Geography and Regional Planning

Geography and Regional Planning

Pathology of Challenges and Opportunities in the Use of Artificial Intelligence Systems by Geography Graduate Students in Iran

Document Type : Original Article

Authors
1 Professor, Department of Geography and Urban Planning, Ferdowsi University of Mashhad, Mashhad, Iran
2 Associate Professor, Department of Architecture, Roma Tre University, Rome, Italy
3 Institute of Geo-AI, Zhaoxing University, Zhaoxing, China
4 Department of Political Geography, Ferdowsi University of Mashhad, Mashhad, Iran
10.22034/jgeoq.2025.151140.1628
Abstract
The rapid advancement of artificial intelligence technologies has highlighted the need to rethink educational and research methods in the geographical sciences. This study employs a mixed-methods (quantitative–qualitative) approach to examine the extent of use, opportunities, challenges, and future scenarios associated with AI adoption among graduate students in geography. In the quantitative phase, the statistical population consisted of 180 master’s and doctoral students, from whom 125 valid questionnaires were collected through random sampling. The results showed that 67% of students had used AI software at least once in their academic or research activities; however, the overall mean usage level (3.1 out of 5) indicated a moderate rate of utilization. The highest levels of application were reported in academic writing and text editing (74%) and in spatial and remote-sensing data analysis (59%), whereas only 28% of students had employed AI-based environmental modeling capabilities. Pearson’s correlation test revealed a significant relationship between the level of AI use and academic degree (r = 0.38, p < 0.01). In the qualitative phase, data obtained from 15 semi-structured interviews were subjected to thematic analysis. The key opportunities identified included accelerated processing of spatial big data, enhanced research creativity, and strengthened problem-solving skills. Conversely, the challenges emerged across three major dimensions: insufficient technical and educational skills, ethical concerns and risks of plagiarism, and access limitations arising from sanctions or high costs. The foresight analysis outlined four potential scenarios: “widespread adoption and structured training,” “fragmented and informal use,” “institutional resistance and restriction,” and “localization and development of domestic AI tools.” Overall, the findings indicate that with targeted investment in formal training and the localization of AI technologies, a profound transformation in geographical education and research can be expected.
Keywords

1. امینی، م.، رضایی، الف.، و احمدی، س. (۱۴۰۰). چالش‌های ساختاری به‌کارگیری فناوری‌های نوین در آموزش عالی ایران. فصلنامه پژوهش و برنامه‌ریزی در آموزش عالی، 27 (3)، 68-45.
2. حسینی، م.، و قاسمی، ر. (۱۳۹۹). چالش‌های اخلاقی استفاده از هوش مصنوعی در آموزش عالی ایران: یک مطالعه کیفی. فصلنامه مطالعات آموزش عالی، 12 (1)، 98-77.
3. شورای عالی انقلاب قرهنگی. (1401). سند ملی توسعه هوش مصنوعی جمهوری اسلامی ایران. دبیرخانه شورای عالی انقلاب فرهنگی.
4. موسوی، س.، کریمی، ش.، و شمس، م. (۱۴۰۱). میزان آشنایی و استفاده دانشجویان علوم انسانی از نرم‌افزارهای هوش مصنوعی. مجله پژوهش‌های میان‌رشته‌ای در علوم انسانی، 9 (2)، 118-101.
5. نادری، م.، و کریمی، ع. (۱۴۰۲). کاربرد شبکه‌های عصبی در پیش‌بینی تغییرات کاربری زمین: مطالعه موردی استان اصفهان. مجله جغرافیا و توسعه، 21 (1)، 74-55.
6. Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W. W. Norton & Company.
7. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
8. Goodchild, M. F., & Li, W. (2021). Replication across space and time must be weak in the social and environmental sciences. Proceedings of the National Academy of Sciences, 118(35), 1–3.
9. Holmes, W., Tuomi, I., & Barber, M. (2021). Artificial intelligence in education: Promise and implications for teaching and learning. UNESCO.
10. Heydari, A., Janparvar, M., Hosseinzadeh, R., Safaralizadeh, E. and Bakhtar, S. (2025). From Academia to Policy-Making: Challenges and Opportunities for the Engagement of Geography Graduates in National Developmental Decision-Making in Iran. Human Ecology, 4(11), 1056-1069. doi: 10.22034/he.2025.548895.1143.
11. Heydari, A., & Bakhtar, S. (2018). Analyzing the regional development of Kurdish border cities of Iran using sustainable urban development indices (study area: Kurdistan province). GeoJournal of Tourism and Geosites, 23 (3), 797–807.
12. Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Lang, M., ... & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274.
13. Latour, B. (2005). Reassembling society: An introduction to actor-network-theory. Oxford University Press.
14. Li, X., Chen, G., & Zhang, H. (2019). Deep learning for remote sensing image classification: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 166–177.
15. Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195–204.
16. Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
17. Russell, S., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson.
18. Stokel-Walker, C. (2023). ChatGPT: What can it do and what’s coming next. Nature, 614(7947), 214–215.
19. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal studies. Management Science, 46(2), 186–204.
20. Zhang, L., Zhang, L., & Du, B. (2020). Deep learning for remote sensing data: A technical tutorial on the state of the art. IEEE Geoscience and Remote Sensing Magazine, 8(4), 22–44.
21. UNESCO. (2021). AI and education: Guidance for policymakers. UNESCO Publishing.