Keywords
ADVANCED DISTRIBUTED LEARNING;COGNITIVE;COMPETENCY BASED TRAINING;EDUCATION;EXPERIENCE API;INTEROPERABILITY;LEARNING ANALYTICS;LEARNING STANDARDS;LEARNING TECHNOLOGY STANDARDS;OPEN ARCHITECTURE;TECHNOLOGYAbstract
Crafting well-defined competency definitions is a complex and labor-intensive process that can be made easier with AI enabled and standards-based automation tools. Best practices typically require cognitive and physical task analysis, which can be tedious and time-consuming and require specialized expertise. Despite these efforts, the resulting definitions are often imprecise, failing to capture the full scope of the competencies as applied in different contexts or lacking the granularity needed for effective assessment. Another challenge is ensuring that these definitions are structured in formats that are interoperable across different platforms and learning modalities, which is essential for scalability and consistency in digital learning environments and for multi-domain training scenarios.
We will explain what it means for a competency to be well-defined for the purposes intended, drawing from the IEEE standard recommended practice for well-defined competencies (IEEE 1484.20.2) and other sources. We will explain the role of the standard for Sharable Competency Definitions (IEEE 1484.20.3), an anchor standard in the Total Learning Architecture.
After this, we will introduce some free and open tools for developing well-defined competencies frameworks in formats that can be used across-platforms and across multi-domain training contexts. These tools can automate a learning engineering approach to development and iterative refinement of well-defined competency definitions using human-in-the-loop generative AI, international standards, and learning analytics for data-verified specificity.
This tutorial is a primer suitable for anyone involved—directly or indirectly—in training, education, performance improvement, or talent management. This tutorial will give attendees important tools to optimize their work.
