DEVELOPING A SCALE FOR FACTORS INFLUENCING TEACHERS’ READINESS TO ADOPT GENERATIVE AI IN TEACHING
Main Article Content
Abstract
This study develops and validates a scale to explore factors influencing teachers’ readiness to adopt generative AI in education. The scale comprises five key factors: Colleague Support (CS), Normative Belief (NB), Subjective Norm (SN), Relevance of AI (RA), and AI Readiness (RE). A survey of 421 teachers revealed that the scale demonstrates high reliability, with CS, SN, and RA showing strong correlations with RE, while NB exhibited a moderate correlation. The study provides a valuable measurement tool to support educational policymakers in designing strategies and training programs to promote digital transformation.
Keywords
behavior, generative AI, readiness, scale, teachers
Article Details
References
Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K. D., & Oyelere, S. S. (2022). Teachers’ Readiness and Intention to Teach Artificial Intelligence in Schools. Computers and Education: Artificial Intelligence, 3, 100099. https://doi.org/10.1016/j.caeai.2022.100099
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Chin, W. W., & Todd, P. A. (1995). On the Use, Usefulness, and Ease of Use of Structural Equation Modeling in MIS Research: A Note of Caution. Management Information Systems Research Center, University of Minnesota, 19(2), 237-246. https://doi.org/10.2307/249690
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, 33(3), 725-755. https://doi.org/10.1007/s40593-021-00243-5
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
DeVellis, R., & Thorpe, C. T. (2021). Scale development: Theory and applications (5th ed.). SAGE Publications.
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
Fundi, M., Sanusi, I. T., Oyelere, S. S., & Ayere, M. (2024). Advancing AI Education: Assessing Kenyan In-Service Teachers' Preparedness for Integrating Artificial Intelligence in Competence-Based Curriculum. Computers in Human Behavior Reports, 14, Article 100412. https://doi.org/10.1016/j.chbr.2024.100412
Gerbing, D. W., & Anderson, J. C. (1988). An Updated Paradigm for Scale Development Incorporating Unidimensionality and Its Assessment. Journal of Marketing Research, 25(2), 186-192. https://doi.org/10.2307/3172650
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS Path Modelling in New Technology Research: Updated Guidelines. Industrial Management & Data Systems, 2-20. https://doi.org/10.1108/IMDS-09-2015-0382
Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural Equation Modeling: Guidelines for Determining Model Fit. Electronic Journal of Business Research Methods, 6(1), 53-60.
Hu, L., & Bentler, P. M. (1998). Fit Indices in Covariance Structure Modeling: Sensitivity to Underparameterized Model Misspecification. Psychological Methods, 3(4), 424-453. https://doi.org/10.1037/1082-989X.3.4.424
Jatileni, C. N., Sanusi, I. T., Olaleye, S. A., Ayanwale, M. A., Agbo, F. J., & Oyelere, P. B. (2024). Artificial Intelligence in Compulsory Level of Education: Perspectives from Namibian In-Service Teachers. Education and Information Technologies, 29(10), 12569-12596. https://doi.org/10.1007/s10639-023-12341-z
Jöhnk, J., Weißert, M., & Wyrtki, K. (2021). Ready or Not, AI Comes - An Interview Study of Organizational AI Readiness Factors. Business & Information Systems Engineering, 63(1), 5-20. https://doi.org/10.1007/s12599-020-00676-7
Kline, R. B. (2016). Principles and Practice of Structural Equation Modeling (4th ed.).
The Guilford Press.
Luckin, R., Cukurova, M., Kent, C., & Boulay, B. D. (2022). Empowering Educators to be AI-Ready. Computers and Education: Artificial Intelligence, 3, 100076. https://doi.org/10.1016/j.caeai.2022.100076
Miller, G. (1956). The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information. The Psychological Review, 63, 81-97.
Montano, D. E., & Kasprzyk, D. (2008). Theory of Reasoned Action, Theory of Planned Behavior, and the Integrated Behavioral Model. In B. K. Karen Glanz, Health Behavior: Theory, Research and Practice (pp. 67-92). Jossey-Bass.
Mulaik, S. A., James, L. R., Van Alstine, J., Bennett, N., Lind, S., & Stilwell, C. D. (1989). Evaluation of Goodness-of-fit Indices for Structural Equation Models. Psychological Bulletin, 105(3), 430-445. https://doi.org/10.1037/0033-2909.105.3.430
Nguyen, H. D., Nguyen, H. N., & Ta, T. T. (2024). Factors Affecting the Implementation of STEAM Education Among Primary School Teachers in Various Countries and Vietnamese Educators: Comparative Analysis. Education 3-13, 1-15. https://doi.org/10.1080/03004279.2024.2318239
Nguyen, H. N., & Ta, T. T. (2024). Developing Scale to Assess Teachers' Behaviour for Implementing The 2018 General Education Program: A Study Based on The Theory of Planned Behavior. HNUE Journal of Science, 69(2), 3-14. https://doi.org/10.18173/2354-1075.2024-0018
Nunnally, J. C., & Bernstein, I. H. (1994). The Assessment of Reliability. In Psychometric Theory (Vol. 3, pp. 248-292). McGraw-Hill.
Peres, R., Schreier, M., Schweidel, D., & Sorescu, A. (2023). On ChatGPT and Beyond: How Generative Artificial Intelligence May Affect Research, Teaching, and Practice. International Journal of Research in Marketing, 40(2), 269-275. https://doi.org/10.1016/j.ijresmar.2023.03.001
Seryapina, Y. S. (2018). The Concept of “Readiness for Pedagogical Activity”: Motivational Readiness, Psychological Readiness, Readiness to Innovative Activity. Вестник Южно-Уральского государственного университета. Серия: Образование. Педагогические науки, 10(4), 77-86.
Ta, T. T., & Nguyen, T. N. (2022). A Comparison of Using CB-SEM and PLS-SEM to Assess Training Effectiveness Evaluation Model for Teacher’s Online Continuing Professional Development. Ho Chi Minh City University of Education Journal of Science, 19(2), 213-228. https://doi.org/10.54607/hcmue.js.19.2.3306(2022)
UNESCO. (2022). K-12 AI curricula: A mapping of government-endorsed AI curricula. https://unesdoc.unesco.org/ark:/48223/pf0000380602
Wang, X. (2023). Preparing for AI-Enhanced Education: Conceptualizing and Empirically Examining Teachers’ AI Readiness. Computers in Human Behavior, 146, 107798. https://doi.org/10.1016/j.chb.2023.107798
Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Sage Publications.
Chin, W. W., & Todd, P. A. (1995). On the Use, Usefulness, and Ease of Use of Structural Equation Modeling in MIS Research: A Note of Caution. Management Information Systems Research Center, University of Minnesota, 19(2), 237-246. https://doi.org/10.2307/249690
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, 33(3), 725-755. https://doi.org/10.1007/s40593-021-00243-5
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
DeVellis, R., & Thorpe, C. T. (2021). Scale development: Theory and applications (5th ed.). SAGE Publications.
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
Fundi, M., Sanusi, I. T., Oyelere, S. S., & Ayere, M. (2024). Advancing AI Education: Assessing Kenyan In-Service Teachers' Preparedness for Integrating Artificial Intelligence in Competence-Based Curriculum. Computers in Human Behavior Reports, 14, Article 100412. https://doi.org/10.1016/j.chbr.2024.100412
Gerbing, D. W., & Anderson, J. C. (1988). An Updated Paradigm for Scale Development Incorporating Unidimensionality and Its Assessment. Journal of Marketing Research, 25(2), 186-192. https://doi.org/10.2307/3172650
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS Path Modelling in New Technology Research: Updated Guidelines. Industrial Management & Data Systems, 2-20. https://doi.org/10.1108/IMDS-09-2015-0382
Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural Equation Modeling: Guidelines for Determining Model Fit. Electronic Journal of Business Research Methods, 6(1), 53-60.
Hu, L., & Bentler, P. M. (1998). Fit Indices in Covariance Structure Modeling: Sensitivity to Underparameterized Model Misspecification. Psychological Methods, 3(4), 424-453. https://doi.org/10.1037/1082-989X.3.4.424
Jatileni, C. N., Sanusi, I. T., Olaleye, S. A., Ayanwale, M. A., Agbo, F. J., & Oyelere, P. B. (2024). Artificial Intelligence in Compulsory Level of Education: Perspectives from Namibian In-Service Teachers. Education and Information Technologies, 29(10), 12569-12596. https://doi.org/10.1007/s10639-023-12341-z
Jöhnk, J., Weißert, M., & Wyrtki, K. (2021). Ready or Not, AI Comes - An Interview Study of Organizational AI Readiness Factors. Business & Information Systems Engineering, 63(1), 5-20. https://doi.org/10.1007/s12599-020-00676-7
Kline, R. B. (2016). Principles and Practice of Structural Equation Modeling (4th ed.).
The Guilford Press.
Luckin, R., Cukurova, M., Kent, C., & Boulay, B. D. (2022). Empowering Educators to be AI-Ready. Computers and Education: Artificial Intelligence, 3, 100076. https://doi.org/10.1016/j.caeai.2022.100076
Miller, G. (1956). The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information. The Psychological Review, 63, 81-97.
Montano, D. E., & Kasprzyk, D. (2008). Theory of Reasoned Action, Theory of Planned Behavior, and the Integrated Behavioral Model. In B. K. Karen Glanz, Health Behavior: Theory, Research and Practice (pp. 67-92). Jossey-Bass.
Mulaik, S. A., James, L. R., Van Alstine, J., Bennett, N., Lind, S., & Stilwell, C. D. (1989). Evaluation of Goodness-of-fit Indices for Structural Equation Models. Psychological Bulletin, 105(3), 430-445. https://doi.org/10.1037/0033-2909.105.3.430
Nguyen, H. D., Nguyen, H. N., & Ta, T. T. (2024). Factors Affecting the Implementation of STEAM Education Among Primary School Teachers in Various Countries and Vietnamese Educators: Comparative Analysis. Education 3-13, 1-15. https://doi.org/10.1080/03004279.2024.2318239
Nguyen, H. N., & Ta, T. T. (2024). Developing Scale to Assess Teachers' Behaviour for Implementing The 2018 General Education Program: A Study Based on The Theory of Planned Behavior. HNUE Journal of Science, 69(2), 3-14. https://doi.org/10.18173/2354-1075.2024-0018
Nunnally, J. C., & Bernstein, I. H. (1994). The Assessment of Reliability. In Psychometric Theory (Vol. 3, pp. 248-292). McGraw-Hill.
Peres, R., Schreier, M., Schweidel, D., & Sorescu, A. (2023). On ChatGPT and Beyond: How Generative Artificial Intelligence May Affect Research, Teaching, and Practice. International Journal of Research in Marketing, 40(2), 269-275. https://doi.org/10.1016/j.ijresmar.2023.03.001
Seryapina, Y. S. (2018). The Concept of “Readiness for Pedagogical Activity”: Motivational Readiness, Psychological Readiness, Readiness to Innovative Activity. Вестник Южно-Уральского государственного университета. Серия: Образование. Педагогические науки, 10(4), 77-86.
Ta, T. T., & Nguyen, T. N. (2022). A Comparison of Using CB-SEM and PLS-SEM to Assess Training Effectiveness Evaluation Model for Teacher’s Online Continuing Professional Development. Ho Chi Minh City University of Education Journal of Science, 19(2), 213-228. https://doi.org/10.54607/hcmue.js.19.2.3306(2022)
UNESCO. (2022). K-12 AI curricula: A mapping of government-endorsed AI curricula. https://unesdoc.unesco.org/ark:/48223/pf0000380602
Wang, X. (2023). Preparing for AI-Enhanced Education: Conceptualizing and Empirically Examining Teachers’ AI Readiness. Computers in Human Behavior, 146, 107798. https://doi.org/10.1016/j.chb.2023.107798