Project-Based Teaching of Matrix Operations in Linear Algebra: Insights from AI Frontier Applications

DOI: https://doi.org/10.62517/jhet.202515524

Author(s)

Yan Liang, Simin Wu*

Affiliation(s)

Abstract

With the rapid advancement of Artificial Intelligence (AI) technologies, matrices and their operations, which are core topics in linear algebra, play a pivotal role in AI frontiers. However, in current linear algebra course, the teaching of matrix operations remains largely centered on abstract theoretical explanations and traditional applications, with insufficient integration of cutting-edge AI technologies. This gap weakens students’ perception of the connection between theory and real-world applications. This study examines the principles of basic matrix operations and their representative AI application scenarios, selecting two typical cases for analysis: matrix operations in basic image processing and matrix factorization in recommender systems. For the image processing case, a practical project-based instructional design are proposed, as well as its analysis. The results demonstrate that integrating frontier AI applications into linear algebra teaching yields positive teaching and learning outcomes, offering valuable insights for innovating teaching models and enriching instructional practices.

Keywords

AI Frontier Application Cases; Teaching Innovation; Matrix Operations; Problem-Chain Teaching Design

References

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