Effects of Artificial Intelligence Decision Support Systems on Operator Trust and Workload

Abstract

AI-based decision support systems (AI-DSS) used in production lines are being increasingly adopted due to their potential to improve operator performance in complex and uncertainty-laden operations. These systems support operators throughout the process in terms of algorithmic accuracy and computational capacity. Another reason for their preference is their direct relationship with operator trust and perceived workload over time. This study aims to examine the effects of AI-based decision support systems on operator trust and workload from the perspective of human factors and ergonomics. By addressing the operator’s interaction with artificial intelligence within the system, the study focuses on the relationship between operators’ trust in system output and cognitive workload. The system outputs were tested by operators within scenarios offering different levels of AI support and were evaluated using multidimensional measurement tools. Subjective and objective indicators reflecting operator workload, together with time-based measurements, were considered jointly. Trust in the system was examined through dimensions such as transparency, predictability, and behavior in the face of errors. The findings suggest that when the output provided to the operator by AI-based support systems are not designed appropriately, they may lead to either over-trust or excessive caution among operators. As a result, both the accuracy of the operator’s decisions and situational awareness may be weakened. It was observed that as the level of automation increases, cognitive workload decreases; however, the ability to respond quickly and accurately to unusual situations within the system also declines. From the operators’ perspective, it was determined that adaptive and highly explainable AI solutions support trust in the system and contribute to maintaining workload balance. Overall, this study demonstrates that ergonomic principles play a decisive role in influencing operator performance in the design of AI-supported decision systems. As a fundamental design criterion, maintaining the balance between sustained performance, operator trust, and cognitive workload is essential. In this regard, the research contributes to the development of evaluation and design approaches for AI-supported decision systems in industrial, healthcare, and safety-critical application areas within lean manufacturing environments. The findings are expected to guide design processes toward establishing a more robust and effective foundation for human–AI interaction.

Keywords: Cognitive Workload, Explainable AI, Human Automation Trust, Lean Smart Manufacturing

DOI: 10.54941/ahfe1007773