Keywords: AI;ASSESSMENT;AUGMENTED AND VIRTUAL REALITY (AR/VR);COMPETENCY BASED TRAINING;LEARNING ANALYTICS;MACHINE LEARNING;PERSONALIZED TRAINING;SIMULATIONS

Abstract:

The evolution of augmented reality (AR) and virtual reality (VR) simulations offers unprecedented opportunities for competency-based training and assessment. However, most existing AR/VR training solutions remain procedural and knowledge-based, primarily supporting vocational or mechanical training rather than constructivist, learning-by-doing approaches that foster real-world competencies. This tutorial session focuses on moving learners from novice to expert efficiently and effectively by leveraging AR/VR simulations as adaptive instructional systems (AISs) designed to assess and develop competencies rather than just knowledge retention.

A core principle of this approach is understanding how people learn best. The ICAP framework (Chi & Wylie, 2014) asserts that increased engagement levels—passive, active, constructive, and interactive—enhance learning outcomes. Similarly, Dewey’s (1938) experiential learning theory reinforces that knowledge is socially constructed and must be situated in real-life contexts. Therefore, AR/VR-based learning environments should enable interactive, socially constructed experiences, offering superior learning outcomes compared to traditional, passive methods.

To maximize effectiveness, AR/VR simulations should integrate best practices from intelligent tutoring systems (ITSs) and adaptive instructional design. This includes modular system architecture with content, learner, and adaptation modules to tailor instruction dynamically based on learner and/or team performance. Unlike static, two-dimensional, computer-based training, a well-designed AIS within AR/VR can accelerate skill acquisition, ensure competency mastery, and provide real-time performance assessment.

This session will explore key design considerations in developing AR/VR competency-based simulations, including:

• Competency-Based Learning and Assessment: Designing for analytical thinking, problem-solving, technical proficiency, digital literacy, communication, project management, and adaptability.
• Scenario Design for High-Quality Evidence Collection: Ensuring tasks reflect real-world complexity and allow valid assessments of competency mastery.
• AI/ML Integration for Adaptive Learning: Leveraging AI-driven performance analysis to support personalized instruction and competency validation.
• Communication to Learning Management Systems (LMSs): Ensuring collected data translates into actionable insights for training improvement.

The future of AR/VR training must go beyond basic procedural tasks and embrace learning engineering principles to create evidence-driven competency development tools. By designing adaptive, immersive, and interactive simulations, we can revolutionize training effectiveness across military, corporate, and technical fields.

We must demand more from AR/VR training. Do not go gentle into the black night of the current state of AR/VR training simulations. Rage, rage against the dying of the light! Challenge every AR/VR training solution to provide evidence of an informed learning design, because the data to prove learning effectiveness can—and must—be collected.