Research Article
Reflections on AI Empowering the Employment of Graduates from Sports Vocational Colleges
Wenzhou Zhang,
Chanjun Chang*
Issue:
Volume 11, Issue 2, April 2026
Pages:
27-33
Received:
9 March 2026
Accepted:
30 March 2026
Published:
25 April 2026
Abstract: Against the background of the in-depth integration of artificial intelligence (AI) technology into various industries and the accelerated intelligent transformation of the global sports industry, graduates from sports vocational colleges are facing the dual pressure of upgraded job demands in the sports industry and insufficient matching of their own professional skills and comprehensive literacy. Focusing on the employment dilemma of graduates from sports vocational colleges, this paper adopts the methods of literature review, questionnaire survey and semi-structured interview to explore the practical value and application path of AI empowering the employment of graduates from sports vocational colleges. Based on the analysis of the current situation of AI application in the employment guidance, talent training and job matching of sports vocational colleges, this paper points out the existing problems such as insufficient application depth, weak AI literacy of teachers, lack of AI application ability of graduates and imperfect school-enterprise collaborative empowerment mechanism. Combined with the real demand of the intelligent sports industry for technical and skilled talents, targeted solutions and empowerment paths are proposed to provide theoretical support and practical reference for promoting the high-quality employment of graduates from sports vocational colleges, optimizing the employment work system of sports vocational colleges and promoting the coordinated development of the sports industry and vocational education. The research shows that AI technology can effectively break through the bottlenecks of homogenized employment guidance, inefficient job matching and backward talent training in sports vocational colleges, and help graduates improve their employment competitiveness. The organic integration of AI technology and the employment work of sports vocational colleges can effectively bridge the gap between talent training and enterprise demand, and promote graduates to achieve high-quality employment.
Abstract: Against the background of the in-depth integration of artificial intelligence (AI) technology into various industries and the accelerated intelligent transformation of the global sports industry, graduates from sports vocational colleges are facing the dual pressure of upgraded job demands in the sports industry and insufficient matching of their o...
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Research Article
Research-Ability-Orientated Course Design and Cultivation Examples of Numerical Simulation Technology on Manufacturing Processes for Graduate Students
Issue:
Volume 11, Issue 2, April 2026
Pages:
34-42
Received:
23 March 2026
Accepted:
20 April 2026
Published:
30 April 2026
DOI:
10.11648/j.her.20261102.12
Downloads:
Views:
Abstract: “Numerical Simulation Technology on Manufacturing Processes” is a graduate-level course designed for students majoring in mechanical engineering, especially in manufacturing technology. With the advancement of manufacturing and information technologies, the course has become significantly more applicable and practical for students across various manufacturing fields. To enable students not only to master numerical simulation techniques but also to extract and analyze engineering problems from a scientific perspective, this paper presents the course design, core contents, and representative outcomes as a reference. The main contents include: (1) the course background, target audience, and key instructional procedures, with an emphasis on the rationale behind student presentation sessions; (2) two typical manufacturing process case studies that illustrate how the course systematically enhances students’ research capabilities through the pre-simulation stages of problem extraction and model simplification; (3) a discussion on the application prospects of cutting-edge artificial intelligence (AI) technologies such as physics-informed neural networks (PINN) and graph neural networks (GNN) in simulation curricula, identifying them as key future directions for numerical simulation technology. Through a comprehensive introduction to this course, we aim to contribute to the cultivation of outstanding research-oriented talents for China’s manufacturing engineering sector, which already holds a competitive edge and having the inevitable requirement for a scientific transformation.
Abstract: “Numerical Simulation Technology on Manufacturing Processes” is a graduate-level course designed for students majoring in mechanical engineering, especially in manufacturing technology. With the advancement of manufacturing and information technologies, the course has become significantly more applicable and practical for students across various ma...
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,
Yiying Liang
