The Learning System That Grows With You
Combining interactive lessons and a personal AI tutor to provide real-time guidance that gets smarter the more you learn.
Featured Courses
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My Little AI Agent
Designed for preschool learners, this course transforms complex computer science concepts—such as Agents, evolutionary learning, prompting, and even negative entropy—into concrete, playful, and age-appropriate experiences. Across five lessons, children build foundational AI literacy: understanding what AI is and how it helps people, learning to give clear instructions (prompting), exploring specialized AI "helpers" (agents) and teamwork, discovering how AI learns from data and mistakes, and finally engaging in a guided human–AI co-creation project that promotes creativity, responsibility, and technology-for-good values.
Introduction to Deep Learning
Deep learning is a sub-field of machine learning that focuses on learning complex, hierarchical feature representations from raw data using artificial neural networks. The course covers fundamental principles, underlying mathematics, optimization concepts (gradient descent, backpropagation), network modules (linear, convolution, pooling layers), and common architectures (CNNs, RNNs). Applications demonstrated include computer vision, natural language processing, and reinforcement learning. Students will use the PyTorch deep learning library for implementation and complete a final project on a real-world scenario.
AI Magic Lab
A rigorous course structure integrating four major sections: AI Fundamentals, Large Model Generation (GenAI & LLM), Agents and Evolutionary Computation (highlighted as a PolyU Feature), and Ethics. The course logic progresses sequentially through Perception & Data (L1-3), Cognition & Generation (L4-6), Agents & Evolution (L7-9), and concludes with Ethics & Future (L10).
Prompt Engineering Advanced Guide
A comprehensive advanced guide to mastering AI through structured logic and precise instruction. The course covers structural frameworks (CO-STAR), Few-Shot learning, Chain of Thought reasoning, output format constraints (JSON/Markdown), and prompt system management to resolve issues such as AI hallucinations and poor logical output.
OpenClaw: Architecture, Dev & Security for Local AI Agents
This course provides an in-depth analysis of OpenClaw, a groundbreaking open-source framework for autonomous AI agents. It systematically deconstructs the framework's layered system architecture, local-first RAG memory mechanisms, browser automation protocols, and highly scalable skill ecosystem. The curriculum covers practical orchestration of complex workflows, including PIV automation flows and multi-agent committee patterns. Furthermore, it critically analyzes hardware trade-offs in production-grade deployment paradigms and presents defense-in-depth strategies against core security threats such as RCE vulnerabilities and prompt injection. The course aims to empower senior developers and architects to build AI agent systems that possess high autonomy while remaining secure and controllable.
LLMs for Everyone: From Basics to Practical Use (2026 Edition)
This course is a beginner-friendly, practical introduction to Large Language Models (LLMs) such as ChatGPT and Gemini. Designed for learners from any background, it explains how LLMs work at a high level, what they can and cannot do, and how to use them effectively in study, work, and everyday life. Through hands-on demonstrations and guided exercises, you will learn prompt techniques, how to evaluate outputs critically, how to handle hallucinations and bias, and how to use common tools (e.g., documents, summaries, translation, data tasks) safely and responsibly. By the end of the course, you will be able to build a personal “LLM workflow” for real tasks—writing, research, planning, and productivity—without needing advanced coding skills.
【人教版】初中英语 八年级上册
本教材为中国初中八年级学生设计的英语教科书，通过10个单元的主题教学，涵盖假期、运动习惯、人物性格、生活偏好、娱乐媒体、职业规划、未来预测、食品制作及社交礼仪等核心话题，旨在提升学生的跨文化交际能力和语法应用水平。
Math Readiness
This is a five-lesson early childhood math curriculum designed by an AI Tutor to help children transition from rote memorization to true mathematical logic. Through engaging, play-based activities, the course guides young learners across five progressive modules. It begins by building a strong foundation in number sense and quantity perception, then develops spatial awareness by exploring 2D and 3D geometric shapes. Children also cultivate their observation and early algebraic thinking through logic and pattern classification games. Additionally, the curriculum introduces practical measurement skills using everyday objects and concludes by making abstract time concepts concrete to help establish daily routines and planning.
Maths in Action (Primary 1-3)
This Primary 1 to Primary 3 Mathematics Curriculum is designed to build a solid and comprehensive mathematical foundation for early learners. The syllabus is systematically structured across five core strands: Number, Measures, Shape and Space, Data Handling, and Further Learning. Throughout this stage, students will progress from basic number recognition and arithmetic operations to developing spatial awareness, mastering practical measurement skills, and learning introductory data visualization. Beyond theoretical knowledge, the curriculum emphasizes cultivating logical thinking and problem-solving abilities, encouraging students to apply abstract mathematical concepts to real-world scenarios.
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