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Cultivating AI Mindset

AI Training Curriculum to Students for After School Club / ECA

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Course Delivery
Onsite Face-to-Face
Language
English or Chinese
Suitable
Elementary School, Middle School, High School

Overall Aim:
A K-12 curriculum designed under the IB framework that empowers students with a deep understanding of Large Language Models (LLM) and multimodal AI capabilities. The curriculum progresses from conceptual exploration and interactive visual tools (MYP) to code-level practice and real-world application development (DP).

Exploring the World of AI  (Middle School / MYP)

(16-20 weeks, 90-minute sessions)

  • Weeks 1-4

    Foundations & Interactive Exploration

    • Introduction to AI & LLM:
    • Understand core AI concepts and explore everyday applications.
    • Learn how Large Language Models acquire language proficiency from vast data sets.
    • Interactive Tools:
    • Engage with visual platforms (e.g., Machine Learning for Kids) to observe model training and behavior.
    • Participate in activities that illustrate pattern recognition and language prediction.

  • Weeks 5-8

    Mastering Prompt Engineering 

    • Prompt Engineering:
    • Develop foundational skills in designing effective prompts.
    • Advance to techniques such as Few-shot and Chain-of-Thought prompting for improved output control.
    • Applications in Writing & Translation:
    • Conduct hands-on exercises with tools like ChatGPT and DeepSeek.
    • Generate creative writing, summaries, and translations to understand practical uses of LLMs.

  • Weeks 9-12

    Expanding Applications & Ethical Considerations

    • Broader AI Applications:
    • Explore AI-assisted coding, creative storytelling, and automated information summarization.
    • Analyze real-world scenarios to see how AI augments various learning tasks.
    • Ethics & Safety:
    • Discuss AI bias, misinformation risks, and the importance of responsible usage.
    • Engage in debates and case studies to develop critical perspectives on ethical AI.

  • Weeks 13-16

    Multimodal Innovation 

    • Image Generation: Experiment with text-to-image tools (e.g., DALL·E, Stable Diffusion) to generate visual content.
    • Voice & Music Synthesis: Practice using Text-to-Speech (TTS) tools and explore AI-driven music creation.
    • Video Generation: Examine the challenges and potentials of AI-generated video content.

  • Capstone Project

    • Collaboratively design and develop an AI assistant prototype that integrates multimodal outputs—text, images, voice, and video.
    • Present projects to peers, highlighting the design process, technical challenges, and ethical considerations.

Advanced LLM Application Development

(High School / DP)

(16-20 weeks, 90-minute sessions)

  • Weeks 1-4

    LLM Fundamentals & Advanced Prompting

    • Course Introduction & LLM Overview:

    • In-depth exploration of Transformer architectures, training processes, and data challenges.

    • Discussion on the evolution and current state-of-the-art in LLMs.

    • Advanced Prompt Engineering:

    • Learn sophisticated techniques to optimize LLM responses in complex scenarios.

    • Analyze real-world examples and experiment with different prompt formulations.

  • Weeks 5-8

    Coding, API Integration & Retrieval-Augmented Generation (RAG)

    • Coding & API Integration:

    • Hands-on sessions using Python to interact with LLM APIs (e.g., OpenAI).

    • Focus on parameter tuning, error handling, and output evaluation.

    • RAG (Retrieval-Augmented Generation):

    • Understand how to combine real-time data retrieval with LLM generation to enhance answer quality.

    • Implement prototype workflows that integrate external knowledge sources.

  • Weeks 9-12

    Model Fine-Tuning & Capstone Project Planning

    • Model Fine-Tuning:

    • Explore concepts and practical techniques for fine-tuning a pre-trained LLM for specialized tasks.

    • Engage in hands-on exercises, including data preparation and iterative training.

    • Team Project Planning:

    • Initiate a capstone project where teams propose a real-world AI application leveraging LLMs.

    • Develop project proposals covering technical approach, ethical considerations, and feasibility.

  • Weeks 13-16

    Multimodal Integration

    • Image & Visual Content Generation:

    • Integrate APIs like DALL·E or Stable Diffusion to generate dynamic visual content.

    • Audio & Video Synthesis:

    • Develop skills in text-to-speech (TTS) and explore basic video creation/editing techniques.

  • Capstone Project

    • Teams develop and refine a comprehensive AI application that integrates text, image, voice, and video outputs.
    • Final presentations include a live demo, detailed documentation, and a project defense session.
Rhoda Chen

Rhoda Chen

Contact Rhoda for Immediate support and information

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