Construction and Application Research of University Academic Early Warning System Based on AIGC Technology

Traditional university academic early warning systems often suffer from single data dimensions, rigid warning thresholds, and a lack of personalized intervention, making it difficult to accurately identify high-risk students and provide effective support[1]. This research innovatively proposes a construction method for a university academic early warning system based on Artificial Intelligence Generated Content (AIGC) technology [2]. This system integrates multi-dimensional data such as student academic performance, classroom behavior, online interactions, and psychological state. Leveraging AIGC technologies, including Natural Language Processing (NLP) and Generative Adversarial Networks (GANs), the system deeply mines students' potential learning behavior patterns and psychological states, generating more interpretable warning reports and personalized intervention suggestions. By constructing a multi-modal data fusion model and intelligent warning algorithms, the system can achieve early and accurate identification of students' academic risks. Simultaneously, personalized learning resources and tutoring suggestions generated using AIGC technology can more effectively help students overcome learning difficulties and improve their academic performance. This research aims to explore the application potential of AIGC technology in the field of university academic early warning and verify the advantages of this system in improving warning accuracy and intervention effectiveness, providing theoretical support and practical reference for building a more intelligent and humanized university academic support system.