Article Details

Abstract

In the era of digital intelligence, new requirements have been put forward for the comprehensive quality evaluation of higher education. Based on the background of information science experimental teaching, an experimental teaching information management platform integrating Artificial Intelligence technology has been constructed, aiming to achieve multi-dimensional, process-oriented and scientific teaching evaluation. The paper firstly analyzes the problems of low management efficiency in current experimental teaching, and proposes a front-end and back-end separation platform based on Vue and Spring Boot frameworks. This platform integrates DeepSeek AI intelligent Q&A, SimHash text plagiarism detection, automated report generation, and Echarts data visualization, supporting full process digital management of pre experiment preparation, experiment process monitoring, and post experiment evaluation feedback. This platform not only improves the efficiency and standardization of teaching process management, but also achieves personalized learning support and scientific evaluation through AI technology, providing certain reference significance for the digital transformation of experimental teaching in universities.

Keywords

digital-intelligent era; experimental teaching; AI Q&A; full process

References

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Funding

2024 School level Experimental Technology Research Project: Design and Implementation of Computer Course Experiment Information Management Platform in the Digital Intelligence Era (SY202402); 2024 School level Teaching Reform Research Project: Research on AI Immersive Practical Teaching Mode (2024Z03); 2024 Hubei Province New Engineering Construction Project: Construction of New Engineering Practice Base "Digital Twin Innovation Practice Talent Training Base" (XGK04069)

About the authors

Li Ruyan (1988.01-), female, Han nationality, from Pingdingshan, Henan Province, associate professor, master's student, research direction: embedded design and system integration; Xilia (1974.12-), female, Han ethnicity, from Wuhan, Hubei, professor, master's student, research direction: network and network security; Corresponding author: Li Yan (April 1985-), female, Han ethnicity, Hubei Xiaorenren, lecturer, master's student, research direction: intelligent software engineering; Yang Wei (1981.03-), male, Han ethnicity, from Tianmen, Hubei, is a senior laboratory technician and a master's student. His research interests include intelligent information processing and embedded systems.