Paper Type

ERF

Abstract

As artificial intelligence (AI) reshapes the future of work, aligning higher-education AI programs with rapidly shifting industry demand has become an urgent challenge for information systems (IS) education. Drawing on Taiwan’s recent expansion of AI degree programs, this emergent research conducts a threephase mixed-methods gap analysis: (i) a curriculum-structure analysis of 1,085 course titles from 16 newly established AI departments, mapped against the ACM CC2020 framework via COP-K means clustering; (ii) an industry-demand analysis of 12,621 AI-related job postings using Jieba/TF-IDF and Latent Dirichlet Allocation (LDA) topic modeling; and (iii) expert validation through structured interviews with five academic and industry leaders. Preliminary findings identify four curriculum patterns and three demand topics, with university programs emphasizing technical depth while approximately 63% of vacancies require application-oriented capabilities. The study contributes empirical, education-of-AI evidence for IS curriculum reform and surfaces design implications for human–AI value co-creation.

Paper Number

1589

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Aug 15th, 12:00 AM

AI Talent Cultivation for the Next Transformation: An Emergent Analysis of Academic-Industry Alignment in Taiwan

As artificial intelligence (AI) reshapes the future of work, aligning higher-education AI programs with rapidly shifting industry demand has become an urgent challenge for information systems (IS) education. Drawing on Taiwan’s recent expansion of AI degree programs, this emergent research conducts a threephase mixed-methods gap analysis: (i) a curriculum-structure analysis of 1,085 course titles from 16 newly established AI departments, mapped against the ACM CC2020 framework via COP-K means clustering; (ii) an industry-demand analysis of 12,621 AI-related job postings using Jieba/TF-IDF and Latent Dirichlet Allocation (LDA) topic modeling; and (iii) expert validation through structured interviews with five academic and industry leaders. Preliminary findings identify four curriculum patterns and three demand topics, with university programs emphasizing technical depth while approximately 63% of vacancies require application-oriented capabilities. The study contributes empirical, education-of-AI evidence for IS curriculum reform and surfaces design implications for human–AI value co-creation.

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