A Comprehensive Framework for Integrating Artificial Intelligence into Vocational Undergraduate Curriculum Systems: A Mixed-Methods Study
Year: 2025
Type: conference-paper
Abstract: The rapid advancement and pervasive integration of Artificial Intelligence (AI) are fundamentally transforming the global labor market. This transformation poses both a critical challenge and a significant opportunity for vocational undergraduate education, which aims to cultivate highly skilled, application-oriented talent. However, traditional curricula, which often adapt slowly, risk creating a significant skills gap, leaving graduates unprepared for an AI-enhanced workplace. This study proposes a novel, comprehensive framework designed to systematically integrate AI, drawing inspiration from the transformative applications of AI in education, such as personalized learning experiences, automated assessment systems, and the enhancement of educational management. competencies into vocational undergraduate programs. The framework is structured around four synergistic dimensions: 1) Universal AI Literacy, 2) Domain-Specific AI Application, 3) Human-AI Collaboration Synergy, and 4) a Dynamic Feedback and Evolution Loop. We also propose a practical, three-phase implementation pathway (Pilot, Scaling, Maturity) to guide this transformation. To validate the framework's relevance and feasibility, a mixed-methods approach was employed, incorporating a survey of 320 industry professionals and in-depth interviews with 15 academic administrators. The results reveal a significant perceived gap between the high industry demand for AI-skilled graduates and their current proficiency levels. Qualitative analysis identified key implementation enablers and barriers, with faculty development emerging as the most critical success factor. This study provides a theoretically grounded, empirically informed, and actionable blueprint for vocational universities to redesign their curricula to bridge the skills gap and cultivate a workforce capable of thriving in the AI-driven economy.[1]
Authors: Yanrui Qu, Syaza Hazwani Binti Zaini
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7161258905