AI大模型融入农科人才培养的理论逻辑与路径设计

Theoretical Logic and Pathway Design for Integrating AI Large Models into Agricultural Talent Cultivation

摘要: 现代农业向“基因化、数字化、绿色化、工程化”转型，对农科人才的知识结构与能力素养提出了系统性重构要求，但传统农科教育在课程体系、实践教学、产教融合与评价机制等方面存在显著的结构性困境。人工智能大模型以其强大的知识融合、复杂问题求解与个性化交互能力，为破解上述困境提供了技术可能。本文首先分析农科人才培养的特殊性与传统教育困境，在此基础上阐明AI大模型融入的内在逻辑：“需求牵引–能力重塑–模式创新–生态协同”的闭环机制；进而设计“一体六翼”的系统化赋能路径，涵盖课程体系重构、教学模式创新、实践平台建设、师资队伍转型、评价机制改革与治理保障六大支柱；最后构建涵盖伦理规范、数据安全与技术依赖风险的综合治理框架。研究认为，AI大模型与农科教育的深度融合，本质上是一场以智能技术为杠杆、以系统重构为特征的教育范式革命，需要顶层设计、多元协同与持续迭代的共同支撑。

Abstract: The ongoing transformation of modern agriculture toward “geneticization, digitalization, greening, and engineering” imposes a systemic restructuring demand on the knowledge structure and competency profile of agricultural talents. However, traditional agricultural education faces significant structural deficiencies in curriculum systems, practical teaching, industry-education integration, and assessment mechanisms. Artificial intelligence large models, with their powerful capabilities in knowledge integration, complex problem solving, and personalized interaction, offer technological opportunities to address these challenges. This paper first analyzes the specificity of agricultural talent cultivation and the traditional educational dilemmas, and on this basis, articulates the intrinsic logic of integrating AI large models: namely, a closed-loop mechanism of demand traction, capability reshaping, model innovation, and ecosystem synergy. It then designs a systematic empowerment pathway with one core and six supporting pillars, covering curriculum restructuring, teaching model innovation, practical platform construction, faculty transformation, evaluation reform, and governance assurance. Finally, it constructs a comprehensive governance framework that addresses ethical norms, data security, and technology dependency risks. The study argues that the deep integration of AI large models into agricultural education is essentially an educational paradigm revolution that leverages intelligent technology and features systematic reconstruction, requiring top-level design, multi-stakeholder collaboration, and continuous iteration as its underpinning supports.