Beyond the Lab: Using AI to Build Better Hands-On Learning

Virtual labs and simulations are powerful tools for helping learners move from understanding a concept to applying it with confidence. But effective hands-on learning does not begin and end with the lab environment. It depends on clear objectives, realistic practice scenarios, reusable supporting content, thoughtful review cycles, and a sustainable plan for keeping experiences current as products, learners, and business needs evolve.

In this interactive webinar, Julie Cochrane , M. Ed., will introduce a practical framework designed to help education leaders and content teams identify where friction shows up in the learning development lifecycle and where AI can create meaningful lift. Rather than treating AI as a shortcut for producing more content, the session will focus on how teams can use AI intentionally to strengthen the design, development, review, and continuous improvement of hands-on learning experiences.

During the session, participants will explore how to:

 Surface hidden friction that slows the creation and upkeep of labs, simulations, and guided practice experiences

 Identify which stage of the learning content lifecycle is creating the greatest constraint

 Evaluate where AI can clarify inputs, accelerate drafting, improve reuse, streamline reviews, or support updates

 Distinguish between work that can be AI-assisted and work that should remain human-led, especially when designing meaningful learner practice

 Translate insights into one focused, measurable improvement to test over the next 30 days

This is not a session about chasing every possible AI use case. It is a guided, practical conversation about moving beyond the lab itself to build stronger learning experiences around it. Attendees will leave with a clearer view of where AI can help, where human expertise still matters most, and how to take one meaningful step toward more scalable, sustainable, and impactful hands-on learning.