ISM-ARIS: Integrated Simulation Model with Adaptive Reinforcement Integration Strategy

ISM-ARIS: Integrated Simulation Model with Adaptive Reinforcement Integration Strategy Overview ISM-ARIS is a hybrid simulation evaluation framework designed to enhance quality monitoring of vocational education through the integration of Mixed Reality (MR) and Reinforcement Learning (RL).The system creates immersive, interactive, and task-oriented learning environments while using adaptive reinforcement learning to dynamically optimize instructional pathways. The framework combines: ISM (Integrated Simulation Model) — immersive, MR-based learning simulations. ARIS (Adaptive Reinforcement Integration Strategy) — hierarchical RL optimization for real-time adaptability and feedback. This unified approach improves monitoring accuracy, learning efficiency, and educational quality across vocational training programs. ✨ Features Mixed Reality Simulation Immersive and interactive task environments. Realistic, real-time simulation of vocational scenarios. Supports multiple training contexts and disciplines. Adaptive Reinforcement Learning Dynamically adjusts instruction and assessment. Uses hierarchical RL agents for task-specific optimization. Incorporates reward shaping for skill acquisition and feedback loops. Scalable Quality Monitoring Real-time performance tracking and analysis. Adaptive feedback based on MR sensor data. Continuous alignment with training objectives and industry standards. Modular System Architecture Multimodal encoder with graphical propagation layer (Fig. 1, p.7). Adaptive learning mechanisms for personalized progression. Policy optimization through Bellman-based composite state modeling. 📊 Datasets Dataset Description Purpose Vocational Education Quality Metrics Dataset Institutional metrics: performance, graduation rates, employment outcomes Evaluation & quality assurance Mixed Reality Learning Environments Dataset MR-based interaction and engagement data Simulation modeling Reinforcement Learning Strategy Outcomes Dataset Performance under different RL strategies Training ARIS agents Integrated Vocational Training Simulation Dataset Real-world simulation and performance records Model optimization 🚀 Usage MR simulation state evolution RL agent policy decisions Real-time quality metrics and performance scores 🧪 Applications Real-time quality monitoring of vocational education Adaptive simulation-based training Immersive MR learning environments for skill acquisition Performance-based educational policy and accreditation support 🧩 Model Components ISM (Integrated Simulation Model) — MR-based immersive simulation layer. ARIS (Adaptive Reinforcement Integration Strategy) — hierarchical RL integration (Fig. 3, p.9). Multimodal Encoder Architecture — feature fusion with CNN + Transformer backbones (Fig. 2, p.8). Graphical Propagation Layer — contextual representation learning. Reward Shaping & Exploration Control — adaptive optimization of learning trajectories. 📈 Performance Dataset Accuracy Precision Recall AUC Vocational Education Quality Metrics 89.12 88.34 88.67 88.45 Mixed Reality Learning Environments 90.23 89.56 89.89 89.67 RL Strategy Outcomes 89.12 88.34 88.68 88.42 Integrated Vocational Training Simulation 91.23 90.56 90.81 90.63 ISM-ARIS consistently outperforms baseline models (ResNet, ViT, I3D, BLIP, DenseNet, MobileNet) across multiple benchmarks (Table 1–2, p.13–14). 🧭 Future Work Optimize MR hardware integration for cost-effective deployment. Develop lightweight RL algorithms for faster training and lower resource requirements. Expand support for multilingual and cross-domain training scenarios. Integrate blockchain for credential tracking and verification. 📜 License This project is licensed under the MIT License. 🙏 Acknowledgments This research was conducted at Hainan Vocational University of Science and Technology.Authors: Nan Hu, DaWei Yun.This project was supported by the Education Department of Hainan Province (Project Hnjgwt2025-5).The system integrates MR and RL to support the modernization of vocational education quality monitoring.