Exploration of knowledge graphs to support teaching in curriculum of live foods cultivation within discipline of aquaculture
DOI:
https://doi.org/10.64549/jaai-ii.v1i2.119Keywords:
Live foods cultivation, Artificial intelligence, Knowledge graphs, Teaching reform and innovationAbstract
Live foods cultivation is a core course for the discipline of aquaculture, covering knowledge from multiple discipline such as microbiology, aquatic biology, ecology, and engineering. The traditional teaching model is lacking in addressing the rapid update of knowledge, the abundance of knowledge points, and the increasing individualized learning needs of students. To address these issues, an exploration has been conducted to build a knowledge graph centered on "data-driven, human-machine collaboration, and personalized empowerment" to assist teaching, guided by the main problem of the complex theoretical knowledge existing in the traditional teaching model. This has initiated an innovative approach for AI technology to assist the teaching of live foods cultivation courses, providing a case for AI used in the education of cultivating higher-level agricultural, forestry, and aquatic science. In the future, these knowledge graphs will continue to be evaluated for their effectiveness in real classroom settings by student test scores and feedback surveys.
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Copyright (c) 2026 Shixi Chen, Li Ao, Xueting Xiao, Bin Li, Taishuo Chen
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