As nuclear operators begin introducing AI and machine learning tools into field activities, engineering workflows, and outage planning, human factors engineering becomes central to whether those tools earn operator trust and acceptance. In this fireside chat, a human factors expert from Idaho National Laboratory sits down with an HFE practitioner from outside the nuclear sector to explore how established HFE principles apply to advanced AI systems. The conversation covers trust calibration, automation transparency, operator workload, and the design choices that determine whether an AI tool supports sound decisions and situational awareness.\n\nWhat You'll Gain
\nLearn how human factors practices from outside of nuclear translate into the nuclear industry AI deployment; pick up practical approaches to calibrating user trust so teams neither over-rely on nor dismiss AI recommendations; understand the design choices behind automation transparency, explainability, and workload management; and recognize the common HFE missteps that surface when AI is layered onto established control room and engineering workflows.
\n\nWho Should Attend
\nIndustry AI leaders, human factors and human performance specialists, I&C and digital engineering teams, licensing and regulatory affairs professionals, and technology leaders responsible for introducing AI tools into operational nuclear environments.
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