A Study on the Application of AI Tutors Integrating Multimodal Data in Predicting Student Behavior

As higher education management undergoes a smart transformation, the precise prediction of student behavior has become a critical component in achieving targeted education. Traditional counseling mechanisms often rely on human experience, making it difficult to promptly and accurately identify potential risky behaviors among students. This paper proposes an artificial intelligence (AI) counselor system that integrates multi-modal data, comprehensively utilizing behavioral data, text data, and emotional signals generated by students across multiple scenarios such as learning platforms, social interactions, and classroom performance, to achieve intelligent prediction and dynamic intervention of student behavior. The system employs a multi-modal fusion mechanism to construct a deep learning model integrating Transformers and long short-term memory (LSTMs), comprehensively modeling and analyzing changes in students' learning attitudes, fluctuations in psychological states, and withdrawal risks. Experimental results demonstrate that the AI counseling system, which integrates text, behavioral, and emotional modalities and incorporates an attention mechanism, achieves significant performance in student behavior prediction tasks, with an F1-score of 0.853, outperforming all ablation comparison models. This study not only improves prediction accuracy and model stability but also validates the feasibility of multi-modal information collaboration in educational scenarios. This research provides theoretical support and technical pathways for the construction of intelligent intervention mechanisms in higher education institutions, offering promising prospects for widespread application.