Aims and Scope

1. Aim & Scope

The Journal of Immersive Simulation and Learning (JISL)is an interdisciplinary journal dedicated to advancing research on immersive simulation-based learning across educational, healthcare, professional training, and other real-world learning contexts. The journal focuses on how immersive and simulation-based environments can be designed, implemented, analyzed, and evaluated to support meaningful learning, skill development, collaboration, and performance improvement.

For JISL, immersive simulationrefers to technology-enhanced learning environments that create interactive, experiential, and situated forms of learning through technologies such as XR (VR/AR/MR), digital twins, hybrid simulation, haptic interfaces, virtual environments, and AI-supported adaptive systems.

JISL welcomes studies that examine not only learning outcomes, but also learning processes, interaction patterns, cognitive and behavioral dynamics, group collaboration, and multimodal forms of learner engagement.The journal particularly encourages research that connects technology design with learning theory, instructional design, empirical evidence, and real-world application.

The journal’s scope includes, but is not limited to, the following areas:

  • immersive simulation-based learning and training;
  • XR, digital twin, hybrid simulation, and haptic-supported learning environments;
  • embodied, experiential, and multimodal learning processes;
  • real-time interaction, behavioral data, and multimodal learning analytics;
  • collaborative and multi-user learning in immersive environments;
  • AI-supported, adaptive, and personalized immersive learning systems;
  • high-fidelity simulation for complex skill development;
  • instructional design, implementation, and evaluation of immersive learning environments;
  • ethical, social, accessibility, and equity issues in immersive simulation-based learning.

JISL encourages a wide range of rigorous scholarly contributions, including experimental and quasi-experimental studies, design-based research, implementation studies, qualitative and mixed-methods research, multimodal data-driven studies, theoretical and conceptual papers, literature reviews, systematic reviews, scoping reviews, and meta-analyses.

Submissions should demonstrate clear relevance to immersive simulation-based learning and should offer theoretical, methodological, design, or practical contributions. Manuscripts that merely describe a technology, present a proof-of-concept system without learning implications, or address learning, AI, XR, or educational technology in isolation without connection to immersive simulation-based learning are generally outside the journal’s scope.

The journal serves researchers, instructional designers, educators, healthcare and professional training specialists, technology developers, and policymakers interested in the design, analysis, and advancement of immersive simulation-based learning environments.

2. Core Keywords
  • Immersive LearningSimulation-based Learning
  • XR (VR/AR/MR) based Learning
  • Artificial Intelligence in Education (AI in Education)
  • Collaborative Learning
  • User Experience (UX)
  • Realtime group dynamics analysis
  • Learning Processes & Outcomes
  • Educational Technology
  • Instructional Design
  • Multimodal Learning Analytics
  • Interaction & Engagement
3. Scope Range
  • Design and Implementation of Immersive Simulation-based Learning Environments
    • Design, development, and implementation of XR, digital twins, hybrid simulation, haptic interfaces, virtual environments, and AI-supported adaptive systems for learning and training
  • Analysis of Learning Processes and Outcomes in Immersive Environments
    • Cognition, behavior, engagement, interaction, collaboration, and performance in immersive simulation-based learning
  • Instructional Design and Evaluation for Immersive Learning
    • Instructional strategies, learning environment design, implementation, assessment, and evaluation methods for immersive simulation-based learning
  • Data-driven Understanding of Immersive Learning
    • Learning analytics, multimodal user data, behavioral data, real-time interaction data, quantitative metrics, and assessment
  • Application in Real-world Learning and Training Contexts
    • Primary/secondary education, higher education, healthcare, vocational and professional training, workplace learning, and other applied learning contexts