R. Jha, C. Johnson
SimInsights Inc,
United States
Keywords: generative AI, VR (virtual reality), MR (mixed reality),XR, stimulus-generation, response-evaluation, personalized learning, adaptive learning, expert-led tutoring, apprenticeship, co-design, no-code tools, workforce training, system architectures, scenario creation, response scoring, feedback, adaptive dialogue, community-led training, collaboration
Summary:
Generative AI (Gen-AI) technologies have developed rapidly on both research and commercialization fronts in recent years, as seen from the rapid launch of increasingly powerful models from OpenAI, Google, Nvidia, and many other smaller startups. At the same time, VR/MR hardware industry continues to produce increasingly powerful devices at increasingly affordable prices. Combining these two technologies can advance the price-performance of both the stimulus-generation and response-evaluation aspects of training and assessment. Generative AI can also enable personalized and adaptive learning at scales and deliver outcomes approaching 1-1 expert-led tutoring and apprenticeship. However, a critical barrier is the high investment required to support a multidisciplinary skilled team of coders, artists and game designers for integrating these technologies to create and maintain training content. We argue that co-design and Gen-AI powered no-code tools are potentially a winning combination for reducing the time, cost and skill required for high quality XR content creation and delivery. By enabling non-technical instructional designers and subject matter experts to rapidly create, edit, share and deliver quality XR content, it becomes possible to democratize high quality workforce training. In this presentation, we will outline high level system architectures for no-code tools that serve common use cases in training such as rapid scenario creation, automated response scoring and feedback, and adaptive dialogue with AI agents. We will also present examples of co-design with subject matter experts, community members and students to highlight the impact of broader participation and collaboration on content quality, customization and agility. Through these examples from our NSF SBIR funded R&D, we aim to demonstrate the opportunities afforded by co-design and no-code tools for delivering the same kind of price-performance improvements in XR content that we have witnessed in XR hardware and AI models.