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RESEARCH PAPER
Artificial Intelligence and International Students' Career Choices: STEM and Healthcare as Sustainable Career Pathways
1
Florida Gulf Coast University
These authors had equal contribution to this work
Submission date: 2026-08-24
Final revision date: 2026-08-28
Acceptance date: 2026-08-28
Publication date: 2026-08-29
Anatolian Journal of Educational Leadership and Instruction 2026;14(1)
KEYWORDS
STEM educationemployabilitycareer developmentinternational studentsartificial intelligencehealthcare education
TOPICS
ABSTRACT
Artificial intelligence (AI) is rapidly transforming global labor markets, higher education, and workforce expectations, creating new challenges for international students’ educational planning, employability, immigration pathways, and long-term career sustainability. Guided by Social Cognitive Career Theory (SCCT) and Human Capital Theory (HCT), this integrative literature review synthesizes empirical research, workforce reports, and policy analyses to examine how AI-driven technological disruption influences educational decision-making, career development, and workforce preparedness among international students. Findings identify several emerging trends, including growing demand for AI literacy, interdisciplinary competencies, adaptability, and continuous lifelong learning. STEM and healthcare disciplines currently demonstrate comparatively strong resilience to AI-driven labor-market disruption because of projected employment growth, complementarity between professional expertise and AI technologies, and post-graduation immigration opportunities through pathways such as Optional Practical Training (OPT) and STEM OPT extensions. The review further indicates that career sustainability increasingly depends on combining advanced technical competencies with uniquely human capabilities, including critical thinking, communication, ethical reasoning, leadership, adaptability, and cross-cultural collaboration. Building on these findings, the study proposes an integrated conceptual model combining SCCT and HCT to explain how self-efficacy, outcome expectations, educational investments, and contextual factors shape career decision-making in AI-driven economies. The review also distinguishes evidence based on direct labor-market projections from insights derived from adjacent literatures and the authors’ conceptual synthesis. Practical implications are offered for higher education institutions, career services, policymakers, and international students seeking to strengthen workforce readiness, informed career decision-making, and long-term career success in an increasingly AI-enabled global economy.
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