Optimization of University Teaching Management in the Era of Artificial Intelligence

DOI: https://doi.org/10.62517/jhet.202515212

Author(s)

Jun Zhang1, Donghua Yang1, Yizheng Wang1, Huinan Xu2

Affiliation(s)

1Qilu Medical University, Zibo, 255300, China 2Zibo Vocational Institute, Zibo, 255300, China

Abstract

This study explores pathways for optimizing university teaching management to enhance quality and efficiency in the era of artificial intelligence (AI). Utilizing literature review and inductive analysis, we collect and analyze relevant academic literature and policy documents to assess the current applications and challenges of AI in various aspects of university teaching management. A detailed examination reveals how AI influences the formulation of teaching plans, course scheduling, teaching evaluation, and student management. Our rigorous analysis indicates that AI can facilitate precise teaching plan development through data analytics, optimize resource allocation, enable intelligent teaching evaluations, and improve the specificity of student management. However, challenges such as high application costs, varying levels of teacher tech proficiency, and concerns over data security and privacy persist. the study concludes that universities should increase investments in AI technology, enhance teachers' digital literacy, and improve data security frameworks to better integrate AI into teaching management and drive the modernization of university education.

Keywords

Artificial Intelligence; University Teaching Management; Teaching Optimization; Digital Literacy; Data Security

References

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