<![CDATA[Artificial intelligence-based academic advising systems are increasingly used in higher education to support course selection, academic planning, and career guidance. However, existing recommender systems often prioritize academic records, course histories, and behavioural data, while students’ psychological characteristics, particularly personality traits, remain insufficiently integrated into recommendation logic. This study aims to examine how personality traits can support personalized academic and career guidance and to propose a personality-aware agentic AI framework for higher education. Using a systematic literature review guided by PRISMA 2020, this study searched Scopus-indexed publications related to personality traits, artificial intelligence, recommender systems, academic advising, and career guidance. From 199 initial records, 45 studies were screened, 27 reports were assessed for eligibility, and 21 studies were included in the qualitative synthesis. Data were analysed through thematic synthesis and organized into five evidence clusters: personality and career development, AI-based academic advising, agentic AI architecture, cross-domain personality-aware recommender systems, and ethics and explainability. The findings reveal three major gaps: personality traits are mostly used as explanatory rather than operational variables; AI-based advising systems remain dominated by performance-driven data; and integrated frameworks combining psychological modelling, agentic reasoning, and recommendation delivery are still limited. In response, this study proposes a conceptual personality-aware agentic AI framework consisting of personality modelling, psychological profiling, agentic AI processing, intelligent recommendation generation, and decision-support interfaces. Although the framework has not yet been empirically validated, it offers a structured foundation for future prototype development, ethical implementation, and human-centred academic advising in higher education.]]>
