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Machine learning (ML) is increasingly utilized in the development and assurance of safety-critical systems (SCSs) nowadays, much like other complex problems. Safety is the topmost priority in SCS, hence, developers who are working in this area must possess extensive knowledge of both ML and SCS. This article presents a methodical investigation that surveys engineering students and professionals in the industry to identify the disparities between the knowledge of students and the industry’s expectations during interviews with undergraduate (UG) and postgraduate (PG) students. The research questions (RQs) were developed based on the student’s proficiency in ML and SCSs, as well as the industry’s expertise in these areas. These questions were then analyzed to determine the factors contributing to the knowledge gap. In this study, a rigorous survey was carried out using two sets of questionnaires. The first …