AI-Enabled Innovation in Vocational Training through Industry-Education Integration: A Dual-Translation Model

Abstract

The integration of industry and education is an important approach for improving the quality of vocational training. However, a gap often remains between enterprise needs and school-based learning because workplace tasks are difficult to directly transfer into curriculum content. This study explores how artificial intelligence (AI) can support curriculum innovation under industry-education integration. Drawing on an integrative literature review and publicly documented practices, this study proposes a dual-translation model that connects enterprise work tasks with teaching projects. The model includes two processes: translating enterprise information into structured occupational tasks, and transforming these tasks into progressive and assessable learning projects. AI supports information collection, task identification, project design, and learning feedback analysis, while teachers and enterprise experts remain responsible for educational and professional decisions. A financial accounting example based on Xero-enabled business practices is used to illustrate how professional tasks, such as financial reporting analysis and business performance evaluation, can be transformed into practical learning projects through the proposed model. The study argues that the role of AI in vocational training is not to replace teachers or enterprise experts, but to support the translation of professional workplace knowledge into structured learning experiences, improve school-enterprise curriculum development, and reduce the gap between learning and workplace requirements.