Advancing Educational Management with the ATT-MR-WL Intelligent Question-answering Model

Abstract

Higher education plays a critical role in cultivating talent, preserving culture, and promoting social progress. However, current challenges, such as inefficient information dissemination and low problem-solving efficiency among students, highlight the need for intelligent question-answering systems. These systems, leveraging artificial intelligence and natural language processing technologies, enable rapid and accurate responses to student queries, thereby providing intelligent support for higher education management. This study introduces the ATT-MR-WL model, a generative AI system integrating Mask R-CNN and Word2Vec+LSTM to enhance intelligent question-answering functionality. The model, customized to handle both text and visual data, is evaluated using the established VQA v2.0 dataset and a specially developed EM dataset reflecting university management scenarios. The ATT-MR-WL model demonstrates a 3% accuracy improvement over traditional methods and enhances its ability to handle multimodal queries. This research provides important insights for enhancing the efficiency and quality of higher education management and advancing the process of educational informatization.