Songkham, W., Tantranont, K., Srisuphan, W., Sakarinkhul, P., & Dhatsuwan, J. (2020). Scale Development and Validation to Measure Occupational Health Literacy Among Thai Informal Workers. Safety and Health at Work, 11(4), 526–532. https://doi.org/10.1016/j.shaw.2020.06.003 DOI: https://doi.org/10.1016/j.shaw.2020.06.003
VanLeeuwen, C. A., Veletsianos, G., Johnson, N., & Belikov, O. (2021). Never‐ending repetitiveness, sadness, loss, and “juggling with a blindfold on:” Lived experiences of Canadian college and university faculty members during the COVID‐19 pandemic. British Journal of Educational Technology, 52(4), 1306–1322. https://doi.org/10.1111/bjet.13065 DOI: https://doi.org/10.1111/bjet.13065
Wilson, M. (2023). Constructing Measures (2nd Edition). Routledge. https://doi.org/10.4324/9781003286929 DOI: https://doi.org/10.4324/9781003286929
Yang, Y., Sun, W., Sun, D., & Salas-Pilco, S. Z. (2024). Navigating the AI-Enhanced STEM education landscape: a decade of insights, trends, and opportunities. Research in Science & Technological Education, 1–25. https://doi.org/10.1080/02635143.2024.2370764 DOI: https://doi.org/10.1080/02635143.2024.2370764
Yue Yim, I. H. (2024). A critical review of teaching and learning artificial intelligence (AI) literacy: Developing an intelligence-based AI literacy framework for primary school education. Computers and Education: Artificial Intelligence, 7, 100319. https://doi.org/10.1016/j.caeai.2024.100319 DOI: https://doi.org/10.1016/j.caeai.2024.100319
Yuwono, E. I., Tjondronegoro, D., Riverola, C., & Loy, J. (2024). Co-creation in action: Bridging the knowledge gap in artificial intelligence among innovation champions. Computers and Education: Artificial Intelligence, 7, 100272. https://doi.org/10.1016/j.caeai.2024.100272 DOI: https://doi.org/10.1016/j.caeai.2024.100272
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