Journal of Applied Science and Engineering

Published by Tamkang University Press

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An Intelligent AI-Driven Course Reconstruction System for Adaptive Learning Ecosystems

 2026

 Volume 35

Hua Zheng

Zhanjiang University of Science and Technology, Guangdong Zhanjiang, 524000, China

Received: May 22, 2026
Accepted: July 24, 2026
Publication Date: September 18, 2026

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An overview of the Methodological workflow of the proposed CSO-DRNN model

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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The system incorporates a Cockroach Swarm Optimized Deep Recurrent Neural Network (CSO-DRNN), which combines the sequential learning capability of recurrent neural networks with the global optimization strength of the cockroach swarm algorithm. This hybrid design enables iterative analysis, reconstruction, and refinement of course content, creating a responsive teaching environment that adapts to learner needs and expert feedback. A course performance dataset, gathered from Kaggle, is developed for experimentation. Data preprocessing is performed using min-max normalization to ensure uniform scaling, while sequential features are extracted through deep recurrent layers to capture temporal and structural dependencies in evaluation metrics. To validate robustness, a 5-fold cross-validation strategy is applied, ensuring reliable generalization across different data partitions. Comparative evaluation confirms that CSO-DRNN achieves superior accuracy (97%), recall (0.9141), F1-score (0.9352), precision (0.9224), and hamming loss (0.0943) compared with conventional approaches, and 5-fold cross-validation achieved an average accuracy of 95.6%, demonstrating robustness. The system is implemented in Python using TensorFlow and Keras, ensuring scalability and applicability in diverse educational platforms. The proposed AI-driven adaptive course reconstruction information teaching system enhances curriculum design, improves teaching quality assessment, and supports education reform toward a human-centered, technology-driven paradigm.

Keywords: Intelligent Learning Ecosystem, AI-Driven Adaptive Course Reconstruction, Information Teaching System, Cockroach Swarm Optimization (CSO), Deep Recurrent Neural Network (DRNN)

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