Future AI Competency and Skill-Gap Analysis for Strengthening AI Talent Development in Undergraduate Education

Kanda Sorn-In; Manussawee Nokkaew; Chayada Surawanitkun; Satit Kravenkit; Nongram  Mueanrit

doi:10.37936/ecti-eec.2026243.262993

Authors

Kanda Sorn-In Center of Multidisciplinary Innovation Network Talent (MINT Center), Faculty of Interdisciplinary Studies, Khon Kaen University, Nong Khai Campus, Nong Khai, 43000, Thailand
Manussawee Nokkaew Center of Multidisciplinary Innovation Network Talent (MINT Center), Faculty of Interdisciplinary Studies, Khon Kaen University, Nong Khai Campus, Nong Khai, 43000, Thailand
Chayada Surawanitkun Center of Multidisciplinary Innovation Network Talent (MINT Center), Faculty of Interdisciplinary Studies, Khon Kaen University, Nong Khai Campus, Nong Khai, 43000, Thailand
Satit Kravenkit Department of Computer Science, College of Computing, Khon Kaen University, Khon Kaen 40002, Thailand
Nongram Mueanrit Center of Multidisciplinary Innovation Network Talent (MINT Center), Faculty of Interdisciplinary Studies, Khon Kaen University, Nong Khai Campus, Nong Khai, 43000, Thailand

DOI:

https://doi.org/10.37936/ecti-eec.2026243.262993

Keywords:

AI Competency, Skill Gap Analysis, Text Mining, Labor-Market Intelligence, Undergraduate Curriculum

Abstract

Artificial intelligence is reshaping socio-technical systems, requiring universities to realign undergraduate curricula with emerging AI competency demands. This study introduces an integrated analytical framework to identify essential AI competencies and diagnose systemic skill gaps critical for future AI workforce development. Evidence is synthesized from five sources: international competency standards, stakeholder requirements, national AI and digital policies, labor-market intelligence derived from text mining, and global future-skills trends. The analysis delineates a structured set of technical, ethical, and innovation-oriented competencies, while revealing significant gaps in MLOps, large language model engineering, responsible AI governance, and human-centered AI design. The resulting AI Master Competency Taxonomy provides evidence-based guidance for designing industry-aligned, future-ready AI curricula that advance national strategic priorities and promote responsible and sustainable AI development.

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