Executive Summary
AI talent has emerged as a core national strategic asset, and as global AI competition
shifts toward securing and cultivating talent, major countries are accelerating their policy
responses. With the spread of generative AI, the demand for AI talent has moved beyond
a simple quantitative shortage toward a qualitative question of what competencies are
needed. Accordingly, the notion of AI talent has diversified beyond specialized
professionals who develop AI models and algorithms to include interdisciplinary talent—
who combine AI competence with domain knowledge—and responsible AI talent.
This report analyzes how leading overseas universities and educational institutions
cultivate AI talent and derives implications for domestic policy and curriculum reform.
The analysis covers eight institutions across major countries focusing on the talent
profiles each pursues, how curricula are designed, and what learning experiences and
institutional support are provided to students. Our analysis identifies four common
features: (1) the reorganization of governance structures, such as advisor-rotation and
co-supervision systems, in an interdisciplinary direction; (2) redefining AI education
as a campus-wide, cross-disciplinary competency; (3) the rise of discipline-specific,
domain-integrated "AI+X" or "X+AI" education; and (4) the incorporation of
responsible AI and the capacity to verify and critique AI outputs as essential curricular
elements.
Building on these findings, this report argues that domestic policy should expand its
objective from increasing the supply of specialized personnel to systematically diffusing
discipline-specific AI competencies. Recommendations include building shared, modular
educational infrastructure; institutionalizing AI+X and X+AI multi-majors,
micro-majors, and convergence tracks; integrating industry- and
public-problem-based projects and internships into the formal curriculum; and
embedding responsible AI and process-oriented assessment as core principles. When
applying these cases, simple imitation should be avoided in favor of tailored designs
reflecting each institution's type and talent objectives. Ultimately, domestic AI talent
policy should be advanced to encompass not only the quantitative expansion of
specialized professionals but also discipline-specific embedding of AI,
experience-based learning, responsible AI education, and convergent governance.