Abstract:
This paper presents an in-depth analysis of how Artificial Intelligence's (AI) is reshaping the domain of Interactive Multimedia Instruction (IMI) for virtual training. Using a mixed-methods comparative analysis combining production metrics and stakeholder feedback, we examine AI’s impact on Instructional Systems Designers’ (ISDs) workflows, focusing on efficiency and training quality through data collected from a workforce of over 100 personnel.
Our research demonstrates how specialized LLM-backed chatbots streamline story board creation by enhancing data synthesis, maintaining grammatical standards, and accelerated content generation. AI capabilities extend to crafting instructional assessment questions and mediating routine interactions between Subject Matter Experts (SMEs), optimizing their valuable time. Additionally, we explore how Machine Learning (ML) methodologies standardize grammar, ensure formatting consistency, and facilitate storyboard translation, significantly reducing manual labor in these areas.
This paper compares periods of extensive AI tool engagement against minimal usage, revealing quantifiable impacts on production metrics. Our AI chatbot effectively managed over 15,000 inquiries with both high efficiency and cost-effectiveness. The system demonstrated the ability to digest extensive technical manuals within minutes, contrasting sharply with previous information processing timelines. Task completion rates increased measurably during high-usage intervals. Qualitative data from interviews and engagement surveys provides additional context to support these findings.
Our results highlight the benefits of AI throughout the IMI development lifecycle, indicating substantial productivity increases and workload reductions for all stakeholders. This research presents the first comprehensive framework for measuring and implementing AI-enhanced IMI development processes across multiple training domains, Moreover, it provides valuable insights into AI’s current role in IMI development and project future refinements that will advance virtual training development processes.
Keywords: AI;AUTOMATION;ENHANCING PERFORMANCE;MACHINE LEARNING;TRAINING