The Elements of Effective AI-Focused Training: Why Traditional Models Fail  

In broad terms, government has embraced AI. Agencies are purchasing, piloting and integrating new tools and urging employees to use them. But therein lies a problem. The traditional learning paths that teach workers about new technology can’t keep pace with AI evolutions and don’t teach the judgement skills, such as evaluating outputs, that are vital to AI implementation. They are so vital, in fact, that four of the five pillars in the Department of Labor’s recent AI Literacy Framework focus on judgement-related knowledge.

If an agency wants to maximize its AI investments, the AI training content must constantly refresh. It should emphasize role-specific hands-on learning and AI judgement skills. And the curriculum should build around the capabilities workers identify, not the tools leaders choose, said Tony Holmes, Pluralsight’s Public Sector Practice Lead for Solutions Architects.

“Any solution … has to put people in contact with the real tools doing real tasks, not just abstract lectures … [and] generic training fails in that perspective,” he said, “because the same AI lesson lands completely differently for a budget analyst as it does for a contracting officer, since their actual day-to-day work is so different.”

In this video interview, Holmes discusses how to overcome the drawbacks of many government AI-training programs. Topics include:  

• Why traditional learning paths are ill-suited for AI
• Specific elements of an effective AI-training program
• How to overcome employee hesitancy to use AI