Work

Exploration of knowledge graphs to support teaching in curriculum of live foods cultivation within discipline of aquaculture

Year: 2026

Type: article

Abstract: 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.

Source: Journal of Applied AI and Interdisciplinary Innovations

DOI: 10.64549/jaai-ii.v1i2.119

Authors: Shixi Chen, Li Ao, Xueting Xiao, Bin Li, Taishuo Chen

Institution: Neijiang Normal University

Language: English

Topic: Artificial Intelligence in Education

Subfield: Education

Field: Social Sciences

Domain: Social Sciences

SDGs: Quality education, Zero hunger

Open Access status: hybrid

Funder: Neijiang Normal University