تحول آفرینی هوش مصنوعی در تعالی آموزشی: واکاوی کاربردهای هوش مصنوعی با رویکردی آمیخته

چکیده مقاله :

 به منظور اثربخشی سرمایه گذاریها، تصمیم گیرندگان می بایست درک روشنی از فرصت های فناوری های مبتنی بر هوش مصنوعی داشته باشند تا بهتر بتوانند جهت گیری ابتکارات مبتنی برهوش مصنوعی در حوزه آموزش را همسو  با اهداف کلان آموزشی سازند. علیرغم محبوبیت مطالعات در این حوزه، طراحی چارچوبی یکپارچه ازکاربردهای هوش مصنوعی در حوزه آموزشی که به واکاوی فرصت ها و نیز اولویت بندی آنها پرداخته باشد مورد غفلت واقع شده است. لذا هدف اصلی پژوهش پرکردن شکاف تئوریک با رویکردی آمیخته می باشد که در مرحله اول، به منظور شناسایی و دسته بندی کاربردها علاوه بر مرور مطالعات این حوزه از روش کیفی گروه کانونی بهره گرفته شده است. در مرحله دوم، با استفاده از رویکرد کمی بهترین-بدترین به اولویت بندی کاربردهای شناسایی شده در مرحله قبل پرداخته شده است. یافته های پژوهش نشان می دهد که چارچوب پیشنهادی پژوهش شامل سه لایه اصلی ابزارهای مبتنی بر هوش مصنوعی، کاربردها و ذینفعان می باشد. دسته بندی اصلی کاربردها به ترتیب اولویت عبارتند از  آموزش و یادگیری شخصی سازی شده، ارتباطات و تعاملات، برنامه ریزی شخصی سازی شده آموزشی، ارزیابی و بازخور هوشمند و مدیریت و اتوماسیون فرایندهای آموزشی. در این پژوهش با بهره گیری رویکرد آمیخته علاوه بر استخراج تجارب بین المللی و با بهره گیری از جلسات گروه کانونی به واکاوی دانش بومی خبرگان پرداخته شده تا الگویی کل نگرانه از کاربردهای هوش مصنوعی در آموزش طراحی گردد و در نهایت به اولویت بندی آنها پرداخته شده که در ادبیات کمتر به آن پرداخته شده است.  

چکیده انگلیسی :

 Given the growing importance of adopting such innovative technologies in education, investment in AI-enabled educational initiatives has become a strategic priority for educational institutions. To ensure the effectiveness of these investments, decision-makers require a comprehensive understanding of the opportunities and applications of AI in order to align AI-driven initiatives with broader educational objectives. Despite the increasing volume of research in this field, the development of an integrated framework that systematically identifies, categorizes, and prioritizes AI applications in education has received limited attention.

Accordingly, the primary objective of this study is to address this theoretical gap through a mixed-methods approach. In the first phase, AI applications in education were identified and categorized through a comprehensive literature review and focus group discussions involving domain experts. In the second phase, the identified applications were prioritized using the Best-Worst Method (BWM). The findings reveal that the proposed framework comprises three primary layers: AI-based tools, application domains, and stakeholders. The identified application categories, ranked by priority, include personalized teaching and learning, communication and interaction, personalized educational planning, intelligent assessment and feedback, and educational management and process automation.

By integrating international evidence from the literature with the contextual knowledge of experts obtained through focus group discussions, this study develops a holistic framework for understanding AI applications in education. Furthermore, it prioritizes these applications, an aspect that has been largely overlooked in previous studies. .

منابع و مأخذ:

Refrences
Abduljabbar, I. A., & Abdullah, S. M. (2022). An evolutionary algorithm for solving academic courses timetable scheduling problem. Baghdad Science Journal, 19(2), 11.
Almasri, F. (2024). Exploring the impact of artificial intelligence in teaching and learning of science: A systematic review of empirical research. Research in Science Education, 54(5), 977-997.
Amin, M. R. M., Ismail, I., & Sivakumaran, V. M. (2025). Revolutionizing education with artificial intelligence (AI)? Challenges, and implications for open and distance learning (ODL). Social Sciences & Humanities Open, 11, 101308.
Bh, M. P., Yellapurkar, S., Chengappa, K., & Pentapati, K. C. (2026). Personalized Learning With Artificial Intelligence in Dental Education: An Integrative Review. International Journal of Dentistry, 2026(1), 8928166.
Bhutoria, A. (2022). Personalized education and artificial intelligence in the United States, China, and India: A systematic review using a human-in-the-loop model. Computers and Education: Artificial Intelligence, 3, 100068.
Birgün, M. (2026). Integrating AI into Qur'an learning: Technical advances and pedagogical gaps. Social Sciences & Humanities Open, 13, 102499.
Brada, A., & Dahmani, F. (2024). Artificial intelligence technologies and their significance in enhancing the quality of adaptive e-learning. Journal of Science and Knowledge Horizons, 4(02), 30-47.
Castro-Schez, J. J., Glez-Morcillo, C., Albusac, J., & Vallejo, D. (2021). An intelligent tutoring system for supporting active learning: A case study on predictive parsing learning. Information Sciences, 544, 446-468.
Chen, X., Xie, H., & Hwang, G. J. (2020). A multi-perspective study on artificial intelligence in education: Grants, conferences, journals, software tools, institutions, and researchers. Computers and Education: Artificial Intelligence, 1, 100005.
Dogan, M. E., Goru Dogan, T., & Bozkurt, A. (2023). The use of artificial intelligence (AI) in online learning and distance education processes: A systematic review of empirical studies. Applied sciences, 13(5), 3056.
Embarak, O. H. (2022). Internet of Behaviour (IoB)-based AI models for personalized smart education systems. Procedia Computer Science, 203, 103-110.
Embarak, O. H. (2022). Internet of Behaviour (IoB)-based AI models for personalized smart education systems. Procedia Computer Science, 203, 103-110.
Gao, P. P., Nagel, A., & Biedermann, H. (2020). Categorization of Educational Technologies as Related to. Pedagogy in basic and higher education: Current developments and challenges, 167.
Ghaffar, A., Din, I. U., Tariq, A., & Zafar, M. H. (2025). Hybridization and artificial intelligence in optimizing university examination timetabling problem: A systematic review. Review of Education, 13(2), e70071.
Hadyaoui, A., & Cheniti-Belcadhi, L. (2026). Scalable adaptive assessment framework for collaborative eLearning: a service-oriented AI approach. Smart Learning Environments, 13(1), 8.
Hennink, M., & Kaiser, B. N. (2022). Sample sizes for saturation in qualitative research: A systematic review of empirical tests. Social science & medicine, 292, 114523.
Demandsage. (2026). 77 AI in Education Statistics 2026 (Global Trends & Facts), Available at: https://www.demandsage.com/ai-in-education-statistics/ (Accessed 17 April 2026).
Hu, C., Li, F., Wang, S., Gao, Z., Pan, S., & Qing, M. (2025). The role of artificial intelligence in enhancing personalized learning pathways and clinical training in dental education. Cogent Education, 12(1), 2490425.
Jin, S. H., Im, K., Yoo, M., Roll, I., & Seo, K. (2023). Supporting students’ self-regulated learning in online learning using artificial intelligence applications. International Journal of Educational Technology in Higher Education, 20(1), 37.
Liu, X., & Zhong, B. (2025). Integrating generative Artificial Intelligence into student learning: A systematic review from a TPACK perspective. Educational Research Review, 100741.
Muangprathub, J., Boonjing, V., & Chamnongthai, K. (2020). Learning recommendation with formal concept analysis for intelligent tutoring system. Heliyon, 6(10).
Qin, Y., Huang, Z., Yu, J., Qing, P., Lui, S., Liu, R., ... & Hu, N. (2022). Practice-based learning using smart class: a competency-based model in undergraduate radiology education. Academic Radiology, 29(1), 150-157.
Sajja, R., Sermet, Y., Cwiertny, D., & Demir, I. (2025). Integrating AI and learning analytics for data-driven pedagogical decisions and personalized interventions in education. Technology, knowledge and learning, 1-31.
Seo, K., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021). The impact of artificial intelligence on learner–instructor interaction in online learning. International journal of educational technology in higher education, 18(1), 54.
Shaikh, A. A., Kumar, A., Jani, K., Mitra, S., García-Tadeo, D. A., & Devarajan, A. (2022). The role of machine learning and artificial intelligence for making a digital classroom and its sustainable impact on education during COVID-19. Materials Today: Proceedings, 56, 3211-3215.
Su, J., & Yang, W. (2022). Artificial intelligence in early childhood education: A scoping review. Computers and Education: Artificial Intelligence, 3, 100049.
Swiecki, Z., Khosravi, H., Chen, G., Martinez-Maldonado, R., Lodge, J. M., Milligan, S., ... & Gašević, D. (2022). Assessment in the age of artificial intelligence. Computers and Education: Artificial Intelligence, 3, 100075.
Tan, X., Cheng, G., & Ling, M. H. (2025). Artificial intelligence in teaching and teacher professional development: A systematic review. Computers and Education: Artificial Intelligence, 8, 100355.
Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, 124167.
Wang, T., & Cheng, E. C. K. (2021). An investigation of barriers to Hong Kong K-12 schools incorporating Artificial Intelligence in education. Computers and Education: Artificial Intelligence, 2, 100031.
Yetik, S. S. (2025). The Impact of Artificial Intelligence on University Teaching Processes: An Analysis Based on Faculty Perspectives. Kastamonu Education Journal, 33(3), 448-468.