Iranian Agricultural Extension and Education Journal

Iranian Agricultural Extension and Education Journal

Analysis of AI Tool Usage Behavior Among Agricultural Students: An Integrated Approach of Structural Equation Modeling (SEM) and Artificial Neural Network (ANN)

Articles in Press

Document Type : Original Article

Authors
Omid Jamshidi 1
Seyed Mohammad Javad Sobhani 2
1 Department of Agricultural Extension and Education, Faculty of Crop Sciences, Sari Agricultural and Natural Resourses University, Sari, Iran
2 Department of Agricultural Extension and Education, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran
10.22034/iaeej.2026.543688.1870
Abstract
Given the rapid expansion of artificial intelligence (AI) technologies across various domains, identifying the factors that influence students’ adoption and use of these tools has become increasingly important. This study aimed to analyze the usage behavior of AI tools among agricultural students using an integrated approach that combines Structural Equation Modeling (SEM) and Artificial Neural Network (ANN). The research employed a descriptive-survey method, and the study population consisted of students from Sari University of Agricultural Sciences and Natural Resources who had participated in AI-related courses during the 2022–2023 academic years. Using Krejcie and Morgan's table, a sample size of 176 was determined, and data were collected through stratified random sampling with proportional allocation. The main research instrument was a questionnaire, whose validity and reliability were confirmed. Data analysis was conducted using SEM with SmartPLS3 software and ANN analysis to incorporate complementary linear and nonlinear perspectives in explaining user behavior. The findings revealed that all examined variables—social norms, self-efficacy, perceived usefulness, perceived ease of use, and technology access—had a significant positive effect on the usage behavior of AI tools. Among these factors, social norms played the most substantial role in the SEM analysis, indicating that support from peers, instructors, and the university environment were the primary drivers of technology adoption. In contrast, the ANN analysis highlighted the critical importance of self-efficacy, suggesting that students’ belief in their individual capability to use technology is a latent yet highly influential factor. Additionally, the results underscored that without adequate access to technological infrastructure, other motivational factors would be ineffective. Based on the findings, facilitating the adoption and utilization of AI tools in agricultural higher education can be achieved by strengthening a technology-oriented culture within academic environments, enhancing students’ digital self-efficacy, developing technological infrastructure, designing user-friendly tools tailored to the needs of agricultural disciplines, and providing continuous and up-to-date training in AI. 
Keywords
Artificial Intelligenc
Agricultural higher education
Artificial neural network
Structural equation modeling
Technology adoption
حاجی‌انوری، ل؛ و رمضانی، ع. (1403). بررسی وضعیت سواد، کاربست و عوامل مؤثر بر پذیرش هوش مصنوعی در بین اعضای هیئت‌علمی. نامه آموزش عالی، دوره 17، شماره 68، صص 131-106. 10.22034/hel.2024.2036769.1985 /https://doi.org
ثانی‌حیدری، ع.ث؛ و صفری، ا. (1404). بررسی عوامل مؤثر بر قصد رفتاری و رفتار استفاده از فناوری هوش مصنوعی: مطالعه موردی کشت و صنعت‌های شرکت کشاورزی رضوی. مجله اقتصاد کشاورزی و توسعه. دوره 39، شماره 1، صص 56-33. https://doi.org/10.22067/jead.2024.88807.1277
دلجو، ح.، سلیمانی، ت؛ و آزاد، ح. (1403). ارزیابی عوامل مؤثر بر قصد استفاده از هوش مصنوعی در آموزش عالی از منظر دانشجویان. ماهنامه علمی تخصصی پایاشهر، دوره 6، شماره 69، صص 19-1. https://civilica.com/doc/2141462
سبحانی، س. م. ج؛ و تاکی، م. (1404). توسعه مدل پذیرش هوش مصنوعی در نظام آموزش عالی کشاورزی ایران. علوم ترویج و آموزش کشاورزی ایران. دوره 21، شماره 1، صص 64-47. https://doi.org/10.22034/iaeej.2025.505374.1848
عسکر، ف. (1404)، بررسی رابطه بین عوامل مؤثر بر پذیرش ابزارهای آموزشی مبتنی بر هوش مصنوعی و قصد رفتاری معلمان، اولین همایش بین‌المللی هوش مصنوعی در آموزش و پرورش، روانشناسی، علوم تربیتی و مطالعات دینی، فرهنگی، اجتماعی و مدیریتی در هزاره سوم، مؤسسه چندمنظوره بام پژوهش پرواز جنوب، بوشهر.  قابل دسترسی در ادرس اینترنتی: <https://civilica.com/doc/2219295 <
فتحعلی‌بیگی، پ. ابطحی، م.ا.، مقامی، ح؛ و مرادی، ر. (1403). رتبه‌بندی عوامل مؤثر بر کارکرد هوش مصنوعی در بهبود آموزش ضمن خدمت معلمان با استفاده از روش تصمیم‌گیری چندمعیاره. مدیریت هوشمند سرمایه انسانی، دوره 2، شماره 2، صص 151-125.  https://doi.org/10.22034/imhr.2025.506738.1021
 
Adhikari, N., and Gope, L. (2025). Students perception and preference of the application of artificial intelligence (AI) in higher education: A metacognitive exploration. The International Journal of Indian Psychology, 13(1), 207-218. https://doi.org/10.25215/1301.019
Bampasidou, M., Goldgaber, D., Gentimis, T., and Mandalika, A. (2024). Overcoming ‘Digital Divides’: Leveraging higher education to develop next generation digital agriculture professionals. Computers and Electronics in Agriculture, 224, 109181. https://doi.org/10.1016/j.compag.2024.109181
Elareshi, M., Habes, M., Youssef, E., Salloum, S. A., Alfaisal, R., and Ziani, A. (2022). SEM-ANN-based approach to understanding students’ academic-performance adoption of YouTube for learning during Covid. Heliyon, 8(4), e09236. https://doi.org/10.1016/j.heliyon.2022.e09236
Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. (2022). A primer on partial least squares struc­tural equation modeling (PLS-SEM). 3rd Edition. Dublin, Irland: Sage.
Hair, J. F., Risher, J. J., Sarstedt, M., and Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203.
Hasan, H. E., Jaber, D., Al Tabbah, S., Lawand, N., Habib, H. A., and Farahat, N. M. (2024). Knowledge, attitude and practice among pharmacy students and faculty members towards artificial intelligence in pharmacy practice: A multinational cross-sectional study. Plos One, 19(3), e0336818, 1-24. https://doi.org/10.1371/journal.pone.0296884
Henseler, J., Ringle, C. M., and Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
Holmes, W., Bialik, M., and Fadel, C. (2021). Artificial intelligence in education: Promises and implications for teaching and learning. Boston: Center for Curriculum Redesign. https://curriculumredesign.org
Jin, S. H. (2025). Measures of learner-generative AI relationships. Computers and Education Open, (8), e100258, https://doi.org/10.1016/j.caeo.2025.100258
Kharroubi, S. A., Tannir, I., Abu El Hassan, R., and Ballout, R. (2024). Knowledge, attitude, and practices toward artificial intelligence among university students in Lebanon. Education Sciences, 14(8), 863.
Li, R., Ouyang, J., Lin, J., and Ouyang, S. (2025). Mediating effect of AI attitudes and AI literacy on the relationship between career self-efficacy and job-seeking anxiety. BMC Psychology, 15(1), e2757. https://doi.org/10.1186/s40359-025-02757-2
Liébana-Cabanillas, F., Marinkovic, V., De Luna, I. R., and Kalinic, Z. (2018). Predicting the determinants of mobile payment acceptance: A hybrid SEM-neural network approach. Technological Forecasting and Social Change, 129, 117-130. https://doi.org/10.1016/j.techfore.2017.12.015
Luckin, R., Holmes, W., Griffiths, M., and Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. London, United Kingdom: Pearson. https://www.pearson.com
OECD. (2023). Digital education outlook 2023: AI and the future of learning. OECD Publishing, Paris, France. https://doi.org/10.1787/19963712
Pallottino, F., Violino, S., Figorilli, S., Pane, C., Aguzzi, J., Colle, G., Nemmi, E. N., Montaghi, A., Chatzievangelou, D., Antonucci, F., Moscovini, L., Mei, A., Costa, C., and Ortenzi, L. (2025). Applications and perspectives of Generative Artificial Intelligence in agriculture. Computers and Electronics in Agriculture, 230, 109919. https://doi.org/10.1016/j.compag.2025.109919
Rane, J., Kaya, Ö., Mallick, S. K., and Rane, N. L. (2024). Generative Artificial Intelligence in agriculture, education, and business. Bengaluru, India: Deep Science Publishing. https://doi.org/10.70593/978-81-981271-7-4
Richter, N. F., and Tudoran, A. A. (2024). Elevating theoretical insight and predictive accuracy in business research: Combining PLS-SEM and selected machine learning algorithms. Journal of Business Research, 173, 114453. https://doi.org/10.1016/j.jbusres.2023.114453
Ringle, C. M., and Sarstedt, M. (2016). Gain more insight from your PLS-SEM results: The importance-performance map analysis. Industrial Management & Data Systems, 116(9), 1865-1886. https://doi.org/10.1108/IMDS-10-2015-0449
Salameh, B., Qaddumi, J., and Hammad, B. (2025). Nursing students' attitudes toward artificial intelligence: Palestinian perspectives. SAGE Open, 15(1), Article 23779608251343297. https://doi.org/10.1177/23779608251343297
Salmon, G. (2019). Learning innovation: A framework for transformation. European Journal of Open, Distance and E-learning, 22(2), 32–41.
Sergeeva, O. V., Masalimova, A. R., Zheltukhina, M. R., Chikileva, L. S., Lutskovskai, L. Y., and Luzin, A. (2025). Impact of digital media literacy on attitude toward generative AI acceptance in higher education. Frontiers in Education, 10, e1563148. https://doi.org/10.3389/feduc.2025.1563148
Shabanian, H., Pourghasemi, M., and Devi, S. (2025). Preliminary findings toward Inclusive AI education: Insights from CS and non-CS students. In Proceedings of the 2025 ACM Southeast Conference (PP. 295-296). Cape Girardeam, Missouri, USA, (24-26 April, 2025).  https://doi.org/10.1145/3696673.3723091
Son, J. B., Ružić, N. K., and Philpott, A. (2025). Artificial intelligence technologies and applications for language learning and teaching. Journal of China Computer-Assisted Language Learning, 5(1), 94-112. https://doi.org/10.1515/jccall-2023-0015
Spanaki, K., Sivarajah, U., Fakhimi, M., Despoudi, S., and Irani, Z. (2022). Disruptive technologies in agricultural operations: A systematic review of AI-driven AgriTech research. Annals of Operations Research, 308(1), 491-524. https://doi.org/10.1007/s10479-020-03922-z
Waqas, M., Hania, A., and Chunyan, X. U. (2025). Understanding AIgiarism in higher education: The lens of general AI attitudes and moral disengagement. Studies in Higher Education,13(454), 1-17. https://doi.org/10.1080/03075079.2025.2497479
Xu, S., Wang, Y., and Luo, W. (2024). Hybrid SEM-ANN model for predicting undergraduates’e-learning continuance intention based on perceived educational and emotional support. PloS One, 19(12), e0308630. https://doi.org/10.1371/journal.pone.0308630
Yasin, M. I. (2022). Youth perceptions and attitudes about artificial intelligence. Izvestiya of Saratov University. Philosophy. Psychology. Pedagogy, 22(2), 197–205. https://www.researchgate.net/publication/361705403
Zawacki-Richter, O., Marín, V. I., Bond, M., and Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education: Challenges and opportunities. International Journal of Educational Technology in Higher Education, 16(1), 1-27. https://doi.org/10.1186/s41239-019-0171-0
Iranian Agricultural Extension and Education Journal

Articles in Press, Accepted Manuscript
Available Online from 25 February 2026