摘要
生成式人工智能的兴起,为历史研究带来了新的工具与方法,也引发了关于学科边界与研究范式的再思考。在林业史这一高度依赖材料整合、时空建构与跨学科联动的研究领域,AI的语言处理能力与结构生成能力显现出高度契合。然而回看实际应用路径,却发现多停留于辅助层面,难以深入知识构建与解释环节。林业史研究所面临的材料分散、逻辑断裂与因果梳理困难,恰是AI介入的关键场域,也因此暴露出生成模型尚未形成稳定机制的现实缺口。问题不在技术能力之不足,而在于结构路径的未被确立。从材料处理到结构建构再到历史解释,唯有在人的主导下建立清晰分工与协同机制,才能使生成模型真正嵌入史学工作流程,成为推动林业史研究深入发展的有效力量。
The rise of generative artificial intelligence has introduced new tools and methods to historical research and has also inspired a rethinking of disciplinary boundaries and research paradigms.In the field of forestry history,which heavily relies on material integration,spatial-temporal construction,and interdisciplinary collaboration,the language processing capabilities and structural generation abilities of AI show a high degree of alignment.However,looking back at the practical application,it is found that most applications remain at a supportive level,making it difficult to delve into knowledge construction and interpretation.The challenges confronting forestry history research-fragmented archival materials,discontinuous logical narratives,and difficulties in causal analysis-precisely constitute the critical domain for AI intervention,while simultaneously exposing the current limitations of generative models in establishing stable mechanistic frameworks.The problem does not lie in the lack of technical capabilities,but rather in the absence of an established structural path.From material handling to structural construction and then to historical interpretation,it is only through the establishment of clear divisions of labor and collaborative mechanisms under human guidance that generative models can truly be integrated into the historical research workflow,becoming an effective force in promoting the in-depth development of forestry history research.
作者
闫德宇
YAN Deyu(Advanced Institute for Confucian Studies,Shandong University)
出处
《南京林业大学学报(人文社会科学版)》
2025年第3期108-118,共11页
Journal of Nanjing Forestry University(Humanities and Social Sciences Edition)
基金
国家社会科学基金重大项目“多卷本《20世纪中国史学通史》”(17ZDA196)。
关键词
中国林业史
生成式人工智能
范式转型
知识生成
史料处理
Chinese forestry history
generative artificial intelligence
paradigm shift
knowledge generation
historical data processing
