The following courses for secondary students — from basics to advanced — develop AI development and application skills for future AI talent.
This course explores core principles, advanced applications and far-reaching social impact of generative AI. Through interaction with multimodal generative AI models (e.g. large language models, diffusion models), students learn advanced prompt engineering techniques to complete more complex creative, analytical and problem-solving tasks.
Use the latest AI platforms and APIs, keeping pace with technology. Projects include AI writing assistants, image generators, chatbots and other practical apps. Explore AI's impact on society, economy and ethics in depth.
This course guides students from fundamentals to advanced generative AI theory and practice, exploring its profound impact on society and human life from multiple angles. Interaction with advanced AI models develops efficient human-AI collaboration for competitive advantage in academic research, creative work, scientific exploration and diverse careers.
The course trains both computational and critical thinking. Students learn to design and optimise complex prompts to guide AI content generation, and analyse how AI models work, their strengths and limitations — improving logical reasoning, problem decomposition and critical evaluation. AI ethics is emphasised, covering bias, copyright, deepfakes and privacy, helping students become responsible digital citizens and future leaders with global perspective and social responsibility.
Generative AI's multimodal creative potential spans text, image, audio and more. The course guides cross-domain integration for innovative projects and interdisciplinary innovation. As universities and workplaces value AI literacy, students' practical experience and ethical awareness from this course give a forward-looking edge in further study and careers.
Covering core AI theory, programming fundamentals and practical applications, this course guides students from basic understanding to hands-on practice, building a systematic AI knowledge framework. Combining theory and practice, students use mainstream programming languages and libraries to design and implement smart applications that can 'learn', judge and execute complex tasks — experiencing AI's diverse real-world applications firsthand.
The course develops advanced computational thinking, logical problem-solving and innovative collaboration. Students master basic AI programming syntax, data structures, functions and modular design, and understand principles and practice of machine learning, image processing, NLP and other frontier AI technologies. Through diverse projects — smart game agents, data analysis tools, simple image recognition systems and text sentiment analysers — students turn creative ideas into concrete outcomes, consolidating knowledge and building practical skills.
Advanced computational thinking and logical problem-solving are emphasised — students learn to break complex smart tasks into executable program logic, design and implement algorithms, and build systematic thinking and problem-solving ability.
Students systematically learn AI fundamentals and practice, laying a solid, comprehensive foundation for advanced computer science, machine learning and data science. Understanding how AI works develops sensitivity to frontier technology and maintains leading advantage in academic research and professional development.
The course also inspires innovation and practical ability. Students write AI programmes, turning ideas into applications such as recommendation systems, image recognition tools or smart game agents, building innovation and hands-on experience. Students also reflect on AI ethics and social impact — bias, privacy, copyright — building responsible digital citizenship and ethical judgment for future leadership.
This course guides students from basic theory to applied practice through the complete large language model development workflow, preparing future AI talent. Students explore core principles of large language models (LLMs) and foundational natural language processing (NLP) methods. They learn to build, deploy and apply LLM models, understand how computers 'understand' and 'generate' human language, and gain initial exposure to real-world applications such as smart chatbots and text analysis. The course aims to develop advanced computational thinking, language logic analysis and AI practical skills.
Through LLM and NLP fundamentals and practice, secondary students master core AI technologies and participate in language model development and application, broadening horizons and preparing for the future technology era.
The course systematically covers the full workflow from data collection and corpus cleaning through model training to deployment, helping students understand how language models analyse and generate text and familiarise themselves with basic programming for related applications. It emphasises computational thinking and logical problem-solving — students learn to break complex language tasks into clear steps and design and implement algorithms, essential for future STEM study and digital society challenges.
In the practical phase, students develop LLM-based smart applications such as Q&A systems, text classifiers or simple chatbots, turning ideas into real outcomes and strengthening innovation and hands-on skills. The course also covers basics of model deployment and performance optimisation, helping students understand the full AI project lifecycle from development to application. Students are encouraged to consider ethical and social issues such as data privacy, bias and responsibility, building good digital citizenship.
The following courses for secondary students cover AI vision, Python programming, database management and AR development — developing technical ability comprehensively.
Combining Micro:bit and HuskyLens for AI vision learning, students practise image recognition, object tracking and face recognition and apply these to autonomous robot navigation, interactive tasks and IoT smart environment construction.
Systematic Python learning aligned directly with DSE ICT requirements. Python is the language of the AI era — mastering Python means mastering the future.
Through simulated real database competitions, project challenges and case analysis, students systematically master core SQL concepts and advanced query techniques.
Built around AR.js augmented reality, combined with HTML, JavaScript and the Three.js 3D library, letting students create interactive AR experiences in the browser.
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