A new study published in the Journal of Science Engineering Technology and Management Science proposes an AI-driven military decision support system designed to automate the classification of battlefield imagery and reduce the lag between data collection and actionable intelligence. The researchers behind the article, titled “AI-Driven Military Decision Support System Using Deep Learning and Tactical
The impact of AI technology for ideological and political education teaching based on deep learning – Scientific Reports
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Scientific Reports , Article number: (2026) Cite this article
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Abstract
This study investigates the innovative use of deep learning models in ideological and political education (IPE) at vocational colleges. The study focuses on addressing two core challenges in traditional IPE: limited adaptability of educational resources and low student engagement. Using datasets related to resource allocation and learner performance, the study applies Graph Neural Networks (GNNs) and a Multimodal Meta-Learning Frameworks (MMLFs). These are combined with the extended sequence modeling capabilities of Transformer-XL to build a dynamic resource optimization model. The model incorporates 42 features spanning three dimensions—learner characteristics, resource attributes, and environmental factors—through heterogeneous data fusion. A multi-head attention mechanism enables cross-feature interaction, while a curriculum knowledge graph maps resources to specific competencies. Experimental results show that the model achieves a resource effectiveness prediction accuracy of 89.7%, surpassing traditional methods by 23.5%. It also improves knowledge acquisition by 37.2% and raises the positive behavioral transformation rate by 41.3%. Course completion rates increased to 0.87, and cross-cultural transfer tests maintained an accuracy of 83.4%. Furthermore, the dynamic optimization mechanism reduced resource redundancy by 32% and improved teacher management efficiency by 80%. Demonstrating strong robustness in cross-cultural educational contexts, this model offers a promising pathway for transforming IPE through artificial intelligence.
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The studies involving human participants were reviewed and approved by School of Architecture and Art, Jiangxi Technical College of Manufacturing Ethics Committee (Approval Number: 2023.2600123). The participants provided their written informed consent to participate in this study. All methods were performed in accordance with relevant guidelines and regulations.
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Wang, Y. The impact of AI technology for ideological and political education teaching based on deep learning. Sci Rep (2026). https://doi.org/10.1038/s41598-026-51233-3
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DOI: https://doi.org/10.1038/s41598-026-51233-3
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