Master of Science in AI-Driven Innovation
理學碩士(人工智能驅動創新)
Programme Aims
The programme is designed to equip students with the essential knowledge, skills, and mindset to become leaders in technological innovation in the AI age. The rapid advancements in artificial intelligence (AI) have created unprecedented opportunities for innovation across industries. The programme places a strong emphasis on AI-driven innovation, focusing on two key dimensions:
- AI as an enabler of innovation – using AI-powered tools and methodologies to enhance the process of designing new materials, devices, products, processes, services, and systems for innovation.
- AI as an intelligent feature in innovation – integrating AI as a core intelligent function in new products, processes, services, and systems for innovation.
Programme Intended Learning Outcomes (PILOs):
- Leverage AI as both an enabler and a functional component of innovation to design innovative technologies, products, services, and systems.
- Apply systems thinking and design science principles to tackle complex innovation challenges in dynamic, technology-driven environments.
- Develop entrepreneurial and intrapreneurial skills, integrating AI and data-driven decision-making to create and scale innovative ventures or corporate innovations.
- Critically assess the societal, ethical, and economic impact of AI-driven innovation, proposing responsible and sustainable innovation strategies.
- Lead interdisciplinary collaboration across AI, engineering, business, and policy fields to drive technological innovation and organizational transformation.
Programme Structure and Contents
Required Core Courses (12 credit units)
| Course Code | Course Title | Credit Units |
|---|---|---|
| SYE6012 | Technological Innovation and Entrepreneurship | 3 |
| SYE6302 | Design Science | 3 |
| SYE6601 | Introduction to Artificial Intelligence: Concepts and Applications | 3 |
| SYE6602 | AI-Driven Innovation: Seminars and Projects | 3 |
Programme Electives (18 credit units)
| Course Code | Course Title | Credit Units |
|---|---|---|
| SYE5006 | Operations Management | 3 |
| SYE5009 | Industrial Marketing Management for Engineers | 3 |
| SYE5010 | Engineering Management Principles and Concepts | 3 |
| SYE6009 | Project Management | 3 |
| SYE6015 | Supply Chain Management | 3 |
| SYE6037 | Managing Strategic Quality | 3 |
| SYE6050 | Engineering Economic Analysis | 3 |
| SYE6053 | Business Process Improvement and Innovation | 3 |
| SYE6102 | Managerial Decision-Making Systems with Artificial Intelligence | 3 |
| SYE6103 | Financial Engineering for Engineering Managers | 3 |
| SYE6105 | Risk and Decision Analysis | 3 |
| SYE6106 | Intelligent Manufacturing for Engineering Managers | 3 |
| SYE6110 | Data Analysis and Artificial Intelligence for Systems Engineering | 3 |
| SYE6610 | AI Innovation Internships | 3 |
| SYE6612 | The Fourth Industrial Revolution | 3 |
| SYE6620 | AI-Based Media Entrepreneurship | 3 |
| SYE6621 | Agentic AI for Innovation | 3 |
| CAI6002 | Venture Creation Seminar | 3 |
| SM5345 | Introduction to Digital Processes: From Creative Computation to Fabrication | 3 |
| SM5354 | Design Thinking and Innovation in Media | 3 |
| IS5113 | AI Ethics and Regulations | 3 |
| IS5542 | Generative Artificial Intelligence for Business | 3 |
| IS6423 | Artificial Intelligence for Business Applications | 3 |
| IS6620 | Large Language Model with Prompt Engineering for Business | 3 |
| SDSC6004 | Data Analytics for Smart Cities | 3 |
| SDSC6016 | Predictive Analytics and Financial Applications | 3 |
| SDSC8007 | Deep Learning | 3 |
| SDSC8009 | Data Mining and Knowledge Discovery | 3 |
| EE5434 | Machine Learning for Signal Processing Applications | 3 |
| EE5437 | Internet of Things Technologies for Future City Applications | 3 |
| EE5438 | Applied Deep Learning | 3 |
| EE5606 | Artificial Intelligence for Antennas in Wireless Communication | 3 |
| EE6435 | Multi-Dimensional Data Modeling and its Applications | 3 |
| EE6621 | Computational Physiology and Neural Systems | 3 |
| MNE6001 | CAD/CAM Integration | 3 |
| MNE6002 | Computer Controlled Systems | 3 |
| MNE6007 | Advanced Automation Technology | 3 |
| MNE6126 | Sensors for Robotics, AI and Control Systems | 3 |
| MNE6128 | Advanced Machine Learning and Quantum Computation for Engineering | 3 |
| NS5007 | Human and Artificial Intelligence | 3 |
| NS5009 | Ethical Application of Artificial Intelligence in Biological Sciences and Healthcare | 3 |
| NS6002 | Advanced Computational Neuroscience | 3 |
| BME5110 | Biomedical Engineering Design | 3 |
| BME6135 | Engineering Principles for Drug Delivery | 3 |
| BME6138 | Robotics in Minimally Invasive Healthcare | 3 |
| BMS5010 | AI in Health Science Research & Management | 3 |
| BMS5011 | Wearable Technologies & Health Science Research | 3 |
| BMS8110 | Genomics and Bioinformatics | 3 |
| PH5101 | Health Economics and Outcomes Research | 3 |
| PH5105 | Basic Biostatistics in Public Health | 3 |
| PH5106 | Fundamentals of Epidemiology | 3 |
| PH6202 | Infectious Disease Epidemiology | 3 |
| PH6204 | Public Health Surveillance & Risk Analysis | 3 |
Remark: These elective courses will be offered subject to the availability of resources.
Total Credit required for the MSc Programme: 30
Admission Requirements
Applicants must be a Bachelor’s degree holder. All disciplines are welcome.
English Proficiency RequirementsApplicants whose entrance qualification is obtained from an institution where the medium of instruction is NOT English should also fulfill the following minimum English proficiency requirement:
- a score of 79 (Internet-based test) in the Test of English as a Foreign Language (TOEFL)@#; or
- an overall band score of 6.0 in International English Language Testing System (IELTS)@##; or
- a score of 450 in the new College English Test (CET6) of China mainland or a pass in the old CET6 test; or
- other equivalent qualifications.
@TOEFL and IELTS scores are considered valid for two years. Applicants are required to provide their English test results obtained within the two years preceding the start of the University's application period. 'TOEFL iBT Home Edition', ‘TOEFL MyBest Score’, ‘IELTS Indicator’, ‘IELTS One Skill Retake’ and ‘IELTS Online’ are not acceptable.
#Applicants are required to arrange with the Educational Testing Service (ETS) to send their TOEFL results directly to the University. The TOEFL institution code for CityUHK is 3401.
##Applicants are required to arrange for sending their IELTS result(s) via the IELTS Results Service e-delivery to the University. Please note that applicants with an overall band score of 6.0 will be required to pass an interview for English proficiency conducted by the concerned academic unit. Interviews for other applicants may not be required.
Tuition Fees
HK$12,000 per credit (for local and non-local students admitted in 2026/27)
Total credit units required: 30
Duration of study
| Normal Period | Maximum Period |
| Full-time (1 year) | FT (2.5 years) |
| Part-time (2 years) | PT/combined mode (5 years) |
Contact Us
Programme Leader General EnquiriesEmail: sye.office@cityu.edu.hk
For application enquiries, please contact School of Graduate Studies (SGS) at tpadmit@cityu.edu.hk
For visa matters, please contact Global Engagement Office (GEO) at geoins@cityu.edu.hk
Last modified on 4 May, 2026