Artificial Intelligence, Employee Performance, and Career Development: A Mechanism-Based Systematic Review and Integrative Framework
DOI:
https://doi.org/10.37680/ijief.v6i1.9802Keywords:
artificial intelligence, employee performance, career development, systematic literature review, career adaptabilityAbstract
Workplace adoption of artificial intelligence (AI) has accelerated sharply, yet research examines its effects on employee performance and on career development separately, leaving the connecting mechanisms unmapped. This study synthesizes how AI shapes both outcomes simultaneously and the conditions governing the direction of its effects. Following PRISMA 2020 and combining the CIMO and PEO frameworks, a Scopus search retrieved 195 documents, of which 63 studies (2019–2025; 6,889 respondents) met eligibility and quality-appraisal criteria (JBI; ROBIS); findings were integrated through thematic synthesis. The literature proves data-rich but framework-poor: 81.0% of studies lack an explicit theoretical anchor and 71.4% treat AI as monolithic. Four performance pathways (skill enhancement, motivational, knowledge augmentation, stress–threat) and three career-pathway clusters emerge, with trust in AI, AI literacy, and supervisory support determining whether AI augments or disrupts; the same exposure can produce opposite effects. The study proposes a provisional integrative framework linking the two domains through career adaptability and outlines six priority research directions for HRM theory, practice, and policy in developing economies.
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