The Role of AIGC in Shaping Postgraduate Prompt Engineering Education for Agriculture-Related Universities
Authors
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Shi Yin
*
College of Economics and Management, Hebei Agricultural University, Baoding 071001, China
College of Humanities and Social Sciences, Hebei Agricultural University, Baoding 071001, China
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Nan Yang
College of Economics and Management, Hebei Agricultural University, Baoding 071001, China
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Ningning Jia
College of Humanities and Social Sciences, Hebei Agricultural University, Baoding 071001, China
DOI:
https://doi.org/10.55121/jele.v2i1.1142Abstract
With the deep penetration of Artificial Intelligence Generated Content (AIGC) in agricultural digital transformation and academic research, prompt engineering has become a core capability bridging professional knowledge and AI tools. Adopting methods including literature review, data investigation, and case analysis, this study systematically sorts out the application value of prompt engineering in the agricultural field and the research foundation related to talent cultivation. It then analyzes the current status of postgraduates' prompt engineering competence—such as adoption rate, learning pathways, and application scenarios—through questionnaire surveys, and examines practical application performance via typical case studies. Finally, based on the research results, the core problems are summarized and corresponding solutions are proposed. The findings indicate that postgraduates in agriculture-related universities generally face several challenges in prompt engineering competence: weak theoretical foundations, uneven distribution of educational resources, insufficient practical application skills, inadequate interdisciplinary integration in training, and imperfect evaluation systems. To address these issues, this paper puts forward countermeasures and suggestions including: incorporating prompt engineering into the cultivation of postgraduates' core competencies and strengthening faculty development; building multi-level practical platforms to deepen industry–university–research collaborative education; optimizing curriculum systems to enhance interdisciplinary integration; improving evaluation and incentive mechanisms by establishing a scientific and diversified competence assessment system; and increasing policy support and resource investment to consolidate the educational foundation.
Keywords:
Agriculture-Related Universities, Postgraduates, Prompt Engineering, Competence CultivationReferences
[1] Gibreel, O., Karataş, K., Arpaci, I., 2026. Prompt Engineering Competence, Knowledge Management, and Technology Fit as Drivers of Educational Sustainability through Generative AI. Scientific Reports. DOI: https://doi.org/10.1038/s41598-026-46335-x
[2] Alenezi, F., 2026. The Use of Digital Libraries and Its Relationship to Digital Competence and AI Literacy among Students of the College of Basic Education in Kuwait. Education for Information. 42(2), 157–179. DOI: https://doi.org/10.1177/01678329251397294
[3] Testa, M., Apuzzo, A., Pittaway, L., 2026. AI Literacy Alone Is Not Enough: Student AI Readiness and Career Adaptability in Business and Management Education. The International Journal of Management Education. 24(2), 101394. DOI: https://doi.org/10.1016/j.ijme.2026.101394
[4] Pangestu, F.N.N., Hamidah, I., Widaningsih, L., et al., 2026. A Scoping Literature Review of Prompt Engineering for Bridging Students AI Literacy in Higher Education. Computers and Education: Artificial Intelligence. 10, 100581. DOI: https://doi.org/10.1016/j.caeai.2026.100581
[5] Dolezel, D., Lambert, E., Watzlaf, V., et al., 2026. Empowering Educators: AI Literacy as a Catalyst for Competency-Based Health Information Training. Health Information Management. DOI: https://doi.org/10.1177/18333583261427155
[6] Podder, S., Date, H., Murthy, S., 2026. Green Prompt Engineering for Sustainable Generative AI. Environmental Science and Ecotechnology. 30, 100684. DOI: https://doi.org/10.1016/j.ese.2026.100684
[7] Ding, L., O’Berry, R., Tallent, H., et al., 2026. Learning with Large Language Models: Beyond Prompt Engineering. Education and Information Technologies. 1–25. DOI: https://doi.org/10.1007/s10639-026-13924-2
[8] Ravindran, J.D.S., Skarga-Bandurova, I., V, S., et al., 2026. AgroLLM: Connecting Farmers and Agricultural Practices through Large Language Models for Enhanced Knowledge Transfer and Practical Application. AgriEngineering. 8(1), 38. DOI: https://doi.org/10.3390/agriengineering8010038
[9] Quince, Z., Nikolic, S., 2026. Student Identification of the Social, Economic and Environmental Implications of Using Generative Artificial Intelligence (GenAI): Identifying Student Ethical Awareness of ChatGPT from a Scaffolded Multi-Stage Assessment. European Journal of Engineering Education. 51(1), 43–62. DOI: https://doi.org/10.1080/03043797.2025.2482830
[10] Shi, J., Zhang, Z., Gao, R., et al., 2025. Cultivating Talents at Tertiary Agricultural Institutions in China for Sustainable and Intelligent Development. Sustainability. 17(21), 9754. DOI: https://doi.org/10.3390/su17219754
[11] Murton, M., Boulton, E., Cross, S., et al., 2025. Harnessing Large-Language Models for Efficient Data Extraction in Systematic Reviews: The Role of Prompt Engineering. Cochrane Evidence Synthesis and Methods. 3(6), e70058. DOI: https://doi.org/10.1002/cesm.70058
[12] Zhang, Z., 2025. Framework for a Smart Breeding 4.0 Curriculum: Insights from China and Global Implications. World. 6(4), 139. DOI: https://doi.org/10.3390/world6040139
[13] Qian, Y., 2025. Prompt Engineering in Education: A Systematic Review of Approaches and Educational Applications. Journal of Educational Computing Research. 63(7–8), 1782–1818. DOI: https://doi.org/10.1177/07356331251365189
[14] Wang, D.R., Zhao, Y.X., Jing, Y.T., et al., 2025. Feature Analysis of Prompt Guides Based on Generative Artificial Intelligence and Preliminary Exploration of Prompt Literacy Cultivation Framework. Information Studies: Theory & Application. 48(12), 94–105. (in Chinese)
[15] Chen, J.X., Guo, D.D., Luo, C., et al., 2025. Framework Research on Effective Review Prompts Based on ChatGPT—Taking “BIM Informatization” as an Example. Information Recording Materials. 26(8), 59–61. (in Chinese)
[16] Chen, L., Zhu, S.N., 2025. Incentive Preposition and Gradient Empowerment: Preliminary Conception of Prompt Protection in Generative Artificial Intelligence. Journal of Beijing Technology and Business University (Social Sciences Edition). 40(4), 131–142. (in Chinese)
[17] Shi, J., Feng, Y., Cao, X., et al., 2025. Perceptions toward Artificial Intelligence (AI) among Animal Science Students in Chinese Agricultural Institutions. Sustainability. 17(6), 2427. DOI: https://doi.org/10.3390/su17062427
[18] Lee, D., Palmer, E., 2025. Prompt Engineering in Higher Education: A Systematic Review to Help Inform Curricula. International Journal of Educational Technology in Higher Education. 22(1), 7. DOI: https://doi.org/10.1186/s41239-025-00503-7
[19] Fei, Y., Fan, J., Zhou, G., 2025. Extracting Fruit Disease Knowledge from Research Papers Based on Large Language Models and Prompt Engineering. Applied Sciences. 15(2), 628. DOI: https://doi.org/10.3390/app15020628
[20] Huang, J., 2024. Cultivating Prompt Literacy in University Libraries in the Digital Intelligence Era: Content, Methods and Paths. New Century Library. 12, 47–52. (in Chinese)
[21] Federiakin, D., Molerov, D., Zlatkin-Troitschanskaia, O., et al., 2024. Prompt Engineering as a New 21st Century Skill. Frontiers in Education. 9, 1366434. DOI: https://doi.org/10.3389/feduc.2024.1366434
[22] Zhang, G.X., Jia, J.Z., 2024. Research on the Cultivation of Prompt Literacy in the Era of Generative AI. Journal of Academic Libraries. 42(6), 63–71. (in Chinese)
[23] Knoth, N., Tolzin, A., Janson, A., et al., 2024. AI Literacy and Its Implications for Prompt Engineering Strategies. Computers and Education: Artificial Intelligence. 6, 100225. DOI: https://doi.org/10.1016/j.caeai.2024.100225
[24] Walter, Y., 2024. Embracing the Future of Artificial Intelligence in the Classroom: The Relevance of AI Literacy, Prompt Engineering, and Critical Thinking in Modern Education. International Journal of Educational Technology in Higher Education. 21, 15. DOI: https://doi.org/10.1186/s41239-024-00448-3
