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News | April 4, 2025

AFCDA explores AI-powered curriculum development to accelerate training, enhance readiness

By Dan Hawkins Air Education and Training Command Public Affairs

Seeking to revolutionize curriculum development and rapidly adapt training for Airmen and Guardians to meet evolving threats, the Air Force is exploring the power of artificial intelligence.

During the Air Education and Training Command Force Development Summit here March 25, the Air Force Career Development Academy team from Keesler Air Force Base, Mississippi, showcased a new AI-powered curriculum development tool that promises to significantly reduce development timelines and deliver more agile and responsive training to Airmen.

Master Sgt. Adam Roush, an AFCDA Learning Engineer, explained the tool, dubbed "the Robot," leverages a retrieval augmented generation, or RAG, model, allowing it to ingest a variety of training materials, including education and training plans, Air Force Instructions, task analyses, and technical orders, to generate initial curriculum drafts.

“We've been achieving really good results here initially,” Roush said. “We’re at about a 90% accurate first draft. We may have to change some things, but it just goes to show the little things that we have to pick out and update, which makes it so rapid for us.”

This accelerated development process means Airmen will have access to the latest training and information faster, enhancing their readiness and lethality in the field, Roush said.

The team emphasized that human oversight remains critical throughout the process.

"While the AI tool significantly accelerates the curriculum development process, human oversight is absolutely essential," Lindsey Fredman, AFCDA Director, emphasized. "We always have subject matter experts review and validate any AI-generated content to ensure accuracy, relevance, and alignment with Air Force standards."

The team is working to make the tool scalable and accessible across AETC and potentially the wider Air Force, Fredman said.

(Editor’s Note: Creation of this article assisted by NIPRGPT)