Keywords
Teacher Education
Psychometric Validation
Technology Acceptance
Professional Development
How to Cite
Abstract
Purpose: This study developed and validated the AI Teacher Education Tool (AITET), a psychometric instrument measuring teacher educators' readiness to incorporate artificial intelligence across various professional domains.
Methodology/Approach: A quantitative cross-sectional design was used with 114 teacher educators from government, private, and government-aided institutions in Northern India. Instrument development was based on the Technology Acceptance Model (Davis, 1989) and Bandura's Self-Efficacy Theory (1997). Validation involved exploratory factor analysis (EFA), confirmatory factor analysis (CFA), reliability testing, and assessments of convergent and discriminant validity using Average Variance Extracted, the Fornell-Larcker criterion, and Heterotrait-Monotrait ratios.
Findings: EFA identified a five-factor structure explaining 70.998% of total variance: Professional Integration and Administrative Utility, Teaching and Learning Applications, Research and Ethical Considerations, Learning Design and Content Creation, and Ethical Awareness and Policy Dimensions. The instrument demonstrated excellent reliability (overall α = 0.967; subscale range: 0.864 to 0.919). Convergent validity was established across all five factors (AVE range: 0.555 to 0.701), and discriminant validity was confirmed through the HTMT criterion (all pairs below 0.85). CFA yielded an acceptable fit (χ²/df = 1.97; SRMR = 0.065), though incremental indices indicated room for improvement.
Research Limitations/Implications: The study is geographically confined to Northern India, which restricts its generalisability. Although the sample is sufficient for EFA, it is relatively modest for CFA. The AITET has implications for professional development planning, institutional policy development, and AI literacy programme design.
Originality/Value: The AITET expands technology acceptance frameworks to include domain-specific competencies, twin ethical constructs, and self-efficacy dimensions, filling a notable gap in psychometric measurement and offering a validated tool for assessing AI readiness in higher education.
References
Allen, L. K., & Kendeou, P. (2024). ED-AI Lit: An interdisciplinary framework for AI literacy in education. Policy Insights from the Behavioral and Brain Sciences, 11(1), 3–10. https://doi.org/10.1177/23727322231220339
American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (2014). Standards for educational and psychological testing. American Educational Research Association.
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
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. https://doi.org/10.1007/BF02723327
Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman.
Boateng, G. O., Neilands, T. B., Frongillo, E. A., Melgar-Quiñonez, H. R., & Young, S. L. (2018). Best practices for developing and validating scales for health, social, and behavioral research: A primer. Frontiers in Public Health, 6, Article 149. https://doi.org/10.3389/fpubh.2018.00149
Brislin, R. W. (1970). Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1(3), 185–216. https://doi.org/10.1177/135910457000100301
Celik, I. (2023). Towards Intelligent-TPACK: An empirical study on teachers' professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, Article 107468. https://doi.org/10.1016/j.chb.2022.107468
Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. TechTrends, 66(4), 616–630. https://doi.org/10.1007/s11528-022-00715-y
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), Article 21582440241242188. https://doi.org/10.1177/21582440241242188
Chiu, T. K. F. (2024). Development and validation of teacher artificial intelligence (AI) competence self-efficacy (TAICS) scale. Education and Information Technologies, 29(15), 6667–6685. https://doi.org/10.1007/s10639-024-13094-z
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(5), 910–922. https://doi.org/10.1080/10447318.2022.2049145
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Routledge.
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. https://doi.org/10.1007/BF02310555
Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302. https://doi.org/10.1037/h0040957
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
DeVellis, R. F. (2017). Scale development: Theory and applications (4th ed.). Sage Publications.
Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). Sage Publications.
Foltynek, T., Bjelobaba, S., Glendinning, I., Khan, Z. R., Santos, R., Pavletic, P., & Kravjar, J. (2023). ENAI recommendations on the ethical use of artificial intelligence in education. International Journal for Educational Integrity, 19(1), Article 12. https://doi.org/10.1007/s40979-023-00133-4
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
Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. https://doi.org/10.1111/bjet.12864
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modelling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. (2022). Ethics of AI in education:
Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 32(3), 504–526. https://doi.org/10.1007/s40593-021-00239-1
Huang, J. L., Curran, P. G., Keeney, J., Poposki, E. M., & DeShon, R. P. (2012). Detecting and deterring insufficient effort responding to surveys. Journal of Business and Psychology, 27(1), 99–114. https://doi.org/10.1007/s10869-011-9231-8
Kaufman, J. H., Woo, A., Eagan, J., Lee, S., & Kassan, E. B. (2025). Uneven adoption of artificial intelligence tools among U.S. teachers and principals in the 2023–2024 school year (Research Report No. RRA134-25). RAND Corporation. https://www.rand.org/pubs/research_reports/RRA134-25.html
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Publications.
Miao, F., & Cukurova, M. (2024). AI competency framework for teachers. UNESCO. https://doi.org/10.54675/ZJTE2084
Milfont, T. L., & Fischer, R. (2010). Testing measurement invariance across groups: Applications in cross-cultural research. International Journal of Psychological Research, 3(1), 111–130. https://doi.org/10.21500/20112084.857
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, Article 100041. https://doi.org/10.1016/j.caeai.2021.100041
Ng, D. T. K., Leung, J. K. L., Su, J., Ng, R. C. W., & Chu, S. K. W. (2023). Teachers' AI digital competencies and twenty-first century skills in the post-pandemic world. Educational Technology Research and Development, 71(1), 137–161. https://doi.org/10.1007/s11423-023-10203-6
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Pallant, J. (2020). SPSS survival manual: A step by step guide to data analysis using IBM SPSS (7th ed.). Routledge.
Pfitzner-Eden, F. (2016). Why do I feel more confident? Bandura's sources predict preservice teachers' latent changes in teacher self-efficacy. Frontiers in Psychology, 7, Article 1486. https://doi.org/10.3389/fpsyg.2016.01486
Ramazanoglu, M., & Akın, T. (2024). AI readiness scale for teachers: Development and validation. Education and Information Technologies. https://doi.org/10.1007/s10639-024-13087-y
Raykov, T. (1997). Estimation of composite reliability for congeneric measures. Applied Psychological Measurement, 21(2), 173–184. https://doi.org/10.1177/01466216970212006
Salas-Pilco, S. Z., Xiao, K., & Hu, X. (2022). Artificial intelligence and learning analytics in teacher education: A systematic review. Education Sciences, 12(8), Article 569. https://doi.org/10.3390/educsci12080569
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, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009
Streiner, D. L. (2003). Starting at the beginning: An introduction to coefficient alpha and internal consistency. Journal of Personality Assessment, 80(1), 99–103. https://doi.org/10.1207/S15327752JPA8001_18
Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson.
Tschannen-Moran, M., & Hoy, A. W. (2001). Teacher efficacy: Capturing an elusive construct. Teaching and Teacher Education, 17(7), 783–805. https://doi.org/10.1016/S0742-051X(01)00036-1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Walter, Y. (2024). Embracing the future of artificial intelligence in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21, Article 15. https://doi.org/10.1186/s41239-024-00448-3
Worthington, R. L., & Whittaker, T. A. (2006). Scale development research: A content analysis and recommendations for best practices. The Counseling Psychologist, 34(6), 806–838. https://doi.org/10.1177/0011000006288127
Yang, Y., Zhang, Y., Sun, D., & Zhang, L. (2025). Navigating the landscape of AI literacy education: Insights from a decade of research (2014–2024). Humanities and Social Sciences Communications, 12, Article 374. https://doi.org/10.1057/s41599-025-04583-8
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2025 Adit Gupta, Bharti Tandon, Nishta Rana