AIST2602 Engineering Practicum


Course code AIST2602
Course title Engineering Practicum
Course description This course arranges industrial and professional workshops or seminars as required by the Major programme.
Unit(s) 1
Course level Undergraduate
Semester 2
Exclusion CSCI3250 or CSCI3251 or ENGG2601 or ENGG2602
Grading basis Pass (P) / Fail (U)
Grade Descriptors P – (Ungraded Pass): Performance meets, exceeds or far exceeds expectation in relevant measurement dimensions; Overall level of competence: Moderate to High;
U – (Failure): Performance does not meet expectation in most relevant measurement dimensions; Overall level of competence: Not reaching the basic standard.
Learning outcomes At the end of the course of studies, students will have acquired the ability to
1. Hand-on skills of engineering practice
2. Understanding the value of practical engineering skills and experiences
Assessment Others: 100%
Recommended Reading List 1. E.A. Stephan et al., Thinking like an engineer: an active learning approach, Pearson Prentice Hall, 3rd ed, 2015.
2. The Hong Kong Institution of Engineers web site (
3. Intellectual Property Department web site (
4. The Office of the Government Chief Information Officer (OGCIO) web site (


AISTN programme learning outcomes Course mapping
Upon completion of their studies, students will be able to:  
1. identify, formulate and solve AI-related engineering problems (K/S);    
2. design a system, component, or process to meet desired needs within realistic constraints, such as economic, environmental, social, political, ethical, health and safety, manufacturability and sustainability (K/S/V);
3. understand the impact of AI solutions in a global and societal context, especially the importance of health, safety and environmental considerations to both workers and the general public (K/V);  
4. communicate and work effectively in multi-disciplinary teams (S/V);
5. apply knowledge of mathematics, science, and engineering appropriate to the AI degree discipline (K/S); 
6. design and conduct experiments, as well as to analyze and interpret massive data (K/S);  Y
7. use the techniques, skills, and modern computing tools necessary for engineering practice  appropriate to the AI and computing discipline (K/S); Y
8. understand professional and ethical responsibility (K/V); and Y
9. recognize the need for and the importance of life-long learning (V).
Remarks: K = Knowledge outcomes; S = Skills outcomes; V = Values and attitude outcomes