摘要
为探索生成式人工智能(AI)在实验实践教学中的创新应用,本文以普通植物病理学创新实验课程为实践场域,探索AI赋能实验实践教学“师—机—生”协同应用的具体路径。通过案例对比分析与系统设计,构建了涵盖AI互动教学平台、知识引擎、创新设计助手及个性化评估的“四维”应用模式框架,并实施了AI赋能实验教学活动。具体路径包括打造基于AI的互动教学平台,使用DeepSeek R1大模型,经过层层调试,开发了基于Python的植物叶片病害严重度计量软件,准确率达97%;发挥AI知识引擎和创新设计助手功能,开发了涵盖病害调查抽样图示、基因组序列提取、猕猴桃病害图像识别小程序等具体应用场景的小工具;利用AI完善反馈与全过程评估机制,对学生实验完成度、创新作品设计等进行全过程考核,并提供针对性辅导。实践表明,AI辅助教学班级的学生活跃度、学习成绩和创新成果产出均明显增加,且开发的工具被应用于科研实践,可有效提升实验实践教学效果。本文为农科及其他实验性学科的AI赋能教学改革提供参考。
To explore the innovative application of generative artificial intelligence in experimental practice teaching,the Innovative Experimental of General Plant Pathology course was taken as the practical field,and the specific path of AI empowering the collaborative application of“teacher-machine-student”in experimental practice teaching was explored.Through case comparison analysis and system design(AI),a“four-dimensional”application model framework covering AI interactive teaching platform,knowledge engine,innovative design assistant,and personalized evaluation was constructed,and multiple AI empowered experimental teaching activities were implemented.The specific path includes building an AI based interactive teaching platform,use the DeepSeek R1 model,and after layer by layer debugging,developing a Python based software for measuring the severity of plant leaf diseases,with an accuracy rate of 97%;utilize the functions of AI knowledge engine and innovative design assistant,it have developed small tools covering specific application scenarios such as disease investigation sampling diagrams,genome sequence extraction,kiwifruit disease image recognition mini programs,etc.;utilize AI to improve the feedback and full process evaluation mechanism,conduct full process assessment on students’experimental completion,innovative work design additional scores,and provide targeted guidance.Practice has shown that the activity level,academic performance,and innovative output of students in AI assisted teaching classes have significantly increased,and the developed tools have been applied to scientific research practice.It can effectively improve the effectiveness of experimental practice teaching.This article provides references for AI empowered teaching reform in agricultural science and other experimental disciplines.
作者
赵志博
王勇
丁海霞
陈相儒
韦珊
ZHAO Zhibo;WANG Yong;DING Haixia;CHEN Xiangru;WEI Shan(College of Agriculture,Guizhou University,Guiyang 550025,China)
出处
《安徽农学通报》
2025年第14期129-133,共5页
Anhui Agricultural Science Bulletin
基金
贵州省级“金课”一流本科课程(2024JKHH0032)
贵州大学本科教改项目(XJG2023090)
贵州大学首批AI课程建设项目(XJG2024106)。
关键词
生成式人工智能
普通植物病理学
AI赋能教学
生物实验
generative artificial intelligence
General Plant Pathology
AI empowered teaching
biological experiments
