Exploration and Research on the “AI +” Talent Cultivation Model for Computer Software Engineering majors in Higher Education Institutions
Abstract
Against the backdrop of the advancement of the national “AI +” strategy and the intelligent upgrade of the software industry, enterprises have an increasingly urgent demand for compound talents who “understand development and are intelligent”, but the traditional training model of software engineering in colleges and universities has obvious shortcomings and is difficult to meet this demand. This paper takes the computer software engineering major in application-oriented universities as the research object, systematically sorts out the problems existing in the current training mode, and proposes targeted four-dimensional solutions of “curriculum reconstruction, practice strengthening, faculty construction, and evaluation optimization”. Practice has shown that this model can effectively enhance students’ interdisciplinary application ability and job fit, providing a practical reference for the reform of software engineering education.
References
Yang C, Chi R, Li C, 2020, Research on the Training Mode and Strategy of Software Engineering Technology. Journal of Shenzhen Polytechnic University, 24(5): 99–104.
Dong Z, Lin Y, Cong R, 2025, New Engineering Curriculum Optimization and Evaluation System Reconstruction from the Perspective of Systems Theory: A Case Study of Computer Vision Course. Computer Education, 2025(6): 168–172 + 177.
Lang C, Qian H, Wu S, et al., 25, Research on the Training Mode of Computer-related Compound Talents in the Context of New Engineering. Electronic Components and Information Technology, 9(1): 234–237.
Liu Y, Wei J, Li J, 2014, Research and Application of Intelligent Testing Tools and Processes. Oil and Gas Well Testing, 23(2): 71–74 + 78.
Zhang H, 2023, Research on the Training of Computer Talents in Private Colleges. Electronics Science Technology and Application, 2023(10): 71–73.
Zhang H, Mu Y, 2024, Research on Curriculum Construction of Artificial Intelligence under the Background of New Engineering. Education Reform and Development, 2024(4): 108–111.
Tian C, Chen L, Wang S, et al., 25, Macrocell Layout Method for Intelligent Chip Based on Graph Convolutional Network and Reinforcement Learning. Shanghai Aerospace (Chinese and English), 42(5): 167–177.
Zhang H, 2024, Research on the Application-oriented Talent Cultivation System of Software Engineering in Independent Colleges. Internet Weekly, 2024(1): 65–67.
Harris N, 2025, AI Technology in the Application of Computer Software Development. Information Recording Materials, 26(10): 121–123.
Zhang H, 2022, Research on the Reform of Database Engineering Teaching in Applied Undergraduate Education. Jiangsu Science and Technology Information, 39(17): 53–56.
Cai Y, Zeng A, 2025, “12345” Integration of Government, Industry, Academia, Research and Application for Innovative Talent Cultivation in Software Engineering. Computer Education, 2025(9): 131–135.
Chen H, Du J, 2025, Exploration of Teaching Reform of “Time Series Analysis” Course Based on “SARIMA-BP/SVM/RF” Joint Model and R Language. Chinese Journal of Health Statistics, 42(4): 632–636.
Shen H, Chen Y, Gu P, et al., 2020, Case Study and Practice of “Disaster Monitoring and Early Warning Technology” Course Based on LSTM Model: Taking Rainfall Prediction as an Example. Industrial Control Computers, 38(8): 106–108.
Gao Z, Tang L, Gu R, 2023, Exploring the Development of an Innovative Case Curriculum Standard for “TensorFlow Basic Practice”. Knowledge Library, 2023(3): 37–39.
Lu J, Wang L, Hou M, 2022, Application and Prospect of Deep Learning-based PyTorch in Flight Safety Teaching. Educational Teaching Forum, 2022(46): 137–140.