Programme syllabus
CAIP™ syllabus, learning outcomes & assessment
The Certified Cognitive AI Professional (CAIP™) programme is built on seven modules aligned to Hong Kong policy and leading global frameworks. Below are the course modules, what you will be able to do after each, and how you will be assessed.
At a glance
Programme structure
- Total duration
- No less than 21 hours: at least 18 hours of live, in-person lecture plus 3 hours of proctored examination.
- Delivery
- Face-to-face live teaching with certified trainers — interactive discussion, case work and governance exercises.
- Modules
- Seven modules with equal weighting, each mapped to authoritative standards and reference frameworks.
- Language
- Courses are offered in English, Cantonese and Mandarin subject to cohort arrangements.
Modules
Course modules & learning outcomes
Each module maps to the Exam Content Outline (ECO). On completion of a module, you will be able to:
1. Hong Kong AI Policy & Practice
HK DPO Ethical AI Framework, GenAI Application Guidelines, OPCPD Model Data Protection Framework
Learning outcomes
- Interpret the Hong Kong SAR Government's AI guidance, including the DPO Ethical AI Framework and Generative AI Application Guidelines
- Apply the OPCPD Model Data Protection Framework to AI systems handling personal data
- Advise an organisation on compliant AI adoption under Hong Kong policy instruments
2. Global AI Regulations
EU AI Act and regulatory standards from major economic zones
Learning outcomes
- Explain the risk-based structure of the EU AI Act and its obligations by system category
- Compare regulatory approaches across major economic zones
- Assess cross-border compliance exposure for enterprise AI deployments
3. AI Lifecycle & Methodology
Global AI Project Methodology (CPMAI)
Learning outcomes
- Apply the CPMAI methodology to plan and manage AI projects end to end
- Identify the data, model and evaluation gates in a trustworthy AI lifecycle
- Recognise common failure modes in AI project delivery and how to prevent them
4. Global AI Governance
AI Governance Policies, Regulations and Practices (AIGP)
Learning outcomes
- Design an AI governance framework aligned with AIGP policies, regulations and practices
- Define roles, accountability and oversight structures for responsible AI
- Draft governance artefacts such as AI policies, registers and review checkpoints
5. AI-Driven Project Management
AI-Driven Project Management Best Practices (AIPM)
Learning outcomes
- Lead AI-driven projects using AIPM best practices
- Integrate AI risk, quality and stakeholder management into delivery plans
- Measure and report AI project outcomes to executive sponsors
6. AI Risk Management
NIST AI Risk Management Framework (NIST AI RMF 1.0)
Learning outcomes
- Apply the NIST AI Risk Management Framework (Govern, Map, Measure, Manage) to real systems
- Identify, assess and prioritise AI risks including bias, opacity and misuse
- Define controls and monitoring for residual AI risk
7. International AI Management Systems
ISO/IEC 42001:2023 AIMS Standard
Learning outcomes
- Interpret the requirements of ISO/IEC 42001:2023 for an AI management system (AIMS)
- Map existing organisational processes to AIMS clauses
- Prepare an organisation for AIMS implementation and audit readiness
Assessment
Assessment criteria
- Question format
- Multiple choice. Approximately 50 MC questions conducted progressively (15 questions per 3-hour module).
- Pass mark
- The pass mark is dynamically calculated per module based on item difficulty. Candidates must pass all sections (approx. 70%–80% benchmark per section).
- Exam integrity
- Exams are live and proctored in person, eliminating remote deepfake impersonation and protecting the value of the credential.
- Re-sits
- Candidates who do not pass a module may re-sit that module; re-sit fees are listed on the pricing page.