Cognitive aid systems can provide the greatest degree of flexibility and personalization while allowing users to obtain useful assistance with their industrial work duties. Eye-tracking technology as a fundamental element of cognitive aid systems to maximize worker performance in human–cyber–physical systems is investigated in this study. Real-time visual attention analysis is made possible by eye tracking, which offers valuable information on task engagement, skill levels, and cognitive load during intricate manual tasks. The use of these technologies is illustrated by a case study involving the electronic car battery module housing quality measurement. The findings, which were confirmed by ANOVA analysis, show that fixation times vary significantly among skill levels with experts exhibiting noticeably faster reactions. These results highlight how important it is to include eye-tracking data into context-sensitive, adaptive cognitive support systems that improve worker productivity and decision-making. This study demonstrates how visual attention measures may be utilized to develop more intelligent, user-focused solutions for Industry 4.0 applications.