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<p>In the era of digital transformation, traditional college student education (CSE) management faces several challenges, including inefficiency, a lack of personalization, and delayed decision-making. To address these limitations, this research aims to integrate artificial intelligence (AI) into education management systems, aiming to refine and enhance both administrative and academic processes in higher education institutions. AI technologies provide significant potential to improve various facets of student management, such as academic performance monitoring, behavioral analysis, early risk detection, and personalized learning support. The dataset used in the research comprises academic records, including course grades, attendance logs, and enrollment histories. Additionally, behavioral and engagement metrics were collected from institutional learning management systems. To prepare the data for model training, feature normalization was performed using the Min-Max scaling technique, and principal component analysis (PCA) was employed for feature extraction. This research proposes a novel deep learning-based Artificial Gorilla Troops Optimizer-driven Residual Recurrent Neural Network (AGTO-ResRNN) model to automate student profiling and intervention planning. The model enables proactive academic advising and timely delivery of personalized support services by adjusting educational strategies based on predictive insights. The experimental implementation of the AGTO-ResRNN model demonstrated significant improvements in education management accuracy, operational efficiency, and student satisfaction. Key performance indicators, such as MAE (0.116), accuracy (98.50%), recall (95.42%), RMSE (0.183), F1-score (96.00%), and precision (96.54%), showed substantial enhancement compared to traditional methods. The research suggests that AI-driven management systems can enhance the educational environment, providing valuable insights to modernize student support.</p>

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Research on the refinement of college student education management based on artificial intelligence

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月12日

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  • Qiujia Lai

摘要

In the era of digital transformation, traditional college student education (CSE) management faces several challenges, including inefficiency, a lack of personalization, and delayed decision-making. To address these limitations, this research aims to integrate artificial intelligence (AI) into education management systems, aiming to refine and enhance both administrative and academic processes in higher education institutions. AI technologies provide significant potential to improve various facets of student management, such as academic performance monitoring, behavioral analysis, early risk detection, and personalized learning support. The dataset used in the research comprises academic records, including course grades, attendance logs, and enrollment histories. Additionally, behavioral and engagement metrics were collected from institutional learning management systems. To prepare the data for model training, feature normalization was performed using the Min-Max scaling technique, and principal component analysis (PCA) was employed for feature extraction. This research proposes a novel deep learning-based Artificial Gorilla Troops Optimizer-driven Residual Recurrent Neural Network (AGTO-ResRNN) model to automate student profiling and intervention planning. The model enables proactive academic advising and timely delivery of personalized support services by adjusting educational strategies based on predictive insights. The experimental implementation of the AGTO-ResRNN model demonstrated significant improvements in education management accuracy, operational efficiency, and student satisfaction. Key performance indicators, such as MAE (0.116), accuracy (98.50%), recall (95.42%), RMSE (0.183), F1-score (96.00%), and precision (96.54%), showed substantial enhancement compared to traditional methods. The research suggests that AI-driven management systems can enhance the educational environment, providing valuable insights to modernize student support.