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AI course production
United Way Worldwide
I introduced AI agents into course production and wrote the procedure they work under: what they draft from, who reviews their work and who can reject a draft. Every review was folded into the agent’s guidance, so drafts came to sound like the expert. Experts were skeptical at first and came to trust the first drafts.
- 67%
- less time to build each course
- 78%
- less subject-expert review time per course
Averaged across 11 courses built in 2026.
An AI agent that keeps what the expert changes
Every review goes into the agent’s guidance and examples, so each draft starts closer while the expert keeps the final say. Used on 11 courses in 2026.
A feedback loop. Frameworks and a procedure Kyle set feed an agent that updates the draft from the expert’s meeting notes. AI reviewers modeled on that expert check it first. The subject expert reviews every draft and can reject it. Each review is folded into the agent’s guidance and examples, not the AI model, so each round starts closer to the expert and the loop gets smaller. Once the expert approves, the learning team checks quality before the course is published. Expert review time per course fell 78%.