Automation is one of the key areas for modern manufacturing systems. It requires coordination of different machines to support manufacturing operations in a company. Recent studies show that there is a gap in the STEM workforce preparation in regards to highly automated production environments. Industrial robots have become an essential part of these semi-automated and automated manufacturing systems. Their control and programming requires adequate education and training in robotics theory and applications. Various engineering technology departments offer different courses related to the application of robotics. These courses are a great way to inspire students to learn about science, math, engineering, and technology while providing them with workforce skills. However, some challenges are present in the delivery of such courses. One of these challenges includes the enrollment of students who come from different engineering departments and backgrounds. Such a multidisciplinary group of students can pose a challenge for the instructor to successfully develop the courses and match the content to different learning styles and math levels. To overcome that challenge, and to spark students\u27 interest, the certified education robot training can greatly support the teaching of basic and advanced topics in robotics, kinematics, dynamics, control, modeling, design, CAD/CAM, vision, manufacturing systems, simulation, automation, and mechatronics. This paper will explain how effective this course can be in unifying different engineering disciplines when using problem solving related to various important manufacturing automaton problems. These courses are focused on educational innovations related to the development of student competency in the use of equipment and tools common to the discipline, and associated curriculum development at three public institutions, in three different departments of mechanical engineering technology. Through these courses students make connections between the theory and real industrial applications. This aspect is especially important for tactile or kinesthetic learners who learn through experiencing and doing things. They apply real mathematical models and understand physical implications through labs on industrial grade robotic equipment and mobile robots
