Building Job-Ready Capability Through AI Practice

Skills are built through practice, but practice becomes much more effective when it is grounded in a clear understanding of the skill itself. Too often, learning experiences ask people to participate in scenarios or roleplays without clearly defining what the skill looks like, what progression looks like, or how feedback should connect to performance.

In this hands-on BYOD session, participants will learn how to design AI-supported practice environments anchored in a simple skill taxonomy. They will define a target skill, identify its key dimensions, describe levels of proficiency, and translate those into realistic workplace scenarios where learners can practice, make decisions, receive feedback, and try again. Using common LLM tools, attendees will build a reusable simulator that delivers structured practice tied to observable performance rather than generic interaction.

The session also explores how skill-based design improves feedback quality and creates more useful evidence of development. When practice is tied to defined skill criteria and levels, learners can better understand what stronger performance looks like, managers can coach with greater specificity, and L&D can support capability development in a way that is more visible and practical. Attendees will leave with a working prototype, a repeatable design approach, and a stronger framework for building job-ready capability through practice.

Participants will be able to:

• Define a workplace skill using dimensions and observable performance criteria.
• Describe levels of proficiency and identify what changes as capability develops.
• Design realistic AI-supported practice aligned to a target skill and level.
• Build feedback structures that guide improvement based on defined performance criteria.
• Explain how AI practice environments can support skill development, coaching, and evidence of growth.