Background and Objective: The digital transformation and the proliferation of Artificial Intelligence (AI) in Human Resource Management (HRM) have necessitated a fundamental rethinking of Assessment Center (AC) design, leading to a gradual transition toward "Smart Assessment Centers." This study aims to elucidate this evolutionary process and develop a conceptual framework for the transformation of ACs within the context of contemporary HRM.
Methodology: his research employs a qualitative and exploratory approach using content analysis. Data were gathered from 42 credible scientific sources published between 2020 and 2025, selected from the Wiley and Taylor & Francis databases based on their relevance to digital transformation and HRM. The data were analyzed through three levels of coding: open, axial, and selective. The scientific rigor and trustworthiness of the findings were ensured based on Lincoln and Guba’s (1985) criteria.
Findings: The findings indicate that the evolution of ACs can be explained in three distinct waves: the first wave involves the digitization of tools; the second wave focuses on augmented intelligence with a Human-in-the-Loop (HITL) approach; and the third wave centers on the emergence of ecosystems driven by generative models and interactive simulations. While smart ACs enhance operational efficiency and the richness of behavioral evidence, they pose significant challenges regarding validity, fairness, explainability, and legal compliance.
Conclusion: The synthesis of the findings suggests that Smart Assessment Centers are more than mere technical tools; they are, in fact, "socio-technical systems" in which technology serves to augment, rather than replace, professional human judgment. Consequently, the future of this field lies not in pure automation, but in responsible, ethical, and human-centric intelligence.