Mechanism Reconfiguration and Practical Pathways for AI-Empowered Labor Education in Higher Education

Linhui Li; Haoyue Xue; Hua Yang; Nila Lan

doi:10.70088/xyar6d37

Authors

Linhui Li Academic Affairs Office, Guilin University of Aerospace Technology, Guilin, China Author
Haoyue Xue School of Artificial Intelligence, Guilin University of Aerospace Technology, Guilin, China Author
Hua Yang Academic Affairs Office, Guilin University of Aerospace Technology, Guilin, China Author
Nila Lan Academic Affairs Office, Guilin University of Aerospace Technology, Guilin, China Author

DOI:

https://doi.org/10.70088/xyar6d37

Keywords:

labor education, higher education, artificial intelligence, mechanism reconstruction, educational pathways, pedagogical innovation

Abstract

With the rapid development of artificial intelligence (AI), labor education in higher education institutions is accelerating toward an unprecedented intelligent transformation. Based on a comprehensive combination of normative research and literature analysis, this study constructs a robust mechanism framework for AI-empowered labor education and systematically examines its underlying value implications, practical dilemmas, and strategic implementation pathways. It is found that current labor education in universities still suffers from lagging pedagogical concepts, significant misalignment between educational objectives and practical implementation, insufficient process effectiveness, and limited scenario provision for students. From the perspective of mechanism reconstruction, this study proposes an innovative four-dimensional operational model encompassing "task generation—process co-creation—ability evolution—scenario ecosystem." Furthermore, it clarifies the structural components and operational logic of this model: optimizing task supply through "generation-driven and context-embedded" mechanisms; reconstructing teaching processes via "human–AI collaboration and data-driven feedback"; improving evaluation systems through "process-oriented and progressive development"; and expanding practice scenarios through "virtual–real integration and cross-domain linkage." The deep integration of AI technologies promotes labor education from static provision to dynamic generation and from unilateral implementation to collaborative co-creation, thereby facilitating a crucial transition from "linear execution" to "systemic coordination" in educational operations. Ultimately, this study provides both rigorous theoretical grounding and actionable practical guidance for the high-quality, sustainable development of labor education in contemporary higher education.

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