Bachelor of Engineering - Artificial Intelligence (AI) and Robotics (Foundation Year) (F123)

Designed to provide students with specialized knowledge and skills in Artificial Intelligence (AI) and Robotics, encompassing related technologies such as sensors, actuators, control systems, Internet of Things, software programming, and user-centred design. Students will gain valuable experience through hands-on learning in dedicated laboratories and immersive learning environments.

This course has been meticulously designed to provide students with comprehensive technical knowledge, hands-on experience, and a diverse set of transferable skills. It equips them to excel in industries such as healthcare, finance, manufacturing, and technology, where the demand for highly skilled engineers is growing rapidly. These industries rely on the expertise of engineers to create and implement AI systems, autonomous robots, and cybernetic solutions.

The course focuses on developing proficiency in robotics, sensors, actuators, control systems, networking, and artificial intelligence for various engineering applications. The first two years of the course lay a solid foundation in subjects essential to all engineering disciplines, encompassing Electronic Systems, Programming, AI, Microprocessors, Embedded Systems, and Network Engineering. In the third year, students have the opportunity to specialize in a specific area within AI and Robotics, which includes Applied Robotics, Computer Vision, Bio-inspired AI and Security, Sensors, Actuators, and Control. This year also involves an individual project. The choice of the project topic is not prescriptive, and LMU encourages students to select a subject of mutual interest to both themselves and their supervisor.

Modules are taught through a mix of traditional lectures, interactive tutorials, lab-based workshops, and blended learning methods. Students can access teaching materials and supplementary resources through the university's Virtual Learning Environment (VLE) network, including platforms like WebLearn and Blackboard, as well as the school's web server. LMU employs various effective blended learning approaches and technologies to enhance and support student learning.

The course employs a variety of assessment methods, including class tests, theory and practical examinations, coursework (via logbooks, case studies, laboratory reports, posters, and artifacts), and viva examinations. Detailed information regarding the assessment methods and marking criteria for each module at every level is readily available in the respective 'Module Guide.' These guides are accessible to students at the beginning of each semester through WebLearn, the Virtual Learning Environment (VLE).

Every module, whether core or optional, maintains a presence on the VLE, offering students a wealth of comprehensive learning and teaching materials, including workshop exercises. Module leaders actively use this platform to engage with students, providing valuable feedback, guidelines for technical report writing and effective presentations, and supporting the maintenance of logbooks.