Abstract
Usability drives efficiency and improvements in performance and work outcomes. As such, organizations have many critical positions that could benefit from improved usability competency. However, the personnel in these positions often lack the time to complete typical certification courses that are rarely tailored to their specific needs. AI provides an opportunity to rapidly catalyze and personalize usability competency advancements. This paper presents process methodologies towards developing a usability competency framework for implementation in an AI-driven knowledge repository. The goal of the repository is to address time-constraints in training by ensuring that the system and training tools are designed to systematically enhance job-specific knowledge, skills, and abilities related to usability. A well-defined competency framework supports structured learning and skill development, making it easier for individuals to acquire and apply knowledge in practical settings. By applying tailored usability practices to critical job functions, this framework aims to improve performance and efficiency.
To illustrate this methodology in practice, this paper presents a case study using Health Informatics professionals within the Veterans Health Administration. This example demonstrates how, by leveraging a customized competency framework and AI-enhanced engine architecture, training content is efficiently organized and disseminated, enabling scalable, relevant, and personalized competency advancement. The system architecture, composed of a Knowledge Curation layer and a Knowledge Dissemination layer, ingests, segments, and tags content assets’ metadata for fast indexing to then provide just-in-time content, recommendations, and content generation. This paper will also provide an overview of how the AI-driven knowledge repository uses 1) Retrieval-Augmented Generation (RAG) to retrieve up-to-date information, 2) a web-based content curation and validation workflow process, and 3) a human-in-the-loop approach that allows for tag inspection and customized updates to final published usability training content.