Conference paper Gold Open Access 2025

The Design of Personalized Cultivation Program for Enterprise Digital Talents Based on Smart Learning Platform

Frontiers in Artificial Intelligence and Applications
Conference · Vol. 405 · pp. 269-277
Abstract

In order to solve the problem of low efficiency and quality of enterprise software engineering talent cultivation, the design of personalized cultivation program for enterprise digital talents based on intelligent learning platform is proposed. The design adopts a layered design method, combined with artificial intelligence algorithms, and develops a platform containing functional modules such as course management, student learning tracking, interaction and feedback, data analysis and reporting. In order to verify the effectiveness and performance of the intelligent platform, professional testing software is used to test the functionality and performance of the platform and the traditional platform. The experimental results show that the functional coverage of the intelligentized platform reaches 98.0% (higher than the 92.0% of the traditional platform), and the error discovery rate is 0.5% (lower than the 2.3% of the traditional platform), which indicates that it has higher stability and reliability. In terms of test pass rate, the platform is 99.5% compared to 96.0% for traditional platforms, reflecting the high usability of the platform. In the performance test, under the simulated scenario of 1, 000 concurrent users, the average response time of the platform is 1.2s, while the traditional platform is 2.5s, reflecting the significant performance advantage of the platform. In terms of system throughput, the platform can handle 1500 requests per second, which is higher than the traditional platform. In terms of resource utilization, the utilization rates of the CPU and memory of the platform under high load are 70.0% and 65.0% respectively, while those of the traditional platform are as high as 85.0% and 80.0% respectively, indicating that the platform is more efficient in resource management. Conclusion: The intelligent platform outperforms the traditional platform in terms of function coverage, error detection rate, system throughput and other key indexes, which verifies its effectiveness and performance advantages in the modern education environment. © 2025 The Authors.

Keywords

Author Keywords

Software Engineering functional modules machine learning algorithms talent training data analysis

Index Keywords

E-learning Learning systems Curricula Engineering education Information management Natural resources management Machine learning Computer aided instruction Learning algorithms Performance Intelligent platform Software testing Learning platform Machine learning algorithms Enterprise software Information analysis Engineering talent Functional modules System throughput Talent cultivations Talent training Talent trainings Data reduction
Author Affiliations
Ltd., Nanning, Guangxi, China
Funding & Acknowledgements
No funding information
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