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Exploration and Practice of Teaching Reform in the Course of Introduction to Artificial Intelligence Based on Knowledge Graph

Author(s)

Haifei Zhang*, Fen Zhao, Yuzhou Zhang

Affiliation(s)

College of information engineering, Nanjing Xiaozhuang University, Nanjing, Jiangsu, China *Corresponding Author

Abstract

The current course "Introduction to Artificial Intelligence" is facing problems such as knowledge connections being fragmented by chapters, significant differences in starting points among students from multiple majors, experimental bias towards verification, and a lack of process data in the final evaluation. This article takes the curriculum knowledge graph as the core to promote teaching reform. The graph is constructed according to four levels of "course knowledge unit knowledge domain knowledge point", defining five types of relationships: predecessor, successor, inclusion, intersection, and application, and linking resources such as courseware, code, and datasets to knowledge points. Teaching is divided into three stages: pre class guidance, in class intensive lectures, and post class expansion. Graph plays a role in navigation and recording learning behavior. Evaluation is based on node mastery, combined with teacher evaluation, student self-evaluation, and group peer evaluation. The plan will be implemented in the second semester of the 2025-2026 academic year at the School of Information Engineering of Nanjing Xiaozhuang University, with 196 students enrolled. The results showed that teaching, practice, and diagnosis share the same knowledge structure, and classroom time has shifted from being evenly distributed to being allocated based on error rates. Teacher intervention can be targeted to specific knowledge points. This model does not rely on commercial platforms and can be used as a reference for structured teaching transformation of similar courses.

Keywords

Introduction to Artificial Intelligence; Course Knowledge Graph; Learning Path; Process Evaluation; Teaching Reform

References

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