Ir al contenido

Documat


Structural equation modeling of Nigerian science, technology and mathematics teachers’ adoption of educational artificial in-telligence tools

  • Awofala, Adeneye Olarewaju A. [1] ; Bazza, Mike Boni [2] ; Ojo, Omolabake Temilade [1] ; Oladipo, Adenike J. [1] ; Olabiyi, Oladiran S. [1] ; Arigbabu, Abayomi A. [3]
    1. [1] University of Lagos

      University of Lagos

      Nigeria

    2. [2] Veritas University

      Veritas University

      Nigeria

    3. [3] University of Education

      University of Education

      Pakistán

  • Localización: Digital Education Review, ISSN-e 2013-9144, Nº. 46, 2025 (Ejemplar dedicado a: Number 46, January 2025), págs. 51-64
  • Idioma: inglés
  • DOI: 10.1344/der.2025.46.51-64
  • Títulos paralelos:
    • Modelado de ecuaciones estructurales de la adopción de herramientas educativas de inteligencia artificial por parte de profesores de ciencia, tecnología y matemáticas de Nigeria
    • Modelatge d'equacions estructurals de l'adopció d'eines educatives d'intel·ligència artificial per part de professors de ciència, tecnologia i matemàtiques de Nigèria
  • Enlaces
  • Resumen
    • español

      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.

    • català

      Els avenços recents en intel·ligència artificial (IA) han despertat interès en el creixement i el desenvolupament d'eines educatives d'IA (EAIT). L'adopció d'EAIT per part dels docents a les aules ha ajudat a donar forma a les decisions d'instrucció que prenen en un intent de promoure de manera intel·ligent i activa l'aprenentatge significatiu de les àrees de contingut dels estudiants. No obstant això, els professors de ciència, tecnologia i matemàtiques (CTM) a Nigèria poques vegades adopten i incorporen EAIT en el discurs pedagògic de les seves aules, i les seves percepcions sobre els EAIT poques vegades s'avaluen. A aquest efecte, aquest estudi va identificar factors humans en l'acceptació d'EAIT per part de professors de STM a Nigèria. L'estudi va proposar un model estès d'acceptació de tecnologia (TAM) que integra la confiança percebuda dels professors de STM i les creences educatives als EAIT mitjançant un model quantitatiu d'un disseny d'enquesta descriptiu. La mostra per a l'estudi va estar composta per 345 professors de STM als sis districtes educatius de l'estat de Lagos, Nigèria. L'estudi va proposar un model estès d'acceptació de tecnologia (TAM) que integra la confiança percebuda dels professors de STM i les creences educatives als EAIT mitjançant un model quantitatiu d'un disseny d'enquesta descriptiu. La mostra per a l'estudi va estar composta per 345 professors de STM als sis districtes educatius de l'estat de Lagos, Nigèria. Es va fer servir un instrument vàlid i fiable etiquetatge com a qüestionari d'adopció d'eines educatives d'intel·ligència artificial (AEAITQ, α = 0,87) per recopilar dades de l'enquesta que es van analitzar mitjançant models d'equacions estructurals. Els resultats de l‟estudi van mostrar que els professors de STM amb creences constructivistes tenien la tendència a adoptar i incorporar EAIT en les seves decisions d‟instrucció que els seus homòlegs amb creences tradicionals. Les creences educatives tradicionals (TIB) van tenir una influència negativa en la confiança percebuda (PT), la facilitat d'ús percebuda (PEOU) i la utilitat percebuda (PU). A més, PT, PEOU i PU van ser factors importants que van predir l'adopció d'EAIT per part dels professors de STM. Tot i això, PEOU va ser el factor més fort que va predir l'adopció d'EAIT per part dels professors de STM en el discurs pedagògic. Es van debatre conclusions importants sobre el creixement i l'adopció de les EAIT per part dels principals interessats en l'ensenyament de les ciències, la tecnologia i les matemàtiques.

    • English

      Recent progress in artificial intelligence (AI) has aroused interest in the growth and development of educational AI tools (EAITs). Teachers’ adoption of EAITs in classrooms has helped in shaping instructional decisions taken by them in an attempt to promote intelligently and actively students’ meaningful learning of contents areas. Nevertheless, science, technology and mathematics (STM) teachers in Nigeria are rarely adopting and incorporating EAITs in their classrooms pedagogical discourse, and their perceptions of EAITs are rarely assessed. To this end, this study identified human factors in acceptance of EAITs by STM teachers in Nigeria. The study proposed an extended technology acceptance model (TAM) integrating STM teachers’ perceived trust and instructional beliefs in EAITs through a quantitative blueprint of a descriptive survey design. The sample for the study consisted of 345 STM teachers in the six education districts of Lagos State, Nigeria. A valid and reliable instrument tagged adoption of educational artificial intelligence tools questionnaire (AEAITQ, α=0.87) was used to collect survey data which, were analysed via structural equation modeling. The study results showed that STM teachers with constructivist beliefs had the tendency to adopt and incorporate EAITs into their instructional decisions than their counterparts with traditional beliefs. Traditional instructional beliefs (TIB) had a negative influence on perceived trust (PT), perceived ease of use (PEOU), and perceived usefulness (PU). In addition, PT, PEOU and PU were strong factors predicting STM teachers’ adoption of EAITs. However, PEOU was the strongest factor that predicted STM teachers’ adoption of EAITs in pedagogical discourse. Important inferences regarding the growth and adoption of EAITs for significant stakeholders in STM education were discussed.

  • Referencias bibliográficas
    • Akar, S. G. M. (2019). Does it matter being innovative: Teachers’ technology acceptance. Education and Information Technologies, 24(6), 3415–3432....
    • Alexandrakis, D., Chorianopoulos, K., & Tselios, N. (2020). Older adults and web 2.0 storytelling technologies: Probing the technology...
    • Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological...
    • Arpaci, I. (2016). Understanding and predicting students’ intention to use mobile cloud storage services. Computers in Human Behavior, 58,...
    • Asan, O., Bayrak, A. E., & Choudhury, A. (2020). Artificial intelligence and human trust in healthcare: Focus on clinicians. Journal of...
    • Awofala, A. O. & Oladipo, A. J. (2023). A simulation study of preservice STM teachers’ technostress as related to supposed utility, attitudes...
    • Awofala, A. O. A. & Ojaleye, O. (2018). An exploration of pre-service teachers’ educational values of mathematics in relation to gender...
    • Awofala, A. O. A., Lawani, A. O. & Oraegbunam, C. O. (2019). An assessment of the psychometric properties of the conceptions of teaching...
    • Awofala, A. O. A., Oladipo, A. J., Akinoso, S. O., Arigbabu, A. A., & Fatade, A. O. (2022). An assessment of google classroom reception...
    • Awofala, A. O., & Sopekan, O. S. (2020). Early-years future teachers’ mathematical beliefs as determinants of performance in primary mathematics....
    • Awofala, A. O., Lawani, A. O., & Oraegbunam, C. O. (2020). A factor analytic structure of the conceptions of mathematics scale among pre-service...
    • Bitkina, O. V., Jeong, H., Lee, B. C., Park, J., Park, J., & Kim, H. K. (2020). Perceived trust in artificial intelligence technologies:...
    • Chan, K.-W., & Elliott, R. G. (2004). Relational analysis of personal epistemology and conceptions about teaching and learning. Teaching...
    • Chiu, T. K. F., & Churchill, D. (2016). Adoption of mobile devices in teaching: Changes in teacher beliefs, attitudes, and anxiety. Interactive...
    • Choi, J. K., & Ji, Y. G. (2015). Investigating the importance of trust on adopting an autonomous vehicle. International Journal of Human-Computer...
    • Choi, S., Jang, Y., & Kim, H. (2023). Influence of Pedagogical Beliefs and Perceived Trust on Teachers’ Acceptance of Educational Artificial...
    • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340....
    • Ertmer, P. A., Ottenbreit-Leftwich, A. T., Sadik, O., Sendurur, E., & Sendurur, P. (2012). Teacher beliefs and technology integration...
    • Estriegana, R., Medina-Merodio, J. A., & Barchino, R. (2019). Student acceptance of virtual laboratory and practical work: An extension...
    • Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics....
    • Fraillon, J., Ainley, J., Schulz, W., Friedman, T., & Gebhardt, E. (2014). Preparing for life in a digital age: The IEA International...
    • Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly: Management Information...
    • Gil-Flores, J., Rodríguez-Santero, J., & Torres-Gordillo, J. J. (2017). Factors that explain the use of ICT in secondary-education classrooms:...
    • Girish, V. G., Kim, M. Y., Sharma, I., & Lee, C. K. (2021). Examining the structural relationships among e-learning interactivity, uncertainty...
    • 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...
    • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM)....
    • Holstein, K., McLaren, B. M., & Aleven, V. (2018). Student Learning Benefits of a Mixed-Reality Teacher Awareness Tool in AI-Enhanced...
    • Hwang, Y., & Kim, D. J. (2007). Customer self-service systems: The effects of perceived web quality with service contents on enjoyment,...
    • Jeon, J. (2022). Exploring AI chatbot affordances in the EFL classroom: Young learners’ experiences and perspectives. Computer Assisted Language...
    • Johal, W., Castellano, G., Tanaka, F., & Okita, S. (2018). Robots for learning. International Journal of Social Robotics, 10(3), 293–294....
    • Kim, C. M., Kim, M. K., Lee, C. J., Spector, J. M., & DeMeester, K. (2013). Teacher beliefs and technology integration. Teaching and Teacher...
    • Kim, J., Merrill, K., Xu, K., & Sellnow, D. D. (2020). My teacher is a machine: Understanding students’ perceptions of AI teaching assistants...
    • 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...
    • Liu, H., Lin, C. H., & Zhang, D. (2017). Pedagogical beliefs and attitudes toward information and communication technology: A survey of...
    • Mercader, C., & Gairín, J. (2020). University teachers’ perception of barriers to the use of digital technologies: The importance of the...
    • Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: Investigating the factors that influence behavioral intention to use....
    • 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...
    • Nye, B. D. (2014). Barriers to ITS Adoption: A systematic mapping study. Lecture Notes in Computer Science (Including Subseries Lecture Notes...
    • Pal, D., & Patra, S. (2021). University students’ perception of video-based learning in times of COVID-19: A TAM/TTF perspective. International...
    • Pawar, U., O’Shea, D., Rea, S., & O’Reilly, R. (2020). Explainable AI in Healthcare. 2020 International Conference on Cyber Situational...
    • Piech, C., Bassen, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L., & Sohl-Dickstein, J. (2015). Deep knowledge tracing. Advances in...
    • Qin, F., Li, K., & Yan, J. (2020). Understanding user trust in artificial intelligence-based educational systems: Evidence from China....
    • Rafique, H., Almagrabi, A. O., Shamim, A., Anwar, F., & Bashir, A. K. (2020). Investigating the acceptance of mobile library applications...
    • 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...
    • Sagnier, C., Loup-Escande, E., Lourdeaux, D., Thouvenin, I., & Valléry, G. (2020). User acceptance of virtual reality: An extended technology...
    • Sánchez-Prieto, J. C., Cruz-Benito, J., Therón, R., & García-Pẽalvo, F. J. (2019). How to measure teachers’ acceptance of AI-driven assessment...
    • Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach...
    • Shin, D. (2021). The effects of explain ability and causability on perception, trust, and acceptance: Implications for explainable AI. International...
    • Sohn, K., & Kwon, O. (2020). Technology acceptance theories and factors influencing artificial intelligence-based intelligent products....
    • Song, P., & Wang, X. (2020). A bibliometric analysis of worldwide educational artificial intelligence research development in recent twenty...
    • Sopekan, O. S. & Awofala, A. O. A. (2019). Mathematics anxiety and mathematics beliefs as correlates of early childhood pre-service teachers’...
    • Tondeur, J., van Braak, J., Ertmer, P. A., & Ottenbreit-Leftwich, A. (2017). Understanding the relationship between teachers’ pedagogical...
    • Troussas, C., Krouska, A., & Virvou, M. (2020). Using a multi-module model for learning analytics to predict learners’ cognitive states...
    • Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315....
    • Wallace, L. G., & Sheetz, S. D. (2014). The adoption of software measures: A Technology Acceptance Model (TAM) perspective. Information...
    • Wei, Y., Yang, Q., Chen, J., & Hu, J. (2018). The exploration of a machine learning approach for the assessment of learning styles changes....
    • Yin, M., Vaughan, J. W., & Wallach, H. (2019). Understanding the effect of accuracy on trust in machine learning models [Paper presentation]....
    • Zhang, Y., Vera Liao, Q., & Bellamy, R. K. E. (2020). Effect of confidence and explanation on accuracy and trust calibration in AI-assisted...

Fundación Dialnet

Mi Documat

Opciones de artículo

Opciones de compartir

Opciones de entorno