LLM Generation of Online Courses from a Curated Set of Documents in the Nuclear Safeguards Domain

Technical Report · Mon Sep 01 00:00:00 EDT 2025

DOI:https://doi.org/10.2172/3011640· OSTI ID:3011640

Parent, Scott; Fisher, Marianne N.; Seger, Heather; Blakely, Benjamin

A multidisciplinary team at Argonne National Laboratory explores the application of advanced technologies to enhance knowledge transfer and retention within the nuclear safeguards domain. Specifically, it examines the feasibility of leveraging secure large language models (LLMs) to streamline the creation of e-learning modules for the U.S. National Nuclear Security Administration (NNSA) Office of International Nuclear Safeguards (NA-241). The initiative addresses the critical need for preserving institutional memory and accelerating skill development amidst the imminent retirement of senior professionals in the field in addition to supporting good knowledge management practices. The project integrates instructional design theory with cutting-edge AI technologies to transform curated document sets from the Safeguards Knowledge Repository (SKR) into modular online courses. By automating the generation of learning objectives and instructional content, the effort aims to reduce manual effort while maintaining high-quality educational outcomes. A limited measure of human supervision, however, ensures accuracy, relevance, and alignment with NNSA’s strategic priorities. Key findings highlight the potential of AI-assisted course generation to support safeguards professionals by creating structured, interactive learning experiences. The report underscores the importance of SME validation to address limitations in AI-generated content, such as terminology errors and gaps in coverage. Recommendations include adopting a structured workflow combining LLM acceleration with expert oversight to ensure accuracy, usability, and alignment with learner needs. This work demonstrates Argonne’s commitment to advancing national security and scientific excellence through innovative knowledge management solutions.

• Research Organization: Argonne National Laboratory (ANL)
• Sponsoring Organization: US Department of Energy; Argonne National Laboratory - Laboratory Directed Research and Development (LDRD)
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 3011640
• Report Number(s): ANL-25/59; 199822
• Country of Publication: United States
• Language: English