Article(id=1315696350826713336, tenantId=1146029695717560320, journalId=1315121759093096577, issueId=1315696340265455712, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1005-7021.2026.03.011, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1760025600000, receivedDateStr=2025-10-10, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791616054492, onlineDateStr=2026-10-10, pubDate=1781884800000, pubDateStr=2026-06-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791616054492, onlineIssueDateStr=2026-10-10, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791616054492, creator=13701087609, updateTime=1791616054492, updator=13701087609, issue=Issue{id=1315696340265455712, tenantId=1146029695717560320, journalId=1315121759093096577, year='2026', volume='46', issue='3', pageStart='1', pageEnd='136', issueExtLink='null', onlineDate='null', pubDate='1781884800000', pubDateStr='2026-06-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791616051974, creator='13701087609', updateTime=1791616181585, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1315696883952108299, tenantId=1146029695717560320, journalId=1315121759093096577, issueId=1315696340265455712, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1315696883952108300, tenantId=1146029695717560320, journalId=1315121759093096577, issueId=1315696340265455712, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=127, endPage=136, ext={EN=ArticleExt(id=1315696351002874105, articleId=1315696350826713336, tenantId=1146029695717560320, journalId=1315121759093096577, language=EN, title=Construction and Exploration of Teaching System by integrating artificial intelligence into "Food Microbiology", columnId=null, journalTitle=Journal of Microbiology, columnName=null, runingTitle=null, highlight=null, articleAbstract=

With the rapid development of artificial intelligence (AI) technology, the food industry is accelerating its transformation toward intelligence in areas such as quality control and supply chain optimization; however, traditional "Food Microbiology" teaching remains primarily centered on theoretical lectures and basic experiments such as microbial cultivation, counting, and Gram staining. This significant disconnect between teaching content and cutting-edge industry demands constrains the cultivation of interdisciplinary talents. "Food Microbiology" grounded in microbiology, integrates advanced domains from multiple disciplines, including food science, bioinformatics, food chemistry, and fermentation engineering. This article systematically explores reform for AI-empowered undergraduate "Food Microbiology" course instruction. It advocates advancing educational reforms by restructuring course content, innovating teaching models, and enhancing practical training. By incorporating AI foundational theory and frontier case studies, an integrated online-offline teaching resource repository is constructed. Hybrid virtual-physical experimental projects are designed to strengthen students′ ability to utilize AI tools in solving practical problems like fermentation optimization and microbial identification. Industry-academia-research platforms are leveraged to establish an innovation capability cultivation mechanism. Based on a case study at Zhejiang A&F University, this article details the reform plan for AI integration into the food microbiology curriculum, demonstrating concrete applications of AI technology in enhancing instructional content such as food fermentation, metabolic prediction, and food safety early-warning systems. The integration of AI with "Food Microbiology" education will propel a transition from "experience-driven" to "data-driven" pedagogy, thereby providing sustainable talent support for the intelligent advancement of the food industry.

, authors=Guan-yu FANG^{1, *}, Zhen WANG², Xing-quan LIU¹, authorsList=Guan-yu FANG, Zhen WANG, Xing-quan LIU, authorCompany=null, correspAuthors=Guan-yu FANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1315696351313252603, articleId=1315696350826713336, tenantId=1146029695717560320, journalId=1315121759093096577, language=CN, title=人工智能融入“食品微生物学”教学体系构建与探索, columnId=1315696351090954490, journalTitle=微生物学杂志, columnName=教学研究, runingTitle=null, highlight=null, articleAbstract=

随着人工智能（Artificial Intelligence，AI）技术的快速发展，食品工业在质控和供应链优化等领域正加速向智能化转型，但传统“食品微生物学”教学仍以理论讲授和微生物培养、计数、革兰染色等基础实验为主，教学内容与行业前沿需求严重脱节，制约了复合型人才的培养。“食品微生物学”以微生物学为基础，融合食品科学、生物信息学、食品化学、发酵工程等多个学科的前沿领域，本文基于其多学科交叉背景，系统探讨AI技术赋能本科“食品微生物学”课程教学的改革路径，提出从课程内容重构、教学模式创新、实践能力培养等方面推进改革，通过补充AI基础理论与前沿案例，构建线上、线下协同的教学资源库，设计虚实结合实验项目，强化学生利用AI工具解决发酵优化、微生物鉴定等实际问题的能力，依托产学研平台，建立创新能力培养机制。以浙江农林大学为例，详细阐述了AI融入“食品微生物学”课程的改革方案，展示了AI技术在优化食品发酵、代谢预测、食品安全预警等教学内容中的具体应用。AI与“食品微生物学”教学优化的深度融合可推动教学从“经验驱动”向“数据驱动”转变，为食品工业智能化发展提供可持续发展的人才支撑。

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方冠宇　男，副教授，博士，硕士生导师。主要从事食品微生物学教学和科研工作。 E-mail: fangguanyu@zafu.edu.cn

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方冠宇　男，副教授，博士，硕士生导师。主要从事食品微生物学教学和科研工作。 E-mail: fangguanyu@zafu.edu.cn

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