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Exploration on the Application of Generative AI Combined with Intelligent Graph in the Teaching of "Food Microbiology" Course
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Wen-wen ZHOU1, *, Xiang-wei ZHANG1, Xin-yi FAN1, Kui SU2, Ya XIE1, Da-bing SUN3
Journal of Microbiology | 2026, 46(4) : 136 - 144
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Journal of Microbiology | 2026, 46(4): 136-144
Exploration on the Application of Generative AI Combined with Intelligent Graph in the Teaching of "Food Microbiology" Course
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Wen-wen ZHOU1, *, Xiang-wei ZHANG1, Xin-yi FAN1, Kui SU2, Ya XIE1, Da-bing SUN3
Affiliations
  • 1.College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058
  • 2.School of Computer and Computational Sciences, Hangzhou City University, Hangzhou 310015
  • 3.Shanghai Able Digital Science & Tech Co., Ltd., Hangzhou 311000
Published: 2026-08-20 doi: 10.3969/j.issn.1005-7021.2026.04.012
Outline
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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.

generative AI  /  intelligent graph  /  Food Microbiology  /  teaching reform  /  digital teaching
周文文, 张湘薇, 范欣怡, 苏奎, 谢亚, 孙大兵. 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. 微生物学杂志, 2026 , 46 (4) : 136 -144 . DOI: 10.3969/j.issn.1005-7021.2026.04.012
Wen-wen ZHOU, Xiang-wei ZHANG, Xin-yi FAN, Kui SU, Ya XIE, Da-bing SUN. Exploration on the Application of Generative AI Combined with Intelligent Graph in the Teaching of "Food Microbiology" Course[J]. Journal of Microbiology, 2026 , 46 (4) : 136 -144 . DOI: 10.3969/j.issn.1005-7021.2026.04.012
Year 2026 volume 46 Issue 4
102
17
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Article Info
doi: 10.3969/j.issn.1005-7021.2026.04.012
  • Receive Date:2026-07-08
  • Online Date:2026-10-10
  • Published:2026-08-20
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  • Received:2026-07-08
Funding
Affiliations
    1.College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058
    2.School of Computer and Computational Sciences, Hangzhou City University, Hangzhou 310015
    3.Shanghai Able Digital Science & Tech Co., Ltd., Hangzhou 311000
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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