{"id":1538,"date":"2025-11-22T15:56:17","date_gmt":"2025-11-22T20:56:17","guid":{"rendered":"https:\/\/ne.ncsu.edu\/artisans\/?page_id=1538"},"modified":"2025-11-26T04:12:51","modified_gmt":"2025-11-26T09:12:51","slug":"cora","status":"publish","type":"page","link":"https:\/\/ne.ncsu.edu\/artisans\/research\/cora\/","title":{"rendered":"CORA"},"content":{"rendered":"\n
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Research Topics<\/strong><\/h4>\n\n\n\n

Inverse Uncertainty Quantification (UQ)<\/a>
Integration of Prior Knowledge, Inverse UQ and Quantitative Validation<\/a>
Uncertainty Quantification of Machine Learning (ML)<\/a>
Deep Generative Modeling (DGM)<\/a>
<\/a>Cognitive Operator Readiness Assistant (CORA)
<\/a>UQ and ML for Nuclear Forensics and Non-proliferation<\/a><\/p>\n<\/div><\/div>\n\n\n\n

Cognitive Operator Readiness Assistant (CORA)<\/mark><\/strong><\/h4>\n\n\n\n

As the U.S. aims to triple nuclear energy capacity by 2050, innovative training methods are critical to facilitate rapid workforce development. We present CORA (Cognitive Operator Readiness Assistant), an AI-based system designed to enhance nuclear reactor operator training at NC State’s PULSTAR facility. CORA is structured around a validated knowledge graph combining facility-specific procedures with NRC-backed reactor fundamentals, and interfaces with a fine-tuned large language model (LLM) to deliver personalized instruction to trainees. The system maintains full traceability to source materials and adapts to each user’s individual learning style, identifying their strengths, weaknesses, and the learning methods that best resonate with them. Human instructors are kept in the loop during the AI-assisted training process, reducing their labor burden while simultaneously providing them with a heightened understanding of the progress of each student. This pilot deployment demonstrates how emerging AI technologies can scale technical education programs while maintaining the rigorous safety standards essential for high-risk engineering professions.<\/p>\n\n\n

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Relevant publications:<\/mark><\/strong><\/p>\n\n\n\n

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  1. Clifford, J., White, J., Heifetz, A., Hawari, A., and Wu, X. (2025). Enhancing Learning for Nuclear Reactor Operator Trainees with Large Language Models. In Proceedings of the 2025 Nuclear Plant Instrumentation and Control & Human-Machine Interface Technology (NPIC&HMIT). Chicago, IL, USA, June 15-18, 2025<\/li>\n<\/ol>\n\n\n\n

    <\/p>\n","protected":false},"excerpt":{"rendered":"

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