تدوین و اعتبارسنجی چارچوب سیاستی شایستگی‌های دانشجومعلمان برای به‌کارگیری مسئولانه هوش مصنوعی مولد در برنامه کارورزی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناسی‌ارشد روان‌شناسی تربیتی، گروه روان شناسی، دانشکده علوم تربیتی و روان شناسی، دانشگاه یاسوج، یاسوج، ایران

2 دانشیار روان شناسی تربیتی، گروه روان شناسی، دانشکده علوم تربیتی و روان شناسی، دانشگاه یاسوج، یاسوج، ایران

3 کارشناسی‌ارشد مشاوره خانواده، گروه مشاوره، دانشگاه آزاد اسلامی واحد استهبان، استهبان، ایران

10.48310/edup.2026.24474.1505

چکیده

پیشینه و اهداف: این پژوهش با هدف تدوین و اعتبارسنجی چارچوب شایستگی‌های دانشجومعلمان برای به‌کارگیری مسئولانه هوش مصنوعی مولد در کارورزی انجام شد.روش‌ها: در بخش کیفی پژوهش آمیخته، با تحلیل مضمون قیاسی بر پایه چارچوب یونسکو، مضامین استخراج شد. در بخش کمی، روایی محتوایی چارچوب و بسته آموزشی با شاخص‌های CVR، CVI و ضریب کندال توسط ۱۵ متخصص ارزیابی گردید. یافته‌ها: یافته‌ها نشان داد چارچوب نهایی شامل ۴ بعد اصلی، ۱۵ مضمون سازمان‌دهنده و ۴۲ مضمون پایه است. ابعاد اصلی چارچوب عبارتند از: ۱) شایستگی شناختی - انتقادی، ۲) شایستگی فنی - پداگوژیکی، ۳) شایستگی اخلاقی - حرفه‌ای، و ۴) عاملیت و هویت حرفه‌ای. همچنین بسته آموزشی پیشنهادی شامل ۱۲ جلسه کارگاهی بود که تمامی جلسات از نظر روایی محتوایی مورد تأیید قرار گرفتند؛ به‌طوری‌که مقادیر CVR بین ۰/۶۰ تا ۱/۰۰و مقادیر CVI بین ۰/۹۱ تا ۰/۹۶به‌دست آمد. ضریب هماهنگی کندال نیز برابر با ۰/۵۱۲ (۰/۰۰۱>p)محاسبه شد که نشان‌دهنده توافق آماری معنادار و قوی ارزیابان دربارهٔ مؤلفه‌ها و توالی بسته است. نتیجه‌گیری: نتایج نشان داد که به‌کارگیری مسئولانه هوش مصنوعی مولد در کارورزی، مستلزم تلفیق هم‌زمان دانش انتقادی، مهارت فنی، حساسیت اخلاقی و حفظ عاملیت حرفه‌ای دانشجومعلم است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Developing and Validating a Policy Framework of Student Teachers’ Competencies for Responsible Use of Generative AI in the Practicum Program

نویسندگان [English]

  • Alireza Afra 1
  • Mohammad Reza Firouzi 2
  • Alireza Najafi Koohjerdi 3
1 M.A. in Educational Psychology, Department of Psychology, Faculty of Humanities, University of Yasouj, Yasouj, Iran
2 Associate Professor, Department of Psychology, Faculty of Humanities, University of Yasouj, Yasouj, Iran.
3 M.A. in Family Counseling, Department of Counseling, Islamic Azad University, Estahban Branch, Estahban, Iran.
چکیده [English]

Background and Objectives: This study aimed to develop and validate a competency framework for the responsible use of generative AI by student-teachers in the practicum.Methods: Employing a sequential mixed-methods design, deductive thematic analysis based on the UNESCO framework identified core themes qualitatively. Quantitatively, a panel of 15 experts evaluated content validity using CVR, CVI, and Kendall’s coefficient (w).Findings: The validated framework encompasses 4 dimensions (cognitive-critical, technical-pedagogical, ethical-professional, and agency-identity), 15 organizing themes, and 42 basic themes. Statistical analysis confirmed robust content validity: CVR (0.60–1.00), CVI (0.91–0.96), and Kendall’s
w (0.512, p<0.001).Conclusion: Responsible AI integration in clinical practicum requires balancing critical discernment, pedagogy, data ethics, and human teacher agency.

کلیدواژه‌ها [English]

  • Generative Artificial Intelligence (GenAI)
  • Professional Competency
  • Student-Teachers
  • Internship(Practicum)
  • Content Validation

مراجع

Abdolmaleki, Sh., & Bagheri, A. (2025). The effect of AI-based instruction on the performance
blended learning course performance. The Journal of Theory and Practice in Teachers of pre-service teachers in integrated teaching. Tajarebe-ye Moallemi, 4(4), 122–146.[In Persian] https://doi.org/10.48310/istt.2025.20771.117
Abdulmaleki, Sh., & Bagheri, A. (2026). Study of artificial intelligence-based education on student-teacher Education, 4(4), 122–146.[In Persian] https://doi.org/10.12345/tej.12.10.112
Adamakis, M., & Rachiotis, T. (2025). Artificial intelligence in higher education: A state-of-the-art overview of pedagogical integrity, artificial intelligence literacy, and policy integration. Encyclopedia, 5(4), 180.‏ https://doi.org/10.3390/encyclopedia5040180
Attride-Stirling, J. (2001). Thematic networks: An analytic tool for qualitative research. Qualitative Research, 1(3), 385–405. https://doi.org/10.1177/146879410100100307
Casadomet, A. G. (2024). Digital Skills Through the Prism of European and Spanish Supranational Frameworks. In Teaching and Assessment in the Era of Education 5.0 (pp. 73-86). IGI Global Scientific Publishing. ‏https://doi.org/4018/979-8-3693-3045-6.ch004
Debreli, E. (2026). Negotiating agency and emotional wellbeing in AI-infused ELT practicum: A qualitative study of pre-service teachers’ lived experiences. Language Teaching Research, 13621688261422528. https://doi.org/10.1177/13621688261422528
Dieterle, E., Dede, C., & Walker, M. (2024). The cyclical ethical effects of using artificial intelligence in education. AI & Society, 39(2), 633-643.‏
https://doi.org/10.1007/s00146-022-01497-w
Ding, H., Xu, G., & Chen, B. (2025, November). From “Free from Coercion and Inducement” to “Curriculum Development”: A Study on the Value Legitimacy of GAI in Curriculum Reform. In the 2025 International Conference on Educational Technology Management (ICETM) (pp. 587-591). IEEE. https://doi.org/10.1109/ICETM67477.2025.11413352
Frøsig, T. B., & Romero, M. (2024). Teacher agency in the age of generative AI: towards a framework of hybrid intelligence for learning design. arXiv preprint arXiv:2407.06655.
https://doi.org/10.48550/arXiv.2407.06655
Hsu, H. P. (2026). Artificial Intelligence literacy and competency in pre-service teacher education. Encyclopedia, 6(4), 76. https://doi.org/10.3390/encyclopedia6040076
Jaipal, K. (2009). Re‐envisioning mentorship: pre‐service teachers and associate teachers as co‐learners. Teaching Education, 20(3), 257-276.
https://doi.org/10.1080/10476210902887503
Jita, T., Jita, L. C., & Omoniyi, A. A. (2025). Mathematics education in the AI era: Preparing teachers for evolving classroom demands. International Journal of Learning, Teaching and Educational Research, 24(10), 417-434.‏ https://doi.org/10.26803/ijlter.24.10.19
Koehler, M., & Mishra, P. (2009). What is technological pedagogical content knowledge (TPACK)?. Contemporary issues in technology and teacher education, 9(1), 60-70.
https://doi.org/10.1177/002205741319300303
Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel psychology, 28(4), 563.‏ https://doi.org/10.1111/j.1744-6570.1975.tb01393.x
Le, T. H., Huynh, L., Dang, B., Pham, H. H., Nguyen, N. T. V., & Nguyen, A. (2026). Development and evaluation of artificial intelligence literacy training for teacher education students. British Journal of Educational Technology. https://doi.org/10.1111/bjet.70047
Liu, S. H. (2012). A multivariate model of factors influencing technology use by preservice teachers during practice teaching. Journal of Educational Technology & Society, 15(4), 137-149.
Mardianti, D., Kusimawati, I., & Arifin, B. (2026). Reorienting Teacher and Lecturer Competencies in the Era of Deep Learning and Generative Artificial Intelligence in the Transformation of 21st-Century Learning. JPI: Jurnal Pustaka Indonesia, 6(1), 414-432.‏ https://doi.org/10.62159/jpi.v6i1.2257
Miao, F., & Shiohira, K. (2024). AI competency framework for students. UNESCO Publishing.
Miao, F., Holmes, W., Ronghuai, H., & Hui, Z. (2023, January). AI and education: guidance for policy-makers-UNESCO Digital Library. https://doi.org/10.54675/PCSP7350
Momdjian, L., Manegre, M., & Gutiérrez-Colón, M. (2025). A study of preservice teachers’ digital competence development: Exploring the role of direct instruction, integrated practice, and modeling. Evaluation and Program Planning, 109, 102538.‏
https://doi.org/10.1016/j.evalprogplan.2025.102538
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
Ololoh, J. N. (2026). A systematic review of generative artificial intelligence in education: Pedagogical impacts, ethical risks, and future directions. International Journal of Applied Resilience and Sustainability, 2(2), 1390-1419. https://doi.org/10.70593/deepsci.0202055
Rothinam, N., Vengrasalam, R., Jabamoney, S., Naidu, S., Gill, R. S., & Foong, A. L. (2025). Systematic review on teacher education and the integration of artificial intelligence. Journal of Cultural Analysis and Social Change, 5061-5075.‏
https://doi.org/10.64753/jcasc.v11i1.3873
Sat, M. (2025). The impact of AI integration in project preparation in education course on pre-service teachers’ innovativeness, AI anxiety, attitudes, and acceptance. BMC psychology, 13(1), 1297. https://doi.org/10.1186/s40359-025-03647-3
Simuţ, R., Simuţ, C., Badulescu, D., & Badulescu, A. (2024). ARTIFICIAL INTELLIGENCE AND THE MODELLING OF TEACHERS'COMPETENCIES. Amfiteatru Economic, 26(65), 181-200.‏ https://doi.org/10.24818/ea/2024/65/181
Su, J., & Yang, W. (2024). Powerful or mediocre? Kindergarten teachers’ perspectives on using ChatGPT in early childhood education. Interactive Learning Environments, 32(10), 6496-6508. https://doi.org/10.1080/10494820.2023.2266490
Tan, S. C., Tan, Y. Y., Teo, C. L., & Yuan, G. (2026). Teachers' professional agency in learning with AI: A case study of a generative AI‐based knowledge building learning companion for teachers. British Journal of Educational Technology, 57(4), 943-964.‏ 
https://doi.org/10.1111/bjet.70013
Tondeur, J., Howard, S. K., & Yang, J. (2021). One-size does not fit all: Towards an adaptive model to develop preservice teachers’ digital competencies Computers in Human Behavior, 116, 106659.‏ https://doi.org/10.1016/j.chb.2020.106659
Tzirides, A. O. O., Zapata, G., Kastania, N. P., Saini, A. K., Castro, V., Ismael, S. A., ... & Kalantzis, M. (2024). Combining human and artificial intelligence for enhanced AI literacy in higher education. Computers & Education Open, 6, 100184.
https://doi.org/10.1016/j.caeo.2024.100184
UNESCO. (2024). AI competency framework for teachers. United Nations Educational, Scientific and Cultural Organization. https://unesdoc.unesco.org/ark:/48223/pf0000391104
Vaughan, N., & Wah, J. L. (2026). Generative AI in teacher education: Theory and practice. Journal of Digital Life and Learning, 6(1), 155-186. https://doi.org/10.51357/jdll.v6i1.338
Waltz, C. F., & Bausell, B. R. (1981). Nursing research: design statistics and computer analysis. Davis Fa.‏
Wang, C., & Zhu, M. (2026). Bridging AI Literacy and AI Pedagogy in Teacher Education and Professional Development: A Multiple Case Study of AI in Education (AIEd) Certificate Programs. Journal of Technology and Teacher Education, 34(1), 79-105.
‏ https://doi.org/10.70725/638212rjkdxq
Wang, V. (2026). Using AI Responsibly, Critically, Ethically, and Flexibly: A Situational and Andragogical Framework for Learning Autonomy. International Journal of AI in Pedagogy, Innovation, and Learning Futures, 1(1). https://doi.org/10.46787/ijaipil.v2026i1.6978
مطالعات سیاست گذاری تربیت معلم
دوره 8، شماره 2
مرداد 1405
صفحه 95-111

فایل ها

سابقه مقاله

  • تاریخ دریافت: 22 شهریور 1405
  • تاریخ بازنگری: 03 مهر 1405
  • تاریخ پذیرش: 08 مهر 1405

هم رسانی

ارجاع به این مقاله

آمار