Trustworthiness and Trust: Identifying Factors that Drive Successful Human-AI Interaction in Nuclear Power Plant Applications

Conference · Wed Jun 18 00:00:00 EDT 2025

OSTI ID:3024592

Yamani, Yusuke ^[1]; Jackson, Austin ^[1]; Joe, Jeffrey C ^[2]; Mohon, Jeremy David ^[2]; Kovesdi, Casey R ^[2]

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1. Old Dominion University
2. Idaho National Laboratory

Emerging technologies such as artificial intelligence (AI) and machine learning (ML) are rapidly evolving and considered a promising tool for efficient and continued safe operations of the U.S. nuclear power plants (NPPs). Emerging AI techniques like large language models (LLMs) are one such technology that may support personnel at existing NPPs perform work more efficiently. For example, operators may query the current operational status of a power plant via a chat interface leveraging LLMs to access plant-related information in an interactive manner rather than manually collecting various sensor data for tasks such as surveillances or completing work orders. This is a fundamental shift in the way operators currently perform their tasks today. The literature of human-automation interaction indicates that trust is a crucial factor that drives successful interaction between a human operator and an automated system, like an AI-infused NPP application. This work presents the results of a literature review on key factors that relate to trust in AI/LLM technologies for NPP applications. The relevant literature of human factors and cognitive engineering has identified various factors related to trust including trustworthiness, performance characteristics, operator skill and perceived risk. This preliminary literature review will guide development and evaluation of models involving the identified factors influencing trust in AI and develop a framework for human-centered design for interface between humans and AI. By addressing trust, this work supports developing a technical basis for designing key characteristics of AI/LLM to support calibrated trust, which will ultimately support wide-scale adoption of AI/LLM technologies, as well as ensure safe, effective, and reliable use.

• Research Organization: Idaho National Laboratory (INL), Idaho Falls, ID (United States)
• Sponsoring Organization: USDOE Office of Nuclear Energy (NE); USDOE Office of Nuclear Energy (NE)
• DOE Contract Number: AC07-05ID14517
• OSTI ID: 3024592
• Report Number(s): INL/CON-25-83190
• Resource Type: Conference proceedings
• Conference Information: 14th International Topical Meeting on Nuclear Plant Instrumentation, Control & Human-Machine Interface Technologies (NPIC&HMIT 2025), Chicago, IL, USA, 06/15/2025 - 06/18/2025
• Country of Publication: United States
• Language: English

Related Subjects

99 - GENERAL AND MISCELLANEOUS
Artificial Intelligence
Large Language Models
Nuclear Plant Modernization
Trust
Trustworthiness