The Role of AI in Academic Advising: A Theoretical Framework for Supporting Student Decision-Making

Abstract

In the context of higher education facing mounting pressure from the overload of traditional academic advising models, this study focuses on clarifying the theoretical gap regarding the interaction mechanisms among artificial intelligence (AI), human advisors, and students in the decision-making support process. Based on the conceptual synthesis method and the integration of Self-Determination Theory (SDT) and Self-Regulated Learning (SRL), the study proposes a Human-AI Learning Decision Support Framework. This framework consists of three closely interconnected blocks: the Input Block, which integrates real-time learning behavior data from the LMS; the Process Block, which organizes the coordination between the AI's role as a Cognitive Filter and the human advisor's role as an Emotional Anchor; and the Output Block, which focuses on developing student agency in the decision-making process. The research findings establish a theoretical conceptual system comprising the proposed structure, operational principles, humanistic principles, and decision-making agency corollaries, thereby clarifying the division of tasks between AI and humans. Furthermore, the framework provides a reference basis for higher education institutions in designing and optimizing hybrid advising systems, as well as defining directions for future empirical studies.