5. Conclusions
This study evaluated the effects of instruction methods and task complexity on cognitive and perceived workloads in maintenance applications. The results supported the hypotheses that the AR method is more suitable, efficient, and effective to display maintenance instructions than the paper method; that AR instructions lead to faster maintenance times than paper instructions; and that AR instructions lead to a greater increase in mental workload than paper-based instructions, especially for the highly demanding task. Using AR instructions, the time was saved by 0.45% for the low-demanding task and by 14.94% for the high-demanding task as compared to paper instructions. It is argued that paper-based maintenance instruction for the highly complex task is time-consuming (i.e., viewing a paper and extracting different features). In contrast, the introduced AR-based instruction provides different functionalities that are presented on demand. Thus, the benefit of using AR for the low-difficulty task (with a small number of task steps) was relatively small. As the difficulty level increased, the advantages of applying AR to an instruction system became more obvious. Thus, in similar cases of higher task complexity, AR is recommended to be utilized. Furthermore, AR could enhance cognitive abilities to process information by easing information access and keeping working memory capacity free for use. Overall, the use of AR to guide workers through high-demanding maintenance tasks increased information processing, which could be connected to germane cognitive load due to learning. The findings suggested that AR instruction increased the germane cognitive load, which is the portion of cognitive load that assists in learning new skills and other information. AR for a high-demanding task allows for using cognitive or metacognitive prompts to hustle the participant in the right direction or to give the participant cues on how to best process the maintenance instructions. So, AR fosters the construction of schemata (GCL) and mental models by activating the cognitive processes of the participants to invest effort into learning, raising mental effort.Overall, this study shows that augmented reality should be used for high-demand maintenance tasks because it cuts down on maintenance time and makes it more likely that information will be stored in long-term memory and encrypted for later use. The results of this study can help manufacturing stakeholders better understand the benefits of AR technology as an instruction tool for manual tasks. Furthermore, the findings of this study could aid manufacturers of AR instruments in fine-tuning their products to overcome some of the limitations encountered. Also, the results of this study are important for the advancement of knowledge relative to cognitive workload evaluation while using AR systems in maintenance and assembly tasks.