<![CDATA[The rapid development of Artificial Intelligence (AI) has driven significant transformations across various aspects of higher education, including the professional responsibilities of academic staff. While previous studies have predominantly focused on the pedagogical and technological dimensions of AI integration, its implications for educator performance management have received comparatively limited attention. This study aims to analyze the implications of Artificial Intelligence utilization for reducing administrative workload and enhancing teaching quality, as well as to examine its contribution to the transformation of educator performance management in higher education institutions. The study employed an Integrative Literature Review approach based on the framework proposed by Whittemore and Knafl. Data were collected from the Scopus, ERIC, and Google Scholar databases, covering publications from 2021 to 2026. The synthesis of 17 selected articles indicates that AI utilization contributes significantly to improving academic work efficiency through the reduction of administrative and repetitive tasks. This efficiency enables the redistribution of lecturers’ time and resources toward higher-value activities, including instructional innovation, research, and academic collaboration. The findings further suggest that the advancement of AI has the potential to shift faculty performance management from an administrative compliance-oriented approach toward a model emphasizing the quality of contributions and academic impact. This study proposes the concept of value-based academic performance as a conceptual framework for managing human resources in higher education within the context of digital transformation]]>
