COURSE CURRICULUM

The Master of Science in Human Factors and Cognition at Lawrence Technological University offers a comprehensive curriculum that combines core coursework, research experience, and a wide array of electives to prepare students for careers in human-centered design, usability, and cognitive systems engineering. In the first year, students build foundational knowledge in human factors and cognitive psychology while developing critical research and statistical skills. The fall semester includes courses in Human Factors and Cognitive Psychology, alongside one elective, totaling nine credits. In the spring, students study Research Design and Statistics and Sensation and Perception, complemented by an elective, also totaling nine credits. In the second year, students apply their learning through a two-part research project spanning the fall and spring semesters, with each semester offering six credits composed of three credits for the research project and three credits for an elective. This structure emphasizes practical, hands-on research experience while allowing students to tailor their studies to their interests.Eligible electives provide extensive opportunities to deepen expertise in areas related to human-computer interaction, design, engineering, and artificial intelligence. Students may choose from courses such as Psychophysiology for Human-Centered Computing, Human-Computer Interaction, Design Leadership, Emerging Visual Communication, and Design Methods. Additionally, electives in technology and AI, including Deep Learning for Engineers, Software Development for Artificial Intelligence, Digital Signal Processing, Machine Learning/Pattern Recognition, Computer Vision, and Cybersecurity, allow students to integrate advanced computational and engineering skills with human factors principles. The flexibility to select electives across psychology, design, and engineering enables students to create a personalized learning pathway aligned with career goals in human-centered system design, cognitive research, usability testing, interface design, and applied AI. By combining rigorous core courses, research projects, and a broad selection of electives, the program ensures that graduates are equipped to design safer, more intuitive, and efficient systems that enhance human performance and interaction with technology.