In highly autonomous robotic systems, human operators are able to attend to their own, separate tasks, but robots still need occasional human intervention. In this scenario, it may be difficult for human operators to determine the status of the system and environment when called upon to aid the robot. The resulting lack of situation awareness (SA) is a problem common to other automated systems, and it can lead to poor performance and compromised safety. Existing research on this problem suggested that reliable automation of information processing, called diagnostic aiding, leads to better operator SA. The effects of unreliable diagnostic aiding, however, were not well understood. These effects are likely to depend on the ability of the operator to perform the task unaided. That is, under conditions in which the operator can reconcile their own sensing with that of the robot, the influence of unreliable diagnostic aiding may be more pronounced. When the robot is the only source of information for a task, these effects may be weaker or may reverse direction. The purpose of the current experiment was to determine if SA is differentially affected by unreliability at different levels of unaided human performance and at different stages of diagnostic aiding. This was accomplished by experimentally manipulating the stage of diagnostic aiding, robot reliability, and the ability of the operator to build SA unaided. Results indicated that while reliable diagnostic aiding is generally useful, unreliable diagnostic aiding has effects that depend on the amount of information available to operators in the environment. This research improves understanding of how robots can support operator SA and can guide the development of future robots so that humans are most likely to use them effectively.