RESEARCH ON THE INTEGRATION OF MODERN APPRENTICESHIP SYSTEMS AND EDUCATIONAL LARGE MODELS: A PERSPECTIVE BASED ON LITERATURE REVIEW AND CASE ANALYSIS

Abstract

The modern apprenticeship system, which serves as a vital link between education and employment, faces challenges such as insufficient dual-teacher teaching capabilities, a limited assessment system, and communication barriers between schools and enterprises. To address these issues, Education Large Models (ELMs), powered by artificial intelligence, offer innovative solutions for reform. ELMs enhance teaching quality through personalized training, intelligent teaching assistance, and the creation of communication platforms for teachers, addressing disparities in teaching capabilities. For assessment, ELMs introduce dynamic, diverse, and personalized evaluation metrics, improving the scientific accuracy of apprenticeship evaluations and overcoming the limitations of traditional methods. Furthermore, ELMs establish a unified platform to bridge the communication gap between schools and enterprises by intelligently interpreting industry needs and educational feedback, facilitating effective collaboration. Some Institutes and colleges  which have taken in developing a vocational education-specific large-scale model, using new technologies such as artificial intelligence to empower large-scale personalized talent cultivation. However, challenges remain, such as data privacy concerns and algorithmic bias. Future research should focus on enhancing data security measures, addressing algorithmic fairness, and developing accessible technological solutions to broaden the applicability of ELMs across various educational contexts.