main mechanisms (1) changes in information presentation with automation; (2) operator monitoring, vigilance, and trust; and (3) operator engagement. In the first case, automation interfaces often lack essential information about the automation's status, leaving operators with little feedback on the controlled system's state. Also, automated systems can be unpredictable due to their need for more transparency regarding understandability and predictability. Secondly, automation requires extensive human monitoring; thus, vigilance problems arise, exacerbated by complacency or overreliance on automation. Besides, when people increase their trust in automation, they tend to decrease monitoring. Lastly, it is difficult for operators to fully understand what is happening when acting as passive automation monitors. In this sense, active cognitive engagement with a task can improve understanding and retention of critical information. The concept of SA has been extensively studied in various operational contexts, including military settings, transportation, sport, healthcare and medicine, process control, and emergency services. There are three theoretical models of SA: individual, team and system (Stanton et al., 2017). At the individual level, the most widely used definition of SA is given by Endsley (Salmon et al., 2008). Situation awareness describes the individual's ability to "be aware of what is happening around you and understand what that information means to you now and in the future" (Endsley, 2016). Endsley's definition can be broken down into three levels of awareness. Level 1 refers to the individual's perception of the relevant environmental elements' status, attributes, and dynamics. For instance, an operator needs data on a machine's status, and a car driver needs to know the positions of other vehicles. Level 2 includes understanding the significance of those elements and integrating them with relevant goals to comprehend the situation. For example, a power plant operator must combine various data elements to determine how well the system is functioning. Level 3 denotes the ability to project future actions from environmental characteristics. A review of the impacts of automation on the mining industry identified issues related to workload, cognitive load, communication, overreliance, acceptance of automation, trust, and mental well-being (Codoceo-Contreras et al., 2024). In addition, incidents reported in Australia between 2010 and 2021 involving autonomous haul trucks and surface blast-hole drills were associated with loss of situation awareness (SA), communication failures, over-trust, workload and complex interactions. All these issues have involved human interaction with technology (Burgess-Limerick et al., 2025). The same authors highlight that existing global standards and guidelines for the safe implementation of automation in mining are insufficient from the perspective of human-centred design(Burgess-Limerick et al., 2025). 3. METHODS Case studies of mining systems with different levels of automation served as the primary data sources for the analysis. Site visits were conducted at a mining site in Australia in February 2024. The case study method adheres to the ethical review process Guidelines of The University of Queensland and the National Statement on Ethical Conduct in Human Research. The data collection process included: 24
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