<![CDATA[Although personality traits are widely recognized as important predictors of academic success and career preferences, their integration into AI-driven academic advising systems remains limited. Existing approaches predominantly rely on academic performance data and historical learning behavior, often overlooking psychological characteristics that influence students’ decision-making processes. In parallel, recent advances in artificial intelligence have enabled more sophisticated recommendation systems; however, these systems typically lack adaptive reasoning capabilities and do not incorporate personality as a core input variable. This study aims to address these gaps by examining how personality traits can support intelligent academic advising and by proposing a conceptual framework for a personality-aware agentic AI system in higher education. A systematic literature review following PRISMA 2020 guidelines was conducted using the Scopus database. From an initial set of 199 records, 21 studies were selected for qualitative synthesis after applying inclusion and exclusion criteria. The findings reveal three key limitations in existing research (1) personality traits are primarily used as explanatory variables rather than operational components in recommendation systems, (2) AI-based advising systems rely heavily on performance-driven data with limited psychological integration, and (3) there is a lack of unified frameworks that combine psychological modelling with adaptive AI architectures. To address these limitations, this study proposes a novel personality-aware agentic AI framework that integrates personality profiling, agentic AI-based reasoning, and intelligent recommendation mechanisms into a unified architecture. The framework introduces a multi-layered approach consisting of personality modelling, agentic AI processing, and recommendation delivery to support adaptive and context-aware academic and career guidance. This research contributes by bridging the gap between personality psychology and AI-driven recommendation systems while introducing agentic AI as a new paradigm for academic advising. Future research should focus on implementing and empirically validating the proposed framework in real-world higher education environments.]]>
