- YING WANG
Intermediate
Event Organizer(s)
Description
This course focuses on digital-intelligent convergence and security, integrating AI, data governance, and digital transformation as three core pillars. It covers foundational AI technologies (computer vision, generative AI), macro trends, and whole-lifecycle data governance (quality control, privacy protection). Practical scenarios include energy digitalization and digital government, with systematic methodologies for AI application planning. Additionally, it interprets international/domestic AI governance frameworks, evaluation standards, and frontier safety-ethical issues. Through theoretical interpretation, case analysis, and practical drills, the course equips policy-makers, technical managers, and industry practitioners with capabilities to grasp trends, design governance strategies, and promote compliant digital transformation, contributing to an ethical and sustainable global digital ecosystem.
This training is primarily targeted at policy-makers, regulators, technical managers, and industry practitioners who are engaged in or responsible for strategies and implementations related to artificial intelligence, data governance, and digital transformation. Other institutions and individuals interested in building strategic capacity and practical insights into the governance, security, and application of emerging digital technologies are also welcome to participate.
Participants are expected to have a foundational understanding of information and communication technology (ICT) concepts, and professional or academic interest in at least one of the core domains: digital transformation, artificial intelligence, or data governance.
Selection criteria:
- Preference of having learnt from previous courses about AI offered by the ITU academy or other resources .
Number of available places for the cohort:
- Max 80-100 participants
Upon completion of this course, participants will be able to:
- Describe the strategic global trends and practical pathways of digital transformation, as demonstrated in key sectors like energy and digital government.
- Analyze the capabilities, risks, and governance frameworks for emerging AI technologies, including generative AI and foundation models, from both technical and policy perspectives.
- Design foundational strategies for data governance and AI application planning that support secure, ethical, and effective digital-intelligent convergence in their respective contexts.
Participants self-learning studies: The participants are required to read and watch the course materials uploaded on the platform;
Tutor-led live and chat zoom sessions: Tutor will deliver live sessions and respond any questions raised from participants;
Participants forum: participants and tutors will interact through the ITU Academy forum platform to discuss relative course topics and express their opinions and submit questions;
Knowledge check: participants are required to take quizzes which will be organized at the end of each week to check their knowledge achievement. The assignment also needs to be submitted at the end of the training course to assess their understanding of the given topic.
Course Materials: The relevant course material will be made available on the website, which will include presentations and recorded video with explanation of key topics. During every week, live sessions will be held, the tutors will brief or highlight some important parts of their course’s presentations.
Online Discussion Forums: Participants are expected to participate actively in discussion forums on selected topics throughout the week. Tutors will respond to the posts and interact with participants
Chat Sessions: Online chat sessions with the tutor will take place 2-3 times during the two weeks’ time. All participants are expected to join the chat sessions to interact with tutors. The specific time will be sent to participants in advance by tutors.
Quizzes: 2 mandatory online quizzes will be launched at the end of each week and required to submit before the announced deadline.
Assignment: There will be a mandatory assignment at the end of the course, which participants are required to submit them before the announced deadline.
Quiz 1: 15%
Quiz 2: 15%
Individual Assignment: 40%
Discussion Forum 1: 10%
Discussion Forum 2: 10%
Active Participation (live and chat sessions): 10%
Total: 100%
A total score of 70% or higher is required to obtain the ITU certificate .
WEEK 1
1. Data Intelligent Industries: Driving Real-Economy Transformation
Learning Outcomes:
- Describe the foundations and technologies of the data-intelligent industry, as well as its critical role in enhancing applications across various fields.
- Explain typical applications of data intelligence in different core scenarios, detailing how it optimises industrial processes, improves service efficiency, and activates the development vitality of MSMEs.
- Clarify the core pathways through which data intelligence empowers the real economy, and the key points of its synergy with data governance and Digital Public Infrastructure (DPI).
- Consider collaboration opportunities with cross-sectoral and international stakeholders to promote the precise alignment of data-intelligent technologies with specific scenarios.
2. AI-Oriented Data Governance: Lifecycle Management & Risk Control
Learning Outcomes:
- Explain the core connotation and unique challenges of AI-oriented data governance, and distinguish its differences from traditional data governance.
- Compare industry best practices for AI data governance (including dataset quality assessment, trusted data circulation, and algorithmic data compliance).
- Grasp practical methods for the entire lifecycle of data governance for AI (data collection/labeling, quality control, privacy-preserving computing, and post-deployment supervision).
- Acquire the ability to identify and address common risks in data governance for AI (e.g., data bias, privacy leakage, non-compliance with regulations) through case analysis).
3. AI-Empowered Object Perception & Scene Understanding
Learning Outcomes:
- Master core theories of AI and computer vision, including three major machine learning types, classic CV tasks, and key evaluation metrics.
- Distinguish the working principles and application scenarios of core CV architectures and grasp multi-modal object perception logic.
- Apply theoretical knowledge to analyze practical problems in typical scenarios.
4. AI Application Planning and Scenario Design: Turning Advanced Models into Real-World Impact
Learning Outcomes:
- Outline the core principles of AI application planning and scenario-based design for digital transformation
- Identify and prioritize high-value AI application scenarios aligned with organizational, business, or policy objectives
- Assess data readiness, implementation feasibility, and governance requirements for AI applications
- Translate advanced artificial intelligence and large-scale model capabilities into structured, deployable solutions
5. Imagine the Future — How Will AI Affect Our Lives?
Learning Outcomes:
- Recognize the synergy between AI and the energy revolution as the core driver of technological disruption.
- Summarize the way in which education models, chip development, and the global AI landscape are undergoing dynamic realignment.
- National competitiveness must be rebuilt on a dual-track strategy of energy independence and intelligent transformation.
WEEK 2
6. Generative AI in Practice: Opportunities, Risks, and Governance Challenges
Learning Outcomes:
- Outline the core concepts and technological characteristics of generative AI
- Identify key application scenarios of generative AI across industries and public services
- Recognize major risks and challenges associated with generative AI deployment
- Apply basic risk mitigation and governance strategies throughout the AI lifecycle
- Compare global perspectives on responsible and sustainable adoption of generative AI
7. From Model to Market: AI Governance, Evaluation Standards, and Foundation Model Registration in Practice
Learning Outcomes:
- Examine why foundation models require new regulatory approaches
- Explain the role of AI evaluation and testing standards in managing model capability, safety, and risk
- Identify key elements of large model assessment, including performance, robustness, security, and alignment
- Recognize the purpose and structure of AI model registration/filing mechanisms and how they operate in practice
- Analyze AI systems from a governance perspective and communicate effectively across policy, legal, and technical teams
- Apply governance principles to real-world AI deployment scenarios in an international context
8. Building Ethical, Inclusive, and Sustainable Futures: Frontier AI Safety, Ethical Risks, and Global Governance
Learning Outcomes:
- Risk Identification: Ability to identify and categorize frontier AI safety risks (e.g., misuse, misalignment) and ethical dilemmas in Generative AI.
- Global Governance Comparison: Gain a comparative perspective on the latest AI governance frameworks in the EU, UN, and specifically China’s regulatory innovations.
- Practical Compliance Strategy: Master the operational steps for AI Safety Assessment and Ethical Review, learning from real-world best practices in China’s tech industry to ensure sustainable development.
9. Digital Transformation Practices and AI Application Reflections in the Energy Sector
Learning Outcomes:
- Gain a global perspective on energy digitalization strategies and policy trends
- Identify critical success factors for energy companies' digital transformation，and apply these insights to other sectors
- Analyse enterprise-level AI application requirements and pathways
10. Digital Transformation and AI-Enabled Public Services and Governance
Learning Outcomes:
- Explain key concepts, models, and global trends in digital government and AI-enabled public governance
- Analyze the role of governments as platform builders, system integrators, and regulators in the digital economy
- Assess policy and institutional prerequisites for digital identity systems, interoperable data-sharing frameworks, and AI-supported public services
- Evaluate international case studies and identify transferable practices suitable for developing country contexts
- Design high-level policy roadmaps and implementation strategies for digital transformation and AI governance aligned with national development priorities
- Identify governance, ethical, and risk management considerations related to the use of AI in the public sector
Final Assignment
Until 10 June 2026
- There will be an assignment required to be submitted after all the training courses with an extended 2 weeks’ time.
Registration information
Unless specified otherwise, all ITU Academy training courses are open to all interested professionals, irrespective of their race, ethnicity, age, gender, religion, economic status and other diverse backgrounds. We strongly encourage registrations from female participants, and participants from developing countries. This includes least developed countries, small island developing states and landlocked developing countries.
