Paper Type
Complete
Paper Number
PACIS2025-1734
Description
As artificial intelligence transforms the digital economy, preparing the next generation for IT-driven careers requires strategic alignment between higher education and industry demands. This study investigates the gap between AI curriculum design across Taiwan's higher education institutions and actual market requirements through systematic analysis of 16 AI-related programs and 12,621 job postings from major recruitment platforms. Using K-means clustering, curriculum data revealed four distinct educational approaches: single-domain application, technical training-focused, foundational education, and interdisciplinary integration. Industry demand analysis employed TF-IDF and Latent Dirichlet Allocation to extract competency requirements and market patterns. The research findings reveal significant curriculum-industry misalignment, with 63% of AI positions emphasizing application-oriented skills (marketing strategy 35.06%, multimedia creation 27.53%) while current university programs predominantly focus on technical development (37.41%). These empirical insights provide evidence-based recommendations for curriculum reform and policy intervention, contributing to information systems education research and supporting Taiwan's digital transformation initiatives.
Recommended Citation
Wu, Ju-Chuan Kate; Lin, Tzu-Wei; and Wang, Fu-Guei, "AI Talent Cultivation: Higher Education-Industry Alignment in Taiwan" (2025). PACIS 2025 Proceedings. 8.
https://aisel.aisnet.org/pacis2025/is_education/is_education/8
AI Talent Cultivation: Higher Education-Industry Alignment in Taiwan
As artificial intelligence transforms the digital economy, preparing the next generation for IT-driven careers requires strategic alignment between higher education and industry demands. This study investigates the gap between AI curriculum design across Taiwan's higher education institutions and actual market requirements through systematic analysis of 16 AI-related programs and 12,621 job postings from major recruitment platforms. Using K-means clustering, curriculum data revealed four distinct educational approaches: single-domain application, technical training-focused, foundational education, and interdisciplinary integration. Industry demand analysis employed TF-IDF and Latent Dirichlet Allocation to extract competency requirements and market patterns. The research findings reveal significant curriculum-industry misalignment, with 63% of AI positions emphasizing application-oriented skills (marketing strategy 35.06%, multimedia creation 27.53%) while current university programs predominantly focus on technical development (37.41%). These empirical insights provide evidence-based recommendations for curriculum reform and policy intervention, contributing to information systems education research and supporting Taiwan's digital transformation initiatives.
Comments
e-Learning