Perceptions of future teachers and pedagogues on responsible AI. A measurement instrument
DOI:
https://doi.org/10.5944/ried.28.2.43288Keywords:
artificial intelligence, teachers in training, pedagogues in training, psychometric instrumentAbstract
This study presents the validity and reliability in the creation of an instrument designed to evaluate the perceptions of teachers and pedagogues in training towards the integration of Artificial Intelligence in tasks related to their teaching profession, taking into account intrinsic factors such as the attitude towards its responsible use, the level of creativity in the creation of didactic material with these tools, the associated enjoyment in the use of these tools, and the level of anxiety when facing the learning of this emerging technology in their academic training and its relevance in their future labor market. A non-experimental ex post facto design was used through surveys with a non-probabilistic sampling by convenience, with a total of 548 teachers and pedagogues in training from faculties of Education Sciences in Spain. Reliability and validity measures were used for the elaboration of the instrument. Regarding reliability, Cronbach's Alpha, Spearman-Brown Coefficient, Guttman's Two Halves and composite reliability were used. Regarding validity, comprehension, construct, convergent and discriminant validity were used. The results showed a highly satisfactory reliability, and in terms of validity, a good model fit was observed. The final version of the instrument consists of 25 items classified in five latent factors.
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Alemany Díaz, M. D. M., Vallés Lluch, A., Villanueva López, J. F., & García-Serra García, J. (2021). E-learning in "innovation, creativity and entrepreneurship": Exploring the new opportunities and challenges of technologies. Journal of Small Business Strategy (Online), 31(1), 39-50. https://doi.org/10.21125/inted.2020.0686
Alenezi, M. A. K., Mohamed, A. M., & Shaaban, T. S. (2023). Revolutionizing EFL special education: how ChatGPT is transforming the way teachers approach language learning. Innoeduca. International Journal of Technology and Educational Innovation, 9(2), 5-23. https://doi.org/10.24310/innoeduca.2023.v9i2.16774
Aler Tubella, A., Mora-Cantallops, M., & Nieves, J. C. (2024). How to teach responsible AI in Higher Education: challenges and opportunities. Ethics and Information Technology, 26(1), 1-14. https://doi.org/10.1007/s10676-023-09733-7
Asencio, E. N., García, E. J., Redondo, S. R., & Ruano, B. T. (2017). Fundamentos de la investigación y la innovación educativa. UNIR editorial.
Bellas, F., Guerreiro-Santalla, S., Naya, M., & Duro, R. J. (2023). AI curriculum for European high schools: An embedded intelligence approach. International Journal of Artificial Intelligence in Education, 33(2), 399-426. https://doi.org/10.1007/s40593-022-00315-0
Bentler, P. M. (1989). EQS structural equations program manual. BMDP Statistical Software.
Brandão, A., Pedro, L., & Zagalo, N. (2024). Teacher professional development for a future with generative artificial intelligence–an integrative literature review. Digital Education Review, (45), 151-157. https://doi.org/10.1344/der.2024.45.151-157
Cattell, R. B. (1966). The screen test for the number of factors. Multivariate Behavioral Research, 1(2), 245-276. https://doi.org/10.1207/s15327906mbr0102_10
Chai, C. S., Yu, D., King, R. B., & Zhou, Y. (2024). Development and validation of the Artificial Intelligence Learning Intention Scale (AILIS) for University Students. SAGE Open, 14(2), 21582440241242188. https://doi.org/10.1177/21582440241242188
Cheng, L., Umapathy, K., Rehman, M., Ritzhaupt, A., Antonyan, K., Shidfar, P., Nichols, J., Lee, M., & Abramowitz, B. (2023). Designing, developing, and validating a measure of undergraduate students’ conceptions of artificial intelligence in education. Journal of Interactive Learning Research, 34(2), 275-311. https://doi.org/10.1037/t93665-000
Cheung, G. W., & Wang, C. (2017). Current approaches for assessing convergent and discriminant validity with SEM: issues and solutions. Academy of Management Proceedings, 2017(1), 12706. https://doi.org/10.5465/AMBPP.2017.12706abstract
Chounta, I. A., Bardone, E., Raudsep, A., & Pedaste, M. (2022). Exploring teachers’ perceptions of artificial intelligence as a tool to support their practice in Estonian K-12 education. International Journal of Artificial Intelligence in Education, 32(3), 725-755. https://doi.org/10.1007/s40593-021-00243-5
Csikszentmihalyi, M., Csikszentmihalyi, M., Abuhamdeh, S., & Nakamura, J. (2014). Flow. Flow and the foundations of positive psychology: The Collected Works of Mihaly Csikszentmihalyi, 227-238. https://doi.org/10.1007/978-94-017-9088-8_15
Espinoza-San Juan, J., Raby, M. D., & Sagredo-Lillo, E. (2024). Validación de un cuestionario sobre las percepciones y usos de la IA-Gen entre estudiantes de pedagogía. Revista Ibérica de Sistemas e Tecnologias de Informação, (E70), 574-585.
European Commission. (2019). Directorate-General for communications networks content and technology: Ethics guidelines for trustworthy AI. Publications Office. https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai
Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272-299. https://doi.org/10.1037/1082-989X.4.3.272
Farrelly, T., & Baker, N. (2023). Generative artificial intelligence: Implications and considerations for higher education practice. Education Sciences, 13(11), 1109. https://doi.org/10.3390/educsci13111109
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
Gocen, A., & Aydemir, F. (2020). Artificial intelligence in education and schools. Research on Education and Media, 12(1), 13-21. https://doi.org/10.2478/rem-2020-0003
González-Mayorga, H., Rodríguez-Esteban, A., & Vidal, J. (2024). El uso del modelo GPT de OpenAIpara el análisis de textos abiertos en investigación educativa [Using OpenAI’s GPT Model to Analyse Open Texts in Educational Research]. Pixel-Bit. Revista de Medios y Educación, 69, 227-253. https://doi.org/10.12795/pixelbit.102032
Grájeda, A., Burgos, J., Córdova, P., & Sanjinés, A. (2024). Assessing student-perceived impact of using artificial intelligence tools: Construction of a synthetic index of application in higher education. Cogent Education, 11(1), 2287917. https://doi.org/10.1080/2331186X.2023.2287917
Guillén-Gámez, F. D., Ruiz-Palmero, J., & García, M. G. (2023). Digital competence of teachers in the use of ICT for research work: development of an instrument from a PLS-SEM approach. Education and Information Technologies, 28(12), 16509-16529. https://doi.org/10.1007/s10639-023-11895-2
Guillén-Gámez, F. D., Tomczyk, Ł., Colomo-Magaña, E., & Mascia, M. L. (2024). Digital competence of Higher Education teachers in research work: validation of an explanatory and confirmatory model. Journal of E-Learning and Knowledge Society, 20(3), 1-12. https://doi.org/10.20368/1971-8829/1135963
Hair Jr, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (pp. 785-785). Prentice Hall.
Hasanein, A. M., & Sobaih, A. E. E. (2023). Drivers and consequences of ChatGPT use in higher education: Key stakeholder perspectives. European Journal of Investigation in Health, Psychology and Education, 13(11), 2599-2614. https://doi.org/10.3390/ejihpe13110181
Heinzl, A., Buxmann, P., Wendt, O., & Weitzel, T. (Eds.). (2011). Theory-guided modeling and empiricism in information systems research. Springer Science & Business Media. https://doi.org/10.1007/978-3-7908-2781-1
Hopcan, S., Türkmen, G., & Polat, E. (2024). Exploring the artificial intelligence anxiety and machine learning attitudes of teacher candidates. Education and Information Technologies, 29(6), 7281-7301. https://doi.org/10.1007/s10639-023-12086-9
Hornberger, M., Bewersdorff, A., & Nerdel, C. (2023). What do university students know about Artificial Intelligence? Development and validation of an AI literacy test. Computers and Education: Artificial Intelligence, 5, 1-12. https://doi.org/10.1016/j.caeai.2023.100165
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
Hwang, G. J., & Chen, N. S. (2023). Exploring the potential of generative artificial intelligence in education: applications, challenges, and future research directions. Journal of Educational Technology & Society, 26(2), 1-19. https://doi.org/10.30191/ETS.202304_26(2).0014
Jang, Y., Choi, S., & Kim, H. (2022). Development and validation of an instrument to measure undergraduate students’ attitudes toward the ethics of artificial intelligence (AT-EAI) and analysis of its difference by gender and experience of AI education. Education and Information Technologies, 27(8), 11635-11667. https://doi.org/10.1007/s10639-022-11086-5
Kaouni, M., Lakrami, F., & Labouidya, O. (2023). The design of an adaptive E-learning model based on Artificial Intelligence for enhancing online teaching. International Journal of Emerging Technologies in Learning (Online), 18(6), 202-219. https://doi.org/10.3991/ijet.v18i06.35839
Kim, S. W., & Lee, Y. (2022). The artificial intelligence literacy scale for middle school students. Journal of the Korea Society of Computer and Information, 27(3), 225-238. https://doi.org/10.9708/jksci.2022.27.03.225
Lambert, J., & Stevens, M. (2023). ChatGPT and generative AI technology: a mixed bag of concerns and new opportunities. Computers in the Schools, 1-25. https://doi.org/10.1080/07380569.2023.2256710
Marquina, M. C. G., Pinto-Villar, Y. M., Aranzamendi, J. A. M., & Gutiérrez, B. J. A. (2024). Adaptación y validación de un instrumento para medir las actitudes de los universitarios hacia la inteligencia artificial. Revista de Comunicación, 23(2), 125-142. https://doi.org/10.26441/RC23.2-2024-3493
Meroño, L., Calderón Luquin, A., Arias Estero, J. L., & Méndez Giménez, A. (2018). Diseño y validación del cuestionario de percepción del profesorado de Educación Primaria sobre el aprendizaje del alumnado basado en competencias (#ICOMpri2). Revista Complutense de Educación, 29(1), 215-235. https://doi.org/10.5209/RCED.52200
Mohamed, A. M., Shaaban, T. S., Bakry, S. H., Guillén-Gámez, F. D., & Strzelecki, A. (2024). Empowering the Faculty of Education Students: Applying AI’s Potential for Motivating and Enhancing Learning. Innovative Higher Education, 1-23. https://doi.org/10.1007/s10755-024-09747-z
Moneta, G. B., & Csikszentmihalyi, M. (1996). The effect of perceived challenges and skills on the quality of subjective experience. Journal of Personality, 64(2), 275-310. https://doi.org/10.1111/j.1467-6494.1996.tb00512.x
Morales-García, W. C., Sairitupa-Sanchez, L. Z., Morales-García, S. B., & Morales-García, M. (2024). Development and validation of a scale for dependence on artificial intelligence in university students. Frontiers in Education, 9, Article 1323898. https://doi.org/10.3389/feduc.2024.1323898
Nazaretsky, T., Cukurova, M., & Alexandron, G. (2022, March). An instrument for measuring teachers’ trust in AI-based educational technology. In LAK22: 12th International Learning Analytics and Knowledge Conference (pp. 56-66). https://doi.org/10.1145/3506860.3506866
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041
Ng, D. T. K., Wu, W., Leung, J. K. L., & Chu, S. K. W. (2023). Artificial Intelligence (AI) literacy questionnaire with confirmatory factor analysis. In 2023 IEEE International Conference on Advanced Learning Technologies (ICALT) (pp. 233-235). IEEE. https://doi.org/10.1109/ICALT58122.2023.00074
Nunnally, J. C. (1978). An overview of psychological measurement. Clinical diagnosis of mental disorders: A handbook (pp. 97-146). https://doi.org/10.1007/978-1-4684-2490-4_4
Ouyang, F., Zheng, L., & Jiao, P. (2022). Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020. Education and Information Technologies, 27(6), 7893-7925. https://doi.org/10.1007/s10639-022-10925-9
Pérez, C., & Carretero-Dios, H. (2005). Normas para el desarrollo y revisión de estudios instrumentales. International Journal of Clinical and Health Psychology, 5(3), 521-551.
Pérez, E. R., & Medrano, L. A. (2010). Análisis factorial exploratorio: bases conceptuales y metodológicas. Revista Argentina de Ciencias del Comportamiento (RACC), 2(1), 58-66.
Ruiz-Rojas, L. I., Acosta-Vargas, P., De-Moreta-Llovet, J., & Gonzalez-Rodriguez, M. (2023). Empowering education with generative artificial intelligence tools: Approach with an instructional design matrix. Sustainability, 15(15), 11524. https://doi.org/10.3390/su151511524
Şahín Kölemen, C. (2024). Artificial intelligence technologies and ethics in educational processes: solution suggestions and results. Innoeduca. International Journal of Technology and Educational Innovation, 10(2), 1-18. https://doi.org/10.24310/ijtei.102.2024.19806
Saz-Pérez, F., Mir, B. P., & Carrió, A. L. (2024). Validación y estructura factorial de un cuestionario TPACK en el contexto de Inteligencia Artificial Generativa (IAG). Hachetetepé: Revista Científica de Educación y Comunicación, (28), 1-14. https://doi.org/10.25267/Hachetetepe.2024.i28.1101
Shaffer, D. R., & Kipp, K. (2010). Developmental Psychology: Childhood and Adolescence. Wadsworth.
Soriano-Alcantara, J. M., Guillén-Gámez, F. D., & Ruiz-Palmero, J. (2024). Exploring Digital Competencies: Validation and Reliability of an Instrument for the Educational Community and for all Educational Stages. Technology, Knowledge and Learning, 1-20. https://doi.org/10.1007/s10758-024-09741-6
Thompson, B. (2004). Exploratory and confirmatory factor analysis: Understanding concepts and applications. American Psychological Association. https://doi.org/10.1037/10694-000
Tiwari, C. K., Bhat, M. A., Khan, S. T., Subramaniam, R., & Khan, M. A. I. (2024). What drives students toward ChatGPT? An investigation of the factors influencing adoption and usage of ChatGPT. Interactive Technology and Smart Education, 21(3), 333-355. https://doi.org/10.1108/ITSE-04-2023-0061
Üzüm, B., Elçiçek, M., & Pesen, A. (2024). Development of Teachers’ Perception Scale Regarding Artificial Intelligence Use in Education: Validity and Reliability Study. International Journal of Human–Computer Interaction, 1-12. https://doi.org/10.1080/10447318.2024.2385518
Uzumcu, O., & Acilmis, H. (2024). Do innovative teachers use AI-powered tools more interactively? a study in the context of diffusion of innovation theory. Technology, Knowledge and Learning, 29(2), 1109-1128. https://doi.org/10.1007/s10758-023-09687-1
Wang, F., Cheung, A. C., Chai, C. S., & Liu, J. (2024). Development and validation of the perceived interactivity of learner-AI interaction scale. Education and Information Technologies, 1-32. https://doi.org/10.1007/s10639-024-12963-x
Wang, Y. M., Wei, C. L., Lin, H. H., Wang, S. C., & Wang, Y. S. (2022). What drives students’ AI learning behavior: A perspective of AI anxiety. Interactive Learning Environments, 1-17. https://doi.org/10.1080/10494820.2022.2153147
Wang, Y. Y., & Wang, Y. S. (2022). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619-634. https://doi.org/10.1080/10494820.2019.1674887
Yilmaz, F. G. K., Yilmaz, R., & Ceylan, M. (2023). Generative artificial intelligence acceptance scale: A validity and reliability study. International Journal of Human–Computer Interaction, 1-13. https://doi.org/10.1080/10447318.2023.2288730
Yu, H., & Guo, Y. (2023, June). Generative artificial intelligence empowers educational reform: current status, issues, and prospects. Frontiers in Education, 8, Article 1183162. https://doi.org/10.3389/feduc.2023.1183162
Zhan, Y., Qiu, Z., Li, X., & Zhao, Z. (2024). Ease of Use or Fun Perception? Factors Affecting Retention of Newly Registered Mobile Game Players Based on Flow Theory and The Technology Acceptance Model. Journal of Internet Technology, 25(4), 497-505. https://doi.org/10.70003/160792642024072504001
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