The Role of AIGC in Shaping Postgraduate Prompt Engineering Education for Agriculture-Related Universities - Yin, Shi; Yang, Nan; Jia, Ningning (Journal of Education and Learning Environments, 2026)
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.