AI Intelligent Technology Empowers the Construction of a Process Evaluation System for Higher Education

Abstract

Policy-driven higher education evaluation faces challenges like formalism and delayed feedback. This study proposes an AI-powered “AI–student–peer–teacher” collaborative evaluation system. It uses intelligent data collection, dynamic diagnosis, and an “EFI” model for immediate feedback and optimization. The system addresses evaluation overload and inefficiency through dynamic weighting, transparent algorithms, and differentiated strategies, ensuring fairness and personalization. It emphasizes integrating AI with educational principles, using human-computer calibration to shift from data-driven to value-driven evaluation, transforming static judgment into dynamic optimization for higher education quality improvement.