The analysis of educational informatization management learning model under the internet of things and artificial intelligence.

This study explores the influence of the Internet of Things (IoT) and Artificial Intelligence (AI)-enhanced learning models on student management in educational informatization management. A game-theoretic enhanced learning model is proposed to achieve this objective, incorporating resource scheduling strategies under fog computing and a student management system that integrates IoT and AI technologies. This model's performance and the student management system are then tested. The results indicate that the fog computing-based hierarchical Q-learning (Q) model proposed in this study achieves faster convergence than a single Q model, reaching convergence after 80 training rounds, ten rounds earlier than the comparative algorithm. The model exhibits a lower average workload delay of 0.5 ms and fog node delay below 1 ms, showcasing significant advantages in terms of overall cost-effectiveness, thus minimizing service costs. The student management system has 3000 concurrent user connections, static page request times ranging from 0 to 25 s, login response time predominantly at 60 s, and a capacity to process up to 20 parallel tasks per second with zero errors. The system functionalities are fully realized, meeting usage demands effectively and achieving the highest average functional score of 9.03 for online interaction functionality. This study demonstrates the efficacy of the game-theoretic enhanced learning model in a fog computing environment and the positive impact of IoT and AI technologies on student management. The proposed student management system better caters to individual student needs, enhancing learning outcomes and experiences. The study's innovation lies in the integration of IoT technology with AI-enhanced learning models, coupled with the introduction of game-theoretic resource scheduling strategies, enabling the student management system to intelligently identify student requirements, allocate learning resources, and dynamically optimize the educational process, ultimately improving learning outcomes. This holds significant implications for enhancing education quality and promoting personalized student development.