Article(id=1315696196300161916, tenantId=1146029695717560320, journalId=1315121759093096577, issueId=1315696195121558078, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1005-7021.2026.04.012, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1783440000000, receivedDateStr=2026-07-08, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791616017650, onlineDateStr=2026-10-10, pubDate=1787155200000, pubDateStr=2026-08-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791616017650, onlineIssueDateStr=2026-10-10, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791616017650, creator=13701087609, updateTime=1791616017650, updator=13701087609, issue=Issue{id=1315696195121558078, tenantId=1146029695717560320, journalId=1315121759093096577, year='2026', volume='46', issue='4', pageStart='1', pageEnd='144', issueExtLink='null', onlineDate='null', pubDate='1787155200000', pubDateStr='2026-08-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1791616017369, creator='13701087609', updateTime=1791616161314, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1315696798950339227, tenantId=1146029695717560320, journalId=1315121759093096577, issueId=1315696195121558078, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1315696798950339228, tenantId=1146029695717560320, journalId=1315121759093096577, issueId=1315696195121558078, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=136, endPage=144, ext={EN=ArticleExt(id=1315696196467934077, articleId=1315696196300161916, tenantId=1146029695717560320, journalId=1315121759093096577, language=EN, title=Exploration on the Application of Generative AI Combined with Intelligent Graph in the Teaching of "Food Microbiology" Course, columnId=null, journalTitle=Journal of Microbiology, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Food Microbiology is a comprehensive professional foundation course that integrates basic microbiological knowledge, such as microbial morphology, metabolism, growth control, and genetic variation, with applied aspects like food spoilage, food poisoning, and microbial food production. However, traditional teaching methods face challenges which knowledge points are scattered and the internal connections are unclear, teaching according to individual aptitude is difficult to implement and coherence is lacked for theoretical and practical teaching. The deep integration of generative AI and intelligent knowledge graphs into classroom teaching is proposed in this paper. The curriculum system is constructed by relying on intelligent knowledge graphs and the teaching content is organized by structurally to illustrate the connections between knowledge points. The intelligent auxiliary teaching is applied by using generative AI agents which have increasing engagement and achieving the integration of teaching and assessment. Using experimental AI agents, the experimental difficulties are assisted for students to understand while the logical organization of teaching content is optimized, and thereby teaching efficiency and learning effectiveness are enhanced. Teaching practice indicates that not only students' learning efficiency and knowledge mastery are significantly improved but also precise education from teachers is implemented by using generative AI integrated with knowledge graphs, and thereby the digital transformation of courses is promoted.

, authors=Wen-wen ZHOU^{1, *}, Xiang-wei ZHANG¹, Xin-yi FAN¹, Kui SU², Ya XIE¹, Da-bing SUN³, authorsList=Wen-wen ZHOU, Xiang-wei ZHANG, Xin-yi FAN, Kui SU, Ya XIE, Da-bing SUN, authorCompany=null, correspAuthors=Wen-wen ZHOU, 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=1315696196765729663, articleId=1315696196300161916, tenantId=1146029695717560320, journalId=1315121759093096577, language=CN, title=生成式AI结合智能化图谱在“食品微生物学”课程教学中的应用探索, columnId=1315696196572791678, journalTitle=微生物学杂志, columnName=教学与研究, runingTitle=null, highlight=null, articleAbstract=

“食品微生物学”涵盖了微生物形态、代谢、生长控制、遗传变异等基础微生物学理论知识以及食品腐败、食物中毒、食品微生物制造等微生物应用方面的内容，是一门集基础知识教学与专业知识应用为一体的综合性专业基础课程。传统教学方式面临着知识点分散且内在联系不清、难以因材施教、理论与实践教学缺乏连贯性等问题。本文将生成式人工智能/生成式AI（Generative artificial intelligence, GAI）与智能化图谱（Intelligent graph, IG）深度融合应用于课堂教学，并依托智能化图谱构建课程体系，对教学内容进行结构化梳理，以展示各个知识点之间的联系；借助GAI开展智能化辅助教学，增加教学趣味性，实现教评融合；利用实验AI智能体，辅助学生理解实验难点，同时优化教学内容的组织逻辑，提升教学效率和学习效果。教学实践结果表明，GAI与IG的融合应用，不仅能够显著地提升学生的学习效率和知识掌握程度，还实现了教师的精准教学，从而推动食品微生物学课程的数字化转型。

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周文文　女，副教授，博士，博士生导师。研究方向为食品微生物与生物技术。Tel：0571-88981934，E-mail: vivianzhou11@zju.edu.cn

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周文文　女，副教授，博士，博士生导师。研究方向为食品微生物与生物技术。Tel：0571-88981934，E-mail: vivianzhou11@zju.edu.cn

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