The Role of Artificial Intelligence in Improving 21st-Century Competencies of Agricultural Students: Literature Review
Keywords:
Artificial Intelligence, Agricultural Education, Higher Education, 21st Century Skills, Digital LiteracyAbstract
The rapid development of Artificial Intelligence (AI) has driven significant transformations in higher education, including in the field of agriculture, which faces complex challenges such as climate change, food security, and the growing demand for data-driven agricultural systems. Agricultural students are required not only to master technical knowledge but also to develop 21st-century competencies, including critical thinking, creativity, collaboration, communication, and digital literacy. However, comprehensive studies that systematically examine the role of AI in enhancing these competencies within the context of agricultural higher education remain limited and fragmented. Therefore, this study aims to systematically review the role of Artificial Intelligence in improving 21st-century competencies among agricultural students through a literature review approach. The study adopts the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines by conducting a structured literature search across Scopus, Web of Science, ERIC, ScienceDirect, and Google Scholar. Following the selection process, 35 relevant articles were included and analyzed using thematic analysis. The findings indicate that AI technologies, such as machine learning, intelligent tutoring systems, virtual laboratories, and decision support systems, significantly contribute to the enhancement of critical thinking, problem-solving skills, and digital and data literacy among agricultural students. However, the development of creativity, collaboration, and communication skills largely depends on pedagogical designs that actively integrate AI into project-based and collaborative learning environments. In conclusion, AI should be positioned as a pedagogical enabler that supports human-centered learning to prepare agricultural graduates who are adaptive, competent, and ready to address contemporary agricultural challenges.
References
Ajith, S., Vijayakumar, S., & Elakkiya, N. (2025). Yield prediction, pest and disease diagnosis, soil fertility mapping, precision irrigation scheduling, and food quality assessment using machine learning and deep learning algorithms. Discover Food, 5. https://doi.org/10.1007/s44187-025-00338-1.
Akintuyi, O. (2024). Adaptive AI in precision agriculture: A review: Investigating the use of self-learning algorithms in optimizing farm operations based on real-time data. Open Access Research Journal of Multidisciplinary Studies. https://doi.org/10.53022/oarjms.2024.7.2.0023.
Ali, Z., Muhammad, A., Lee, N., Waqar, M., & Lee, S. (2025). Artificial Intelligence for Sustainable Agriculture: A Comprehensive Review of AI-Driven Technologies in Crop Production. Sustainability. https://doi.org/10.3390/su17052281.
Basche, A., Krupek, F., Chatterjee, N., & Speth, C. (2021). Farm simulation platforms increase undergraduate skills and confidence in decision‐support technologies. Natural Sciences Education. https://doi.org/10.1002/nse2.20058.
Berliana, M., Wardani, D. K., Rahmaningtyas, A., Salqaura, S. S., Fauziah, I., Tarigan, R. A., ... & Safitri, S. A. (2025). Pengantar Ilmu Pertanian: Konsep, Praktik, dan Perkembangan Terkini. Penerbit Mifandi Mandiri Digital, 1(02).
Çelik, I., Gedrimiene, E., Siklander, S., & Muukkonen, H. (2024). The affordances of artificial intelligence-based tools for supporting 21st-century skills:. Australasian Journal of Educational Technology. https://doi.org/10.14742/ajet.9069.
Cob-Parro, A., Lalangui, Y., & Lazcano, R. (2024). Fostering Agricultural Transformation through AI: An Open-Source AI Architecture Exploiting the MLOps Paradigm. Agronomy. https://doi.org/10.3390/agronomy14020259.
D, D. (2025). AI-Powered Crop Yield Prediction System Using Multi-Model ML & Real-Time Agricultural Data. International Journal for Research in Applied Science and Engineering Technology. https://doi.org/10.22214/ijraset.2025.73996.
Dawam, A., & Syaidah, K. (2025). Mengintegrasikan Teknologi Digital dan Artificial Intelligence (AI) Untuk Pengalaman Belajar Mahasiswa Yang Lebih Baik. In Prosiding Seminar Nasional Fakultas Ilmu Tarbiyah dan Keguruan UIN Syarif Hidayatullah Jakarta (Vol. 2, No. 1, pp. 235-252).
Espinosa-Aguilar, M., Loayza-Romero, C., Romero-Herrera, D., & González-Romero, D. (2025). Inteligencia artificial en la educación agrícola: un análisis de los modelos de aprendizaje personalizado. Multidisciplinary Latin American Journal (MLAJ). https://doi.org/10.62131/mlaj-v3-n1-021.
Fahrurrozi, M., & SE, M. (2024). Trend And Innovation In Economics Education Analysis and Implication. PT. Literasi Nusantara Abadi Grup.
Habiba Ulfahyana, & Sape, H. (2024). Penggunaan Media dalam Pembelajaran Matematika: Literature Review. Jurnal Penalaran Dan Riset Matematika, 3(1), 39–52. https://doi.org/10.62388/prisma.v3i1.432
Hadi, Z., & Ali, M. (2025). Analisis Dampak Negatif Pengunaan Artificial Intelligence dalam Pendidikan. Jurnal Ilmu Pendidikan & Sosial (Sinova), 3(02), 165-174.
Hikmatiar, H., Sya’bania, N., Jayadin, R. A., Kasman, R. A., Imranah, I., Sahlan, S., & Saputra, S. (2024). The Effectiveness of Chatgpt in Completing Astronomy Lectures: Building Awareness of Its Use. Jurnal Pendidikan Fisika, 12(2), 121-130.
Hoseinzadeh, S., & Garcia, D. (2024). Ai-driven innovations in greenhouse agriculture: Reanalysis of sustainability and energy efficiency impacts. Energy Conversion and Management: X. https://doi.org/10.1016/j.ecmx.2024.100701.
Kabudi, T., Pappas, I., & Olsen, D. (2021). AI-enabled adaptive learning systems: A systematic mapping of the literature. Comput. Educ. Artif. Intell., 2, 100017. https://doi.org/10.1016/j.caeai.2021.100017.
Kasman, R. A. (2025). The Utilisation Of Artificial Intelligence, Internet Of Things, And Virtual/Augmented Reality In Enhancing The Effectivenessand Efficiency Of Learning: A Literature Review. Indonesian Journal of Education (INJOE), 4(3), 434-443.
Kasman, R. A., & Fuad, M. (2025). Systematic Literature Review: Implementation of STEM Integrated PBL Model in Improving 21st Century Skills. Cerdas: Jurnal Ilmu Pendidikan Sosial dan Humaniora, 1(1), 1-9.
Kasman, R. A., Fuad, M., & Ilham, M. (2025). Development of a Chemistry Teaching Module on Chemical Bonding Based on STEM Integrated with Artificial Intelligence (AI) to Improve Students’ Critical Thinking Skills. Jurnal Penelitian Pendidikan IPA, 11(12), 967-977.
Kasman, R. A., & HB, A. M. (2025). Peran dan Tantangan Kecerdasan Buatan (AI) dalam Pendidikan Tinggi: Implementasi dan Implikasi Etis. Jurnal Pendidikan dan Pembelajaran, 5(1), 24-33.
Kasman, R. A., & Judijanto, L. (2024). Artificial intelligence in the classroom: Innovation or disruption in education. Indonesian Journal of Education (INJOE), 4(1), 844-856.
Kasman, R. A., & Sape, H. (2023). Meta-Analisis: Literasi Digital Pada Pembelajaran SMA. Jurnal Pendidikan Dan Pembelajaran, 3(2), 102-106.
Lengyel, P., Felvégi, E., & Füzesi, I. (2025). Integrating Artificial Intelligence in agricultural higher education: Transforming learning and research. Journal of Agricultural Informatics. https://doi.org/10.17700/jai.2024.15.2.738.
Linaza, M., Posada, J., Bund, J., Eisert, P., Quartulli, M., Döllner, J., Pagani, A., Olaizola, I., Barriguinha, A., Moysiadis, T., & Lucat, L. (2021). Data-Driven Artificial Intelligence Applications for Sustainable Precision Agriculture. Agronomy. https://doi.org/10.3390/agronomy11061227.
Mazari, N. (2025). Building Metacognitive Skills Using AI Tools to Help Higher Education Students Reflect on Their Learning Process. RHS-Revista Humanismo y Sociedad. https://doi.org/10.22209/rhs.v13n1a04.
Ng, D., Leung, J., Su, J., Ng, R., & Chu, S. (2023). Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. Educational Technology Research and Development, 71, 137 - 161. https://doi.org/10.1007/s11423-023-10203-6.
Putri, I. A., & Ikhsan, J. (2025). Pemanfaatan Artificial Intelligence dalam Pengembangan Media Pembelajaran untuk Meningkatkan Minat Belajar Peserta Didik. Journal of Learning Technology and Innovation, 1(1), 16-30.
Quintero, R. (2025). AI-Enhanced Think-Pair-Share: A Learning Analytics Approach to Foster Linguistic Creative Thinking and Collaborative Learning. J. Learn. Anal., 12, 19-34. https://doi.org/10.18608/jla.2025.8807.
Rachmawati, R. R. (2020). Smart Farming 4.0 untuk mewujudkan pertanian Indonesia maju, mandiri, dan modern. In Forum Penelitian Agro Ekonomi (Vol. 38, No. 2, pp. 137-154).
Sape, H., Muh. Ridwan, & Muhammad Fuad. (2025). Pengaruh Penggunaan Artificial Inteligence (AI) dalam Pembelajaran Interaktif untuk Meningkatkan Literasi Numerasi Mahasiswa. Jurnal Pendidikan Dan Pembelajaran, 5(1), 17–23. https://doi.org/10.62388/jpdp.v5i1.521
Spyrou, O., Ariza-Sentís, M., & Vélez, S. (2025). Enhancing Education in Agriculture via XR-Based Digital Twins: A Novel Approach for the Next Generation. Applied System Innovation. https://doi.org/10.3390/asi8020038.
Tahir, M., , Q., & Latif, A. (2025). Explore the role of Artificial Intelligence on Students’ Critical Thinking and Problem-Solving Skills at higher Education Institution in Pakistan. Social Science Review Archives. https://doi.org/10.70670/sra.v3i3.1084.
Wei, H., Dai, Y., Yuan, K., Li, K., Hung, K., Hu, E., Lee, A., Chang, J., Zhang, C., & Li, X. (2025). AI-Powered Problem- and Case-based Learning in Medical and Dental Education: A Systematic Review and Meta-analysis. International Dental Journal, 75. https://doi.org/10.1016/j.identj.2025.100858.
Xu, Z., Choudhury, F., , S., Murphrey, T., & Dooley, K. (2025). Leveraging Learning Analytics to Model Student Engagement in Graduate Statistics: A Problem-Based Learning Approach in Agricultural Education. Behavioral Sciences, 15. https://doi.org/10.3390/bs15101360.
