بومی‌سازی و اعتبارسنجی پرسشنامه خودکارآمدی شایستگی هوش مصنوعی معلمان و مؤلفه‌های آن در ایران

شیما  جامی; شهربانو دهرویه; زهرا  ادیبی; علی  خانزاد

Authors

Shima Jami Department of Educational Sciences, Sar. c, Islamic Azad University, Sari, Iran Author https://orcid.org/0009-0004-6206-5579
Shahrbanoo Dahrouieh Department of Psychology, Adib Mazandaran Institute of Higher Education, Sari, Iran https://orcid.org/0009-0009-4769-8059
Zahra Adibi Department of Educational Sciences, Sar. c, Islamic Azad University, Sari, Iran Author https://orcid.org/0000-0002-7289-5937
Ali Khanzad Department of Educational Sciences, Sar. c, Islamic Azad University, Sari, Iran Author https://orcid.org/0009-0008-2438-3719

Keywords:

Artificial Intelligence, Self-Efficacy, Teacher Competency, Validation, Localization, Validity, Reliability

Abstract

This study aimed to localize and validate the Teachers’ Artificial Intelligence Competency Self-Efficacy Questionnaire and examine the psychometric properties of its components among Iranian teachers. This applied descriptive-psychometric study was conducted among Iranian teachers. A total of 122 teachers were selected through convenience sampling. The data collection instrument was the Persian version of the Teachers’ Artificial Intelligence Competency Self-Efficacy Questionnaire, which initially included 24 items and six components: AI knowledge, AI pedagogy, AI assessment, AI ethics, human-centered education, and professional commitment. Data were analyzed using descriptive indices, factor analysis, structural equation modeling, reliability and validity indices, one-way analysis of variance, and the Friedman test. The Kaiser-Meyer-Olkin index was 0.935, and Bartlett’s test of sphericity was significant, confirming the adequacy of the data for factor analysis. Items 1 and 13 were removed due to low communalities and weak factor loadings. The model fit indices confirmed the adequacy of the measurement model, with RMS Theta = 0.159, SRMR = 0.088, and R² = 0.996. The overall Cronbach’s alpha was 0.926, composite reliability was 0.942, and convergent validity was 0.732. The results of one-way ANOVA showed that only gender produced a significant difference in teachers’ AI competency self-efficacy scores. The Friedman test also indicated a significant difference among the ranks of the six components, with AI ethics receiving the highest rank. The Persian version of the Teachers’ Artificial Intelligence Competency Self-Efficacy Questionnaire, with 22 items and six components, demonstrated acceptable validity and reliability and can be used to assess Iranian teachers’ readiness for educational, ethical, and professional use of artificial intelligence.

Downloads

Download data is not yet available.

References

Aksakallı, C. (2025). Pre-Service EFL Teachers’ Perceived AI Literacy and Competency: The Integration of ChatGPT Into English Language Teacher Education. Sage Open, 15(3). https://doi.org/10.1177/21582440251379712

Alam, S., Hameed, A., Madej, M., & Kobylarek, A. (2024). Perception and Practice of Using Artificial Intelligence in Education: An Opinion Based Study. Xlinguae, 17(1), 216-233. https://doi.org/10.18355/xl.2024.17.01.15

Alshorman, S. (2024). The Readiness to Use Ai in Teaching Science: Science Teachers' Perspective. Journal of Baltic Science Education, 23(3), 432-448. https://doi.org/10.33225/jbse/24.23.432

Dumalasa, J. J. A. (2026). Teachers’ Awareness and Attitudes Toward Artificial Intelligence’s Role in Enhancing Elementary Numeracy Education. https://doi.org/10.21203/rs.3.rs-9795748/v1

Erol, M., Canbeldek, M., Erol, A., & Çolak, F. G. (2025). Exploring the Relationship Between Teachers' AI Attitudes, AI Self‐Efficacy, and AI Technological Pedagogical Content Knowledge. European Journal of Education, 60(4). https://doi.org/10.1111/ejed.70332

Gerdes, M. A., Bayne, A. N., Henry, K., Ludwig, B., Vance, A., Wessol, J. L., & Winston, S. (2024). Emerging Artificial Intelligence-Based Pedagogies in Didactic Nursing Education. Nurse Educator, 50(1), E7-E12. https://doi.org/10.1097/nne.0000000000001746

Gheihman, G., Li, H., Csaba, G., Bacchi, S., Huth, K., & Wolbrink, T. A. (2026). The Educator‐in‐the‐Loop: Intentional Integration of Generative Artificial Intelligence in Health Professions Education. The Clinical Teacher, 23(1). https://doi.org/10.1111/tct.70324

Hania, A., Waqas, M., & Chun-yan, X. U. (2025). Enhancing Teaching Competency in Higher Education: The Role of AI Efficacy, Social Media Use and Classroom Dynamics. European Journal of Education, 60(3). https://doi.org/10.1111/ejed.70197

Jia, X.-H., & Tu, J.-C. (2024). Towards a New Conceptual Model of AI-Enhanced Learning for College Students: The Roles of Artificial Intelligence Capabilities, General Self-Efficacy, Learning Motivation, and Critical Thinking Awareness. Systems, 12(3), 74. https://doi.org/10.3390/systems12030074

Kitcharoen, P., Howimanporn, S., & Chookaew, S. (2024). Enhancing Teachers’ AI Competencies Through Artificial Intelligence of Things Professional Development Training. International Journal of Interactive Mobile Technologies (Ijim), 18(02), 4-15. https://doi.org/10.3991/ijim.v18i02.46613

Koka, N. A., Khan, M. R., Ahmad, J., & Wahab, M. O. A. (2024). Gender Dynamics in Digital Classroom; Measuring Artificial Intelligence (AI) Acceptance and Integration by Senior Lectures in Foreign Language Instruction. Archive Des Sciences, 74(5), 35-44. https://doi.org/10.62227/as/74506

Kosbar, Y. (2026). TransformED Futures: Towards Human-Centred, Ethical and Inclusive Use and Governance of AI in Higher Education. World Futures Review. https://doi.org/10.1177/19467567251406292

Li, C. (2025). The Integration and Innovative Practice of Intelligent AI and Local Opera in College Teaching. Frontiers in psychology, 15. https://doi.org/10.3389/fpsyg.2024.1521777

Li, M. (2024). Assessing Chinese Primary Mathematics Teachers’ Self-Efficacy for Technology Integration: Development and Validation of a Multifaceted Scale. Asian Journal for Mathematics Education, 3(2), 231-253. https://doi.org/10.1177/27527263241254496

Li, M., Wang, X., Du, Y., Zhang, H., & Liao, B. (2025). Based on Digital Intelligence: Teaching Innovation and Practice of Veterinary Internal Medicine in China’s Southwest Frontier. Frontiers in veterinary science, 12. https://doi.org/10.3389/fvets.2025.1651179

Liu, X., Du, J., Wang, J., & Song, H. (2026). From Foundation to Intelligence Integration: The Synergistic Associations of ICT and AI Support With Pre-Service Teachers’ TPACK Development. Behavioral Sciences, 16(6), 922. https://doi.org/10.3390/bs16060922

Mazid, I., Wallace, A. A., Chen, J., & Choi, S. w. (2024). Exploring Key Drivers for Embracing Artificial Intelligence in Public Relations Pedagogy. Journalism & Mass Communication Educator, 80(2), 175-196. https://doi.org/10.1177/10776958241299075

Mohamed, A., Ghazali, N., & Mokhtar, M. M. (2023). Chatbots: A New Digital Teaching Tool Paradigm in Artificial Intelligence (AI) Technology Among Secondary School Teachers. Journal of Public Administration and Governance, 13(4), 23. https://doi.org/10.5296/jpag.v13i4.21577

Ng, D. T. K., Leung, J. K. L., Su, J., Ng, 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

Omeh, C. B., Olelewe, C. J., & Ohanu, I. B. (2025). Impact of Artificial Intelligence Technology on Students' Computational and Reflective Thinking in a Computer Programming Course. Computer Applications in Engineering Education, 33(3). https://doi.org/10.1002/cae.70052

Park, J. H. (2023). A Case Study on Enhancing the Expertise of Artificial Intelligence Education for Pre-Service Teachers. https://doi.org/10.20944/preprints202305.2006.v1

Ruiz, I. d. A., Pérez, F., & Ferreras, J. M. M. (2024). Extended TAM Based Acceptance of AI-Powered ChatGPT for Supporting Metacognitive Self-Regulated Learning in Education: A Mixed-Methods Study. Heliyon, 10(8), e29317. https://doi.org/10.1016/j.heliyon.2024.e29317

Shang, J. (2025). Charting Diverse Pathways to AI Empowerment Among Teachers: A Multi-Case Study in Chinese K-12 Education. International Journal of Chinese Education, 14(3). https://doi.org/10.1177/2212585x251408661

Sihag, P., & Vibha, V. (2024). Transforming and Reforming the Indian Education System With Artificial Intelligence. Digital Education Review(45), 98-105. https://doi.org/10.1344/der.2024.45.98-105

Suvendu, R., & S, D. P. (2024). AI-Driven Flipped Classroom: Revolutionizing Education Through Digital Pedagogy. British Journal of Education Learning and Development Psychology, 7(2), 169-179. https://doi.org/10.52589/bjeldp-ltdjflih

Tetteh, A., Foli, J. Y., Ackon, S. K., & Awaah, F. (2026). Towards Improving Students’ Curiosity Using Indigenous Teaching Methods: Applying the Culturo-Techno-Contextual Approach. https://doi.org/10.21203/rs.3.rs-10058356/v1

Wei, S. (2024). Research on the Disruptive Transformation and Future Development of American Teaching Models in the Context of Intelligent Transformation. Advances in Economics Management and Political Sciences, 72(1), 201-210. https://doi.org/10.54254/2754-1169/72/20240696

Williams, R., Alghowinem, S., & Breazeal, C. (2024). Dr. R.O. Bott Will See You Now: Exploring AI for Wellbeing With Middle School Students. Proceedings of the Aaai Conference on Artificial Intelligence, 38(21), 23309-23317. https://doi.org/10.1609/aaai.v38i21.30379

Yeter, I. H., Yang, W., & Sturgess, J. B. (2024). Global Initiatives and Challenges in Integrating Artificial Intelligence Literacy in Elementary Education: Mapping Policies and Empirical Literature. Future in Educational Research, 2(4), 382-402. https://doi.org/10.1002/fer3.59

Zhu, Y., & Siquan, X. (2025). The Impact of Pre‐Service Language Teachers' Basic Psychological Needs on Behavioural Intentions to Utilise Artificial Intelligence ( AI ) in Teaching: AI Literacy and Self‐Efficacy as Mediators. European Journal of Education, 60(3). https://doi.org/10.1111/ejed.70160