فصلنامه مطالعات مدیریت راهبردی

فصلنامه مطالعات مدیریت راهبردی

تحلیل راهبردی تجارب زیسته دانشجو معلمان از کاربرد هوش مصنوعی در آموزش عالی: از فرصت‌سازی تا دوگانگی‌های متناقض‌نما

نوع مقاله : پژوهشی

نویسندگان
1 استادیار، گروه مدیریت بازرگانی و فناوری اطلاعات، دانشگاه پیام نور، تهران، ایران
2 استادیار، گروه آموزش جغرافیا، دانشگاه فرهنگیان، تهران، ایران
چکیده
در طی چند دهه اخیر، با رشد شتابان فناوری‌های نوین، به‌ویژه هوش مصنوعی، نظام‌های آموزش عالی با فرصت‌ها و چالش‌های بی‌سابقه‌ای مواجه شده‌اند. این امر ضرورت بازاندیشی در راهبردهای یاددهی - یادگیری و سیاست‌گذاری‌های آموزشی را دوچندان ساخته است. پژوهش حاضر با هدف تحلیل راهبردی تجارب زیسته دانشجو معلمان از کاربرد هوش مصنوعی در آموزش عالی، به شیوه کیفی و مبتنی بر رویکرد پدیدارشناسی انجام شد. جامعه پژوهش شامل ۴۳ دانشجو معلم است که با روش تدریس مبتنی بر استفاده آزاد از نرم‌افزارهای هوش مصنوعی آموزش دیدند. با استفاده از نمونه‌گیری هدف‌مند ۱۸ مشارکت‌کننده تا رسیدن به اشباع مضمونی انتخاب شدند. داده‌ها از طریق مصاحبه‌های نیمه‌ساختاریافته عمیق گردآوری شد. روش تحلیل داده‌ها، تحلیل مضمون با رویکرد براون و کلارک بود که با استفاده از روش بازبینی همتایان و بررسی اعضا اعتبارسنجی شد. یافته‌ها در سه حوزه اصلی دربرگیرنده مزایای استفاده از هوش مصنوعی شامل 5 مضمون اصلی تحول در آموزش، توانمندسازی دانشجویان، دسترسی و انعطاف‌پذیری، الزامات، تعامل اجتماعی؛ محدودیت‌های استفاده از هوش مصنوعی شامل 6 مضمون اصلی: مشکلات سلامتی، موانع دسترسی و عملکرد، چالش‌های فنی، چالش‌های آموزشی، چالش‌های اخلاقی و امنیتی وآینده‌نگاری و در نهایت دوگانگی‌های متناقض‌نما شامل 4 مضمون اصلی جهت‌گیری محتوای هوش مصنوعی، سرعت یادگیری، مدیریت زمان در تدریس، تمرکز حواس در کلاس سازماندهی شدند. این دوگانگی‌ها نشان‌دهنده پیامدهای متناقض‌نما هوش مصنوعی هستند، مانند افزایش سرعت یادگیری که همزمان با خطر بمباران اطلاعاتی و کاهش تمرکز دانشجویان همراه است. بر اساس این یافته‌ها، بهره‌گیری راهبردی از هوش مصنوعی مستلزم سیاست‌گذاری هوش‌مندانه، طراحی چارچوب‌های اخلاقی، توسعه زیرساخت‌های فناورانه، و توانمندسازی مستمر معلمان و دانشجویان است. این مطالعه نتایج قابل‌توجهی را برای بازنگری برنامه‌های درسی و تدوین سیاست‌های منسجم به‌منظور بهره‌گیری آگاهانه و مسئولانه از هوش مصنوعی در آموزش عالی ارائه می‌کند.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Strategic analysis of student teachers’ lived experiences of artificial intelligence application in higher education: from opportunity creation to paradoxical dualities

نویسندگان English

Masarat Ayat 1
Rasoul Sharifinajafabadi 2
1 Assistant Proffessor, Department of Business Administration and Information Technology Management, Payame Noor University, Tehran, Iran
2 Assistant Proffessor, Department of Geography Education, Farhangian University, Tehran, Iran
چکیده English

Introduction:
In recent decades, the rapid development of emerging technologies, particularly artificial intelligence (AI), has profoundly influenced higher education systems worldwide. The integration of AI tools into teaching and learning processes has generated a wide range of expectations and concerns. On the one hand, AI is considered a powerful strategic instrument capable of transforming instructional methods, enhancing learner autonomy, and expanding access to educational resources. On the other hand, it brings significant challenges including ethical dilemmas, infrastructural limitations, and potential risks of superficial learning or dependency on technology. In Iran, the discourse around AI in education is still evolving, and there is a need for empirical research that explores how future educators experience and perceive these technologies. Therefore, the present study aimed to conduct a strategic analysis of the lived experiences of student-teachers in applying AI in higher education, with an emphasis on understanding the paradoxical dualities, opportunities, and managerial challenges arising from this phenomenon.
Methodology:
This research employed a qualitative design with a phenomenological approach to capture the depth and complexity of participants’ experiences. The study population comprised 43 student-teachers from Farhangian University and Payame Noor University who participated in courses utilizing teaching strategies that encouraged the free use of AI-based applications and tools. Using purposive sampling, 18 participants were selected to ensure maximum diversity of experiences until thematic saturation was reached. Data were collected through in-depth semi-structured interviews, each lasting approximately 60–90 minutes. The interviews explored perceptions of benefits, challenges, and contradictions encountered when engaging with AI in educational contexts. Braun and Clarke’s thematic analysis framework guided the systematic coding and organization of data. To enhance trustworthiness, the study applied peer debriefing and member checking techniques.
Results and Discussion:
The analysis yielded a rich thematic structure organized into three primary domains: benefits, limitations, and paradoxical dualities. Benefits of AI Use: Participants emphasized that AI had transformative potential in higher education. Five main themes emerged in this domain: Transformation in Teaching: AI facilitated the personalization of content, adaptive learning pathways, and dynamic lesson planning, leading to more engaging and relevant instruction. Student Empowerment: Tools such as chatbots and intelligent tutoring systems increased learner autonomy and confidence by providing instant feedback and tailored support. Accessibility and Flexibility: AI expanded access to diverse educational resources beyond temporal and spatial constraints. Technological Learning Requirements: The integration of AI motivated students to improve their digital literacy and adapt to new learning paradigms. Social Interaction: Certain AI tools supported collaborative learning and enhanced peer engagement. Limitations of AI Use: Despite the perceived advantages, participants identified critical constraints. Six main themes were extracted: Health Problems: Prolonged use of AI tools led to eye strain, fatigue, and reduced physical activity. Access and Performance Barriers: Inadequate infrastructure, limited internet connectivity, and outdated hardware impeded effective utilization. Technical Challenges: Users faced difficulties in configuring and mastering complex AI applications. Educational Challenges: There were concerns that AI might encourage surface learning and reduce critical thinking. Ethical and Security Challenges: Participants worried about data privacy, algorithmic bias, and intellectual property issues. Future-Oriented Challenges: The uncertainty about AI’s long-term impacts on professional identity and employment prospects created anxiety. Paradoxical Dualities: One of the most significant findings was the identification of paradoxical dualities that underscored the multifaceted nature of AI in education: Content Orientation: While AI could generate neutral and objective materials, it sometimes produced biased or contextually irrelevant content. Learning Speed: Although AI accelerated information retrieval, it occasionally diminished deep reflection and critical analysis. Time Management: AI tools could optimize lesson planning but also led to time wastage due to technical disruptions. Classroom Focus: AI applications enhanced engagement for some students while distracting others. These contradictions align with previous studies, which emphasize that AI implementation requires careful regulation and ongoing evaluation.


Conclusion:
Overall, the study demonstrates that artificial intelligence holds substantial strategic potential for enhancing educational quality, personalizing learning experiences, and improving access to knowledge. However, realizing these benefits demands intelligent policymaking, robust ethical frameworks, and systematic investment in technological infrastructures. The findings underscore that educators and policymakers must approach AI integration with a balanced perspective, acknowledging both its transformative promise and its inherent risks. By addressing infrastructural gaps, clarifying ethical standards, and equipping teachers and students with necessary competencies, Iran’s higher education system can leverage AI in a way that is both innovative and responsible. This research contributes valuable insights for curriculum redesign and policy development aimed at fostering meaningful, equitable, and future-ready learning environments.

کلیدواژه‌ها English

Artificial intelligence
Higher education
Strategic educational management
Lived experiences
Paradoxical dualities
1.      Abbasnejad, B., Soltani, S., Taghizadeh, F., & Zare, A. (2025). Developing a multilevel framework for AI integration in technical and engineering higher education: insights from bibliometric analysis and ethnographic research. Interactive Technology and Smart Education, ahead-of-print(ahead-of-print). https://doi.org/10.1108/ITSE-12-2024-0314
2.      Abdelkader, A. A. M., Hassan, H., & Abdelkader, M. (2024). The Role of Artificial Intelligence in Designing Higher Education Courses: Benefits and Challenges. In M. D. Lytras, A. Alkhaldi, S. Malik, A. C. Serban, & T. Aldosemani (Eds.) , The Evolution of Artificial Intelligence in Higher Education (Emerald Studies in Active and Transformative Learning in Higher Education, pp. 83–97). Emerald Publishing Limited. https://doi.org/10.1108/978-1-83549-486-820241005
3.      Aghili, S. H., & ahmadihaji, O. (2025). Analysis of Short-Term In-Service Training Courses Based on the CIPP Model؛ Providing AI-Based Solutions within the Framework of Educational Planning. Journal of Educational Planning Studies, 13(26), 150–171. [In Persian]. https://doi.org/10.22080/eps.2025.28107.2293
4.      Aksakalli, A. (2024). From Marx to the classroom: Understanding teacher alienation in policy contexts. Policy Futures in Education, 23(2), 337-354. https://doi.org/10.1177/14782103241279583
5.      Al-Kamzari, F., & Alias, N. (2025). A systematic literature review of artificial intelligence (AI) in secondary school physics: applications, benefits, and challenges. Interactive Learning Environments, 1(18). https://doi.org/10.1080/10494820.2025.2508323
6.      Anyon, J. (2011). Marx and education. Routledge.
7.      Barrot, J. S. (2025). Leveraging Google Gemini as a Research Writing Tool in Higher Education. Tech Know Learn, 30, 593–600. https://doi.org/10.1007/s10758-024-09774-x
8.      Bilal, D., He, J., & Liu, J. (2025). Guest editorial: AI in education: transforming teaching and learning. Information and Learning Sciences, 126(1/2), 1–7. https://doi.org/10.1108/ILS-01-2025-268
9.      Bohara, D. K., & Rana, K. (2024). Unmasking teachers’ proficiency in harnessing Artificial Intelligence (AI) for transformative education. SN Soc Sci, 4, 203. https://doi.org/10.1007/s43545-024-01003-7
10.   Bourdieu, P. (2020). Outline of a Theory of Practice. In The new social theory reader (pp. 80-86). Routledge. https://www.taylorfrancis.com/chapters/edit/10.4324/9781003060963-11/outline-theory-practice-pierre-bourdieu
11.   Bungati, L. (2024). Improving Learning Achievement in Elementary Schools through an Active Learning Approach. Journal of Education Review Provision, 4(1), 1–5. https://doi.org/10.55885/jerp.v4i1.319
12.   Busuttil, L., & Calleja, J. (2025). Teachers’ Beliefs and Practices About the Potential of ChatGPT in Teaching Mathematics in Secondary Schools. Digit Exp Math Educ, 11, 140–166. https://doi.org/10.1007/s40751-024-00168-3
13.   Cheng, E. C. K. (2025). Leveraging generative AI in science lesson study: transforming density concept instruction through ChatGPT integration. International Journal for Lesson and Learning Studies, ahead-of-print(ahead-of-print). https://doi.org/10.1108/IJLLS-11-2024-0277
14.   Christensen, C. M., Raynor, M., & McDonald, R. (2015). 17. Disruptive innovation. Harvard business review, 93(12), 44-53.
15.   Christian, M., Nan, G., Gularso, K., Dewi, Y. K., & Wibowo, S. (2024). Impact of AI Anxiety on Educators Attitudes Towards AI Integration. In 2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT) (pp. 1–7). IEEE. https://doi.org/10.1109/ICCIT62134.2024.10701130
16.   Churi, P. P., Joshi, S., Elhoseny, M., & Omrane, A. (Eds.). (2022). Artificial intelligence in higher education: A practical approach. CRC Press.
17.   Collie, R. J. (2021). Motivation Theory and Its Yields for Promoting Students' Social and Emotional Competence. In N. Yoder & A. Skoog-Hoffman (Eds.) , Motivating the SEL Field Forward Through Equity (Advances in Motivation and Achievement, Vol. 21, pp. 43–59). Emerald Publishing Limited. https://doi.org/10.1108/S0749-742320210000021004
18.   Cui, D., & Wu, F. (2021). The influence of media use on public perceptions of artificial intelligence in China: evidence from an online survey. Inf Dev, 37(1), 45–57. https://doi.org/10.1177/0266666919893411
19.   da Silva Tiago, R., & Mitchell, A. (2024). Integrating Digital Transformation in nursing education: best practices and challenges in Curriculum Development. In Digital transformation in higher education, Part B (pp. 57–101). Emerald Publishing Limited.
20.   Dai, C. P., & Ke, F. (2022). Educational applications of artificial intelligence in simulation-based learning: A systematic mapping review. Computers and Education: Artificial Intelligence, 3, 100087. https://doi.org/10.1016/j.caeai.2022.100087
21.   Demaidi, M. N. (2023). Artificial intelligence national strategy in a developing country. AI Soc. https://doi.org/10.1007/s00146-023-01779-x
22.   Fan, X., & Li, J. (2023). Artificial intelligence-driven interactive learning methods for enhancing art and design education in higher institutions. Applied Artificial Intelligence, 37(1), 2225907.    https://doi.org/10.1080/08839514.2023.2225907
23.   Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2024). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International, 61(3), 460–474. https://doi.org/10.1080/14703297.2023.2195846
24.   Gill, S., Xu, M., Patros, P., Wu, H., Kaur, R., Kaur, K., Fuller, S., Singh, M., Arora, P., Parlikad, A., Stankovski, V., Abraham, A., Ghosh, S., Lutfiyya, H., Kanhere, S., Bahsoon, R., Rana, O., Dustdar, S., Sakellariou, R., & Buyya, R. (2024). Transformative effects of ChatGPT on modern education: Emerging era of AI chatbots. Internet of Things and Cyber-Physical Systems, 4, 19–23. https://doi.org/10.1016/j.iotcps.2023.06.002
25.   Hafizi, N. (2024). Enhancements in design education by integrating advanced learning technologies (AR/VR) in architectural schools. Open House International, ahead-of-print(ahead-of-print). https://doi.org/10.1108/OHI-03-2024-0118
26.   Heydari, Z., Taleb, Z., & Golzari, Z. (2023). Metasynthesis of Internet of Things and Artificial intelligence applications in smart educational environments. Educational Technologies in Learning, 6(20), 134–165. [In Persian]. https://doi.org/10.22054/jti.2023.75649.1397
27.   Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education promises and implications for teaching and learning. Center for Curriculum Redesign. https://discovery.ucl.ac.uk/id/eprint/10139722/
28.   Hoseini Moghadam, M. (2023). Artificial Intelligence and the Future of University Education in Iran. Quarterly Journal of Research and Planning in Higher Education, 29(1), 1–25. [In Persian].  https://doi.org/10.61838/irphe.29.1.1
30.   Javed, F. (2024). The Evolution of Artificial Intelligence in Teaching and Learning of English Language in Higher Education: Challenges, Risks, and Ethical Considerations. In M. D. Lytras, A. Alkhaldi, S. Malik, A. C. Serban, & T. Aldosemani (Eds.) , The Evolution of Artificial Intelligence in Higher Education (Emerald Studies in Active and Transformative Learning in Higher Education, pp. 249–276). Emerald Publishing Limited. https://doi.org/10.1108/978-1-83549-486-820241015
31.   Jowarder, M., & I. (2023). The influence of ChatGPT on social science students: Insights drawn from undergraduate students in the United States. Indonesian J Innov Appl Sci, 3(2), 194–200. https://doi.org/10.47540/ijias.v3i2.878
32.   Karami, Z. (2025). Applications, challenges, and tools of artificial intelligence in education. Journal of Educational Sciences. [In Persian]. https://doi.org/10.22034/lrsi.2025.516171.1353
33.   Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
34.   Khastar. H. (2009). A Method for Calculating Coding Reliability in Qualitative Research Interviews. Methodology of Social Sciences and Humanities. 15(58). 161-174. [In Persian]
35.   Lai, K.-W. (2011). Digital technology and the culture of teaching and learning in higher education. Australasian Journal of Educational Technology, 27(8). https://doi.org/10.14742/ajet.892
36.   Lin, Y., & Yu, Z. (2024). A bibliometric analysis of artificial intelligence chatbots in educational contexts. Interactive Technology and Smart Education, 21(2), 189–213. https://doi.org/10.1108/ITSE-12-2022-0165
37.   Luckin, R., & Holmes, W. (2016). Intelligence unleashed: An argument for AI in education. University College London.
38.   Makeleni, S., Mutongoza, B. H., & Linake, M. A. (2023). Language education and artificial intelligence: an exploration of challenges confronting academ ics in global south universities. J Cult Values Educ, 6(2), 158–171. https://doi.org/10.46303/jcve.2023.14
39.   Mayombe, C. (2024). Promoting youths' skills acquisition through experiential learning theory in vocational education and training in South Africa. Higher Education, Skills and Work-Based Learning, 14(1), 130–145. https://doi.org/10.1108/HESWBL-10-2022-0216
40.   McGee, R. W. (2023). Is ChatGPT biased against conservatives? An empirical study. SSRN. https://doi.org/10.2139/ssrn.4359405
41.   Meadows, D. (2008). Thinking in systems: International bestseller. chelsea green publishing.
42.   Mokhtari, S. A. M., & Rezvani, R. (2022). The application of artificial intelligence in history education. Research in History Education, 3(4), 53–65. [In Persian].
43.   Motallebinejad, A., Fazeli, F., & Navaii, E. (2023). A systematic review of the promises and challenges of artificial intelligence for teachers. Technology and Scholarship in Education, 3(1), 23–44. [In Persian]. https://doi.org/10.30473/t-edu.2023.68819.1101
44.   Mwogosi, A., & Simba, R. (2025). Integration of AI into teaching methodologies in health training institutions in Tanzania. Journal of Research in Innovative Teaching & Learning, ahead-of-print(ahead-of-print). https://doi.org/10.1108/JRIT-03-2025-0069
45.   Nyaaba, M. (2025). Glocalizing Generative AI in Education for the Global South: The Design Case of 21st Century Teacher Educator AI for Ghana. arXiv preprint arXiv:2504.07149. https://doi.org/10.48550/arXiv.2504.07149
46.   Nyaaba, M. (2025). Glocalizing Generative AI in Education for the Global South: The Design Case of 21st Century Teacher Educator AI for Ghana. arXiv preprint arXiv:2504.07149.
47.   Ofosu-Asare, Y. (2025). Cognitive imperialism in artificial intelligence: counteracting bias with indigenous epistemologies. AI & Soc, 40, 3045–3061. https://doi.org/10.1007/s00146-024-02065-0
48.   Osorio, C., Fuster, N., Chen, W., Men, Y., & Juan, A. A. (2024). Enhancing accessibility to analytics courses in higher education through AI, simulation, and e-collaborative tools. Information, 15(8), 430. https://doi.org/10.3390/info15080430
49.   Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Res Pract Technol Enhanc Learn. https://doi.org/10.1186/s41039-017-0062-8
50.   Raimi, L., Bamiro, N. B., & Lim, S. A. (2024). Two Facets of AI-Driven Applications for Sustainable Learning and Development: A Systematic Review of Tech-Entrepreneurial Benefits and Threats to Creative Learning. In D. Crowther & S. Seifi (Eds.) , Social Responsibility, Technology and AI (Developments in Corporate Governance and Responsibility, Vol. 23, pp. 223–248). Emerald Publishing Limited. https://doi.org/10.1108/S2043-052320240000023012
51.   Ramezani, A., & Sharifi, M. (2025). Investigating the relationship between the use of artificial intelligence and critical thinking through the mediation of self-learning (Case study: high school students). Research In Instructional Methods, 3(3). [In Persian]. https://doi.org/10.22091/jrim.2025.11934.1171
52.   Rani, S., Kaur, G., & Dutta, S. (2024). Educational AI Tools: A New Revolution in Outcome-Based Education. In T. Singh, S. Dutta, S. Vyas, & Á. Rocha (Eds.) , Explainable AI for Education: Recent Trends and Challenges (Information Systems Engineering and Management, Vol. 19). Springer. https://doi.org/10.1007/978-3-031-72410-7_3
53.   Ren, X., & Wu, M. L. (2025). Examining Teaching Competencies and Challenges While Integrating Artificial Intelligence in Higher Education. TechTrends, 69, 519–538. https://doi.org/10.1007/s11528-025-01055-3
54.   Sayari, K. T. (2025). Infrastructure and Investment Needs for AI Implementation in Education. In Teachers' Roles and Perspectives on AI Integration in Schools (pp. 141–162). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-1017-6.ch005
55.   Shiri, A. (2024). Artificial intelligence literacy: a proposed faceted taxonomy. Digital Library Perspectives, 40(4), 681–699. https://doi.org/10.1108/DLP-04-2024-0067
56.   Sweller, J. (2023). The development of cognitive load theory: Replication crises and incorporation of other theories can lead to theory expansion. Educational Psychology Review, 35(4), 95. https://doi.org/10.1007/s10648-023-09817-2
57.   Voultsiou, E., & Moussiades, L. (2025). A systematic review of AI, VR, and LLM applications in special education: Opportunities, challenges, and future directions. Educ Inf Technol. https://doi.org/10.1007/s10639-025-13550-4
58.   Vygotsky, L. S., & Cole, M. (1978). Mind in society: Development of higher psychological processes. Harvard university press.
59.   Waquar, A., Sujood, Kareem, S., Yasmeen, N., & Hussain, S. (2025). From traditional to virtual classrooms: unravelling themes and shaping the future of metaverse education. Interactive Technology and Smart Education, 22(2), 266–303. https://doi.org/10.1108/ITSE-02-2024-0032
60.   Wertsch, J. V. (1988). Vygotsky and the social formation of mind. Harvard university press.
61.   Yuskovych-Zhukovska, V., Poplavska, T., Diachenko, O., Mishenina, T., Topolnyk, Y., & Gurevych, R. (2022). Application of artificial intelligence in education. Problems and opportunities for sustainable development. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 13(1Sup1), 339–356. https://brain.edusoft.ro/index.php/brain/article/view/1272
62.   Zhan, Z., Tong, Y., Lan, X., & Zhong, B. (2024). A systematic literature review of game-based learning in Artificial Intelligence education. Interactive Learning Environments, 32(3), 1137–1158. https://doi.org/10.1080/10494820.2022.2115077
63.   Zhang, H. L., & Leong, W. Y. (2024). Transforming Rural and Underserved Schools with AI-Powered Education Solutions. ASM Science Journal, 19, 1–12. https://doi.org/10.32802/asmscj.2023.1895
 
دوره 17، شماره 66
تابستان 1405
صفحه 293-315

  • تاریخ دریافت 20 تیر 1404
  • تاریخ بازنگری 13 مرداد 1404
  • تاریخ پذیرش 25 آبان 1404