Modelado de ecuaciones estructurales de la adopción de herramientas educativas de inteligencia artificial por parte de profesores de ciencia, tecnología y matemáticas de Nigeria
DOI:
https://doi.org/10.1344/der.2025.46.51-64Palabras clave:
Modelado de equaciones estructurales, profesores nigerianos, ciencia, tecnoligía, matemáticas, adopción, herramientas educatives de inteligencia artificialResumen
Los avances recientes en inteligencia artificial (IA) han despertado interés en el crecimiento y desarrollo de herramientas educativas de IA (EAIT). La adopción de EAIT por parte de los docentes en las aulas ha ayudado a dar forma a las decisiones de instrucción que toman en un intento de promover de manera inteligente y activa el aprendizaje significativo de las áreas de contenido de los estudiantes. Sin embargo, los profesores de ciencia, tecnología y matemáticas (CTM) en Nigeria rara vez adoptan e incorporan EAIT en el discurso pedagógico de sus aulas, y sus percepciones sobre los EAIT rara vez se evalúan. Con este fin, este estudio identificó factores humanos en la aceptación de EAIT por parte de profesores de STM en Nigeria. El estudio propuso un modelo extendido de aceptación de tecnología (TAM) que integra la confianza percibida de los profesores de STM y las creencias educativas en los EAIT a través de un modelo cuantitativo de un diseño de encuesta descriptivo. La muestra para el estudio estuvo compuesta por 345 profesores de STM en los seis distritos educativos del estado de Lagos, Nigeria. Se utilizó un instrumento válido y confiable etiquetado como cuestionario de adopción de herramientas educativas de inteligencia artificial (AEAITQ, α = 0,87) para recopilar datos de la encuesta que se analizaron mediante modelos de ecuaciones estructurales. Los resultados del estudio mostraron que los profesores de STM con creencias constructivistas tenían la tendencia a adoptar e incorporar EAIT en sus decisiones de instrucción que sus homólogos con creencias tradicionales. Las creencias educativas tradicionales (TIB) tuvieron una influencia negativa en la confianza percibida (PT), la facilidad de uso percibida (PEOU) y la utilidad percibida (PU). Además, PT, PEOU y PU fueron factores importantes que predijeron la adopción de EAIT por parte de los profesores de STM. Sin embargo, PEOU fue el factor más fuerte que predijo la adopción de EAIT por parte de los profesores de STM en el discurso pedagógico. Se debatieron importantes conclusiones sobre el crecimiento y la adopción de las EAIT por parte de los principales interesados en la enseñanza de las ciencias, la tecnología y las matemáticas.
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Akar, S. G. M. (2019). Does it matter being innovative: Teachers’ technology acceptance. Education and Information Technologies, 24(6), 3415–3432. https://doi.org/10.1007/s10639-019-09933-z.
Alexandrakis, D., Chorianopoulos, K., & Tselios, N. (2020). Older adults and web 2.0 storytelling technologies: Probing the technology acceptance model through an age-related perspective. International Journal of Human–Computer Interaction, 36(17), 1623–1635. https://doi.org/10.1080/10447318.2020.1768673
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling 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
Arpaci, I. (2016). Understanding and predicting students’ intention to use mobile cloud storage services. Computers in Human Behavior, 58, 150–157. https://doi.org/10.1016/j.chb.2015.12.067.
Asan, O., Bayrak, A. E., & Choudhury, A. (2020). Artificial intelligence and human trust in healthcare: Focus on clinicians. Journal of Medical Internet Research, 22(6), e15154. https://doi.org/10.2196/15154
Awofala, A. O. & Oladipo, A. J. (2023). A simulation study of preservice STM teachers’ technostress as related to supposed utility, attitudes towards portable technology and continuance intents to use portable technology. Digital Education Review, 44, 23-29.
Awofala, A. O. A. & Ojaleye, O. (2018). An exploration of pre-service teachers’ educational values of mathematics in relation to gender and attitudes toward mathematics in Nigeria. Journal of Pedagogical Research, 2(1), 1-15.
Awofala, A. O. A., Lawani, A. O. & Oraegbunam, C. O. (2019). An assessment of the psychometric properties of the conceptions of teaching and learning questionnaire by pre-service mathematics teachers in south-west, Nigeria. International Journal of Pedagogy and Teacher Education, 3(2), 89-102.
Awofala, A. O. A., Oladipo, A. J., Akinoso, S. O., Arigbabu, A. A., & Fatade, A. O. (2022). An assessment of google classroom reception and usage among pre-service science, technology and mathematics teachers in south-west Nigeria. Jurnal Pendidikan Progresif, 12(2), 796-805.
Awofala, A. O., & Sopekan, O. S. (2020). Early-years future teachers’ mathematical beliefs as determinants of performance in primary mathematics. JRAMathEdu (Journal of Research and Advances in Mathematics Education), 5(1), 54-68.
Awofala, A. O., Lawani, A. O., & Oraegbunam, C. O. (2020). A factor analytic structure of the conceptions of mathematics scale among pre-service mathematics teachers in south-west Nigeria. JRAMathEdu (Journal of Research and Advances in Mathematics Education), 5(1), 94-104.
Bitkina, O. V., Jeong, H., Lee, B. C., Park, J., Park, J., & Kim, H. K. (2020). Perceived trust in artificial intelligence technologies: A preliminary study. Human Factors and Ergonomics in Manufacturing & Service Industries, 30(4), 282–290. https://doi.org/10.1002/hfm.20839
Chan, K.-W., & Elliott, R. G. (2004). Relational analysis of personal epistemology and conceptions about teaching and learning. Teaching and Teacher Education, 20(8), 817–831. https://doi.org/10.1016/j.tate.2004.09.002
Chiu, T. K. F., & Churchill, D. (2016). Adoption of mobile devices in teaching: Changes in teacher beliefs, attitudes, and anxiety. Interactive Learning Environments, 24(2), 317–327. https://doi.org/10.1080/10494820.2015.1113709
Choi, J. K., & Ji, Y. G. (2015). Investigating the importance of trust on adopting an autonomous vehicle. International Journal of Human-Computer Interaction, 31(10), 692–702. https://doi.org/10.1080/10447318.2015.1070549
Choi, S., Jang, Y., & Kim, H. (2023). Influence of Pedagogical Beliefs and Perceived Trust on Teachers’ Acceptance of Educational Artificial Intelligence Tools. International Journal of Human–Computer Interaction, 39(4), 910-922. DOI: 10.1080/10447318.2022.2049145
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
Ertmer, P. A., Ottenbreit-Leftwich, A. T., Sadik, O., Sendurur, E., & Sendurur, P. (2012). Teacher beliefs and technology integration practices: A critical relationship. Computers & Education, 59(2), 423–435. https://doi.org/10.1016/j.compedu.2012.02.001
Estriegana, R., Medina-Merodio, J. A., & Barchino, R. (2019). Student acceptance of virtual laboratory and practical work: An extension of the technology acceptance model. Computers & Education, 135, 1–14. https://doi.org/10.1016/j.compedu.2019.02.010
Fornell, C., & 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
Fraillon, J., Ainley, J., Schulz, W., Friedman, T., & Gebhardt, E. (2014). Preparing for life in a digital age: The IEA International Computer and Information Literacy Study international report. Springer Nature.
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly: Management Information Systems, 27(1), 51–90. https://doi.org/10.2307/30036519
Gil-Flores, J., Rodríguez-Santero, J., & Torres-Gordillo, J. J. (2017). Factors that explain the use of ICT in secondary-education classrooms: The role of teacher characteristics and school infrastructure. Computers in Human Behavior, 68, 441–449. https://doi.org/10.1016/j.chb.2016.11.057
Girish, V. G., Kim, M. Y., Sharma, I., & Lee, C. K. (2021). Examining the structural relationships among e-learning interactivity, uncertainty avoidance, and perceived risks of COVID-19: Applying extended technology acceptance model. International Journal of Human–Computer Interaction, 1–11. https://doi.org/10.1080/10447318.2021.1970430
Guilherme, A. (2019). AI and education: The importance of teacher and student relations. AI & Society, 34(1), 47–54. https://doi.org/10.1007/s00146-017-0693-8
Gurer, M. D., & Akkaya, R. (2021). The influence of pedagogical beliefs on technology acceptance: A structural equation modeling study of pre-service mathematics teachers. Journal of Mathematics Teacher Education, 1–17. https://doi.org/10.1007/s10857-021-09504-5
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
Holstein, K., McLaren, B. M., & Aleven, V. (2018). Student Learning Benefits of a Mixed-Reality Teacher Awareness Tool in AI-Enhanced Classrooms. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10947 LNAI, 154–168. https://doi.org/10.1007/978-3-319-93843-1_12
Hwang, Y., & Kim, D. J. (2007). Customer self-service systems: The effects of perceived web quality with service contents on enjoyment, anxiety, and e-trust. Decision Support Systems, 43(3), 746–760. https://doi.org/10.1016/j.dss.2006.12.008
Jeon, J. (2022). Exploring AI chatbot affordances in the EFL classroom: Young learners’ experiences and perspectives. Computer Assisted Language Learning, 1–26. https://doi.org/10.1080/09588221.2021.2021241
Johal, W., Castellano, G., Tanaka, F., & Okita, S. (2018). Robots for learning. International Journal of Social Robotics, 10(3), 293–294. https://doi.org/10.1007/s12369-018-0481-8
Kim, C. M., Kim, M. K., Lee, C. J., Spector, J. M., & DeMeester, K. (2013). Teacher beliefs and technology integration. Teaching and Teacher Education, 29(1), 76–85. https://doi.org/10.1016/j.tate.2012.08.005
Kim, J., Merrill, K., Xu, K., & Sellnow, D. D. (2020). My teacher is a machine: Understanding students’ perceptions of AI teaching assistants in online education. International Journal of Human–Computer Interaction, 36(20), 1902–1911. https://doi.org/10.1080/10447318.2020.1801227
Kline, R. B. (2015). TXTBK Principles and practices of structural equation modeling Ed. 4 ***. In Methodology in the social sciences.
Lano-Maduagu, A. T. Awofala, A. O. A., & Arigbabu, A. A. (2022). Assessment of psychometric properties of teachers’ sense of efficacy beliefs by pre-service home economics teachers in southwest, Nigeria. Nigerian Online Journal of Educational Sciences and Technology, 4 (2), 89-99.
Liu, H., Lin, C. H., & Zhang, D. (2017). Pedagogical beliefs and attitudes toward information and communication technology: A survey of teachers of English as a foreign language in China. Computer Assisted Language Learning, 30(8), 745–765. https://doi.org/10.1080/09588221.2017.1347572
Mercader, C., & Gairín, J. (2020). University teachers’ perception of barriers to the use of digital technologies: The importance of the academic discipline. International Journal of Educational Technology in Higher Education, 17(1), 1–14. https://doi.org/10.1186/s41239-020-0182-x
Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: Investigating the factors that influence behavioral intention to use. Computers & Education, 109, 56–73. https://doi.org/10.1016/j.compedu.2017.02.005
Nja, C. O., Idiege, K. J., Uwe, U. E. et al. (2023). Adoption of artificial intelligence in science teaching: From the vantage point of the African science teachers. Smart Learning Environment, 10, 42. https://doi.org/10.1186/s40561-023-00261-x.
Nye, B. D. (2014). Barriers to ITS Adoption: A systematic mapping study. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8474 LNCS, 583–590. https://doi.org/10.1007/978-3-319-07221-0_74
Pal, D., & Patra, S. (2021). University students’ perception of video-based learning in times of COVID-19: A TAM/TTF perspective. International Journal of Human–Computer Interaction, 37(10), 903–921. https://doi.org/10.1080/10447318.2020.1848164
Pawar, U., O’Shea, D., Rea, S., & O’Reilly, R. (2020). Explainable AI in Healthcare. 2020 International Conference on Cyber Situational Awareness, Data Analytics and Assessment, Cyber SA 2020. https://doi.org/10.1109/CyberSA49311.2020.9139655
Piech, C., Bassen, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L., & Sohl-Dickstein, J. (2015). Deep knowledge tracing. Advances in Neural Information Processing Systems, 2015-Janua, 505–513.
Qin, F., Li, K., & Yan, J. (2020). Understanding user trust in artificial intelligence-based educational systems: Evidence from China. British Journal of Educational Technology, 51(5), 1693–1710. https://doi.org/10.1111/bjet.12994
Rafique, H., Almagrabi, A. O., Shamim, A., Anwar, F., & Bashir, A. K. (2020). Investigating the acceptance of mobile library applications with an extended Technology Acceptance Model (TAM). Computers & Education, 145, 103732. https://doi.org/10.1016/j.compedu.2019.103732
Rai, A. (2020). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science, 48(1), 137–141. https://doi.org/10.1007/s11747-019-00710-5
Riestra-González, M., Paule-Ruíz, M. d P., & Ortin, F. (2021). Massive LMS log data analysis for the early prediction of course-agnostic student performance. Computers & Education, 163, 104108. https://doi.org/10.1016/j.compedu.2020.104108
Sagnier, C., Loup-Escande, E., Lourdeaux, D., Thouvenin, I., & Valléry, G. (2020). User acceptance of virtual reality: An extended technology acceptance model. International Journal of Human–Computer Interaction, 36(11), 993–1007. https://doi.org/10.1080/10447318.2019.1708612
Sánchez-Prieto, J. C., Cruz-Benito, J., Therón, R., & García-Pẽalvo, F. J. (2019). How to measure teachers’ acceptance of AI-driven assessment in e-learning: A TAM-based Proposal. ACM International Conference Proceeding Series, 181–186. https://doi.org/10.1145/3362789.3362918
Scherer, R., Siddiq, F., & 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(0317), 13–35. https://doi.org/10.1016/j.compedu.2018.09.009
Shin, D. (2021). The effects of explain ability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies, 146, 102551. https://doi.org/10.1016/j.ijhcs.2020.102551
Sohn, K., & Kwon, O. (2020). Technology acceptance theories and factors influencing artificial intelligence-based intelligent products. Telematics and Informatics, 47, 101324. https://doi.org/10.1016/j.tele.2019.101324
Song, P., & Wang, X. (2020). A bibliometric analysis of worldwide educational artificial intelligence research development in recent twenty years. Asia Pacific Education Review, 21(3), 473–486. https://doi.org/10.1007/s12564-020-09640-2
Sopekan, O. S. & Awofala, A. O. A. (2019). Mathematics anxiety and mathematics beliefs as correlates of early childhood pre-service teachers’ numeracy skills. Pedacta, 9(2), 13-24.
Tondeur, J., van Braak, J., Ertmer, P. A., & Ottenbreit-Leftwich, A. (2017). Understanding the relationship between teachers’ pedagogical beliefs and technology use in education: A systematic review of qualitative evidence. Educational Technology Research and Development, 65(3), 555–575. https://doi.org/10.1007/s11423-016-9481-2
Troussas, C., Krouska, A., & Virvou, M. (2020). Using a multi-module model for learning analytics to predict learners’ cognitive states and provide tailored learning pathways and assessment. In Machine Learning Paradigms (pp. 9–22). Springer.
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
Wallace, L. G., & Sheetz, S. D. (2014). The adoption of software measures: A Technology Acceptance Model (TAM) perspective. Information & Management, 51(2), 249–259. https://doi.org/10.1016/j.im.2013.12.003
Wei, Y., Yang, Q., Chen, J., & Hu, J. (2018). The exploration of a machine learning approach for the assessment of learning styles changes. Mechatronic Systems and Control, 46(3), 121–126. https://doi.org/10.2316/journal.201.2018.3.201-2979
Yin, M., Vaughan, J. W., & Wallach, H. (2019). Understanding the effect of accuracy on trust in machine learning models [Paper presentation]. Conference on Human Factors in Computing Systems – Proceedings. https://doi.org/10.1145/3290605.3300509
Zhang, Y., Vera Liao, Q., & Bellamy, R. K. E. (2020). Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making [Paper presentation]. FAT* 2020 – Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 295–305. https://doi.org/10.1145/3351095.3372852.
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Derechos de autor 2025 Adeneye Olarewaju A. Awofala, Mike Boni Bazza, OMOLABAKE TEMILADE OJO, ADENIKE J. OLADIPO, OLADIRAN STEPHEN OLABIYI, ABAYOMI A. ARIGBABU, ALFRED O. FATADE, UCHENNA N. UDEANI
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