AI-Driven Closed-Loop Information Management for English Translation in Higher Education

This study presents an artificial intelligence-driven closed-loop system for college English translation teaching, transforming student learning activities into structured information resources for institutional decision-making. By integrating artificial intelligence scoring, multimodal data, and human-in-the-loop evaluation, the system enables dynamic monitoring, anomaly detection, and process-level optimization. Empirical results demonstrate improvements in translation accuracy, cultural adaptability, and feedback efficiency, while revealing operational challenges under high workload. From an information resources management perspective, this framework illustrates how educational processes can be leveraged as strategic organizational assets, supporting adaptive management, resource allocation, and sustainable decision-making.