DOI: 10.5281/zenodo.17369011
title: ISM-ARIS: Integrated Simulation Model with Adaptive Reinforcement Integration Strategy

description: 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.