Design a course curriculum for AI for Hugh School

Okay, here's a possible course curriculum for AI for High School. This curriculum aims to provide a conceptual understanding of AI, practical skills, and an awareness of its societal impact.

AI for High School: Unveiling Artificial Intelligence 🤖

Course Description: This course will introduce students to the fascinating world of Artificial Intelligence. Students will explore the fundamental concepts of AI, learn about its various applications, and discuss its ethical implications. The course will involve hands-on activities and projects, allowing students to experience AI in action. No prior programming experience is strictly required, but a curious mind and a willingness to learn are essential!

Course Goals:

• Understand the basic concepts and history of Artificial Intelligence.
• Identify and describe different types of AI and machine learning.
• Explore real-world applications of AI in various fields.
• Develop basic skills in using AI tools and platforms (no-code or low-code focus).
• Critically evaluate the ethical and societal impacts of AI.
• Foster problem-solving and critical-thinking skills through AI-related projects.

Module 1: What is AI? - The Big Picture 🖼️

• Topics:
  • Defining Artificial Intelligence: What it is and what it isn't.
  • A Brief History of AI: Key milestones and pioneers.
  • Types of AI: Narrow (Weak) AI, General (Strong) AI, Superintelligence.
  • AI in Everyday Life: Recognizing AI around us (recommendation systems, voice assistants, etc.).
  • Why Study AI?: Future career opportunities and societal relevance.
• Activities:
  • Brainstorming AI applications.
  • "Turing Test" discussion and simulation.
  • Researching a historical AI breakthrough.
• Project Idea: Create a presentation or infographic explaining AI to a younger student.

Module 2: How Does AI "Learn"? - Introduction to Machine Learning 🧠

• Topics:
  • Introduction to Machine Learning (ML): The core idea of learning from data.
  • Types of Machine Learning:
    • Supervised Learning (classification, regression) - with simple analogies.
    • Unsupervised Learning (clustering) - with simple analogies.
    • Reinforcement Learning - with simple analogies (e.g., training a pet).
  • The Importance of Data: "Garbage in, garbage out." Bias in data.
  • Training and Testing Models: A conceptual overview.
• Activities:
  • Interactive simulations of ML concepts (e.g., Teachable Machine by Google).
  • Analyzing datasets for potential bias.
  • Categorizing examples of ML problems.
• Project Idea: Use a tool like Teachable Machine to train a simple image or sound classifier.

Module 3: AI in Action - Exploring Applications 🚀

• Topics:
  • Computer Vision: How AI "sees" (image recognition, object detection, facial recognition).
  • Natural Language Processing (NLP): How AI "understands" language (chatbots, machine translation, sentiment analysis).
  • Robotics: AI in physical systems (autonomous robots, drones).
  • AI in Creative Arts: AI-generated music, art, and writing.
  • AI in Specific Industries: Healthcare, finance, transportation, entertainment, etc.
• Activities:
  • Experimenting with online AI tools for image generation or text analysis.
  • Designing a concept for an AI application to solve a school or community problem.
  • Watching and discussing videos showcasing cutting-edge AI applications.
• Project Idea: Research and present on how AI is transforming a specific industry of interest.

Module 4: The Building Blocks - Data and Algorithms (Gentle Introduction) 🧱

• Topics:
  • What is Data?: Types of data (numbers, text, images, sound).
  • Data Collection and Preparation: Why it's important.
  • Introduction to Algorithms: What they are and how they relate to AI (simple, non-coding examples).
  • Flowcharts and Pseudocode: Visualizing problem-solving steps.
  • (Optional) Introduction to a beginner-friendly programming concept if time and student interest allow (e.g., basic Python syntax for AI libraries, or block-based coding for AI).
• Activities:
  • Creating a flowchart for a simple daily task.
  • Working with a sample dataset to identify patterns.
  • Exploring how algorithms power recommendation systems.
• Project Idea: Design an algorithm (using a flowchart or pseudocode) for a simple AI task, like a basic recommendation system for movies or books.

Module 5: AI and Society - The Ethical Landscape 🤔

• Topics:
  • Bias in AI: How it happens and its consequences (fairness, discrimination).
  • Privacy Concerns: Data collection and surveillance.
  • Job Displacement and the Future of Work: How AI might change employment.
  • Accountability and Transparency: Who is responsible when AI makes a mistake? The "black box" problem.
  • The Ethics of Autonomous Systems (e.g., self-driving cars).
  • Responsible AI Development and Use.
• Activities:
  • Debates on ethical dilemmas in AI.
  • Analyzing case studies of AI bias or misuse.
  • Developing a set of ethical guidelines for AI development.
• Project Idea: Write an essay or create a short video discussing a major ethical challenge posed by AI and proposing potential solutions.

Module 6: The Future of AI - Opportunities and Challenges 🔮

• Topics:
  • Current Trends in AI Research: What's new and exciting?
  • The Path to Artificial General Intelligence (AGI): Possibilities and uncertainties.
  • AI for Good: How AI can help solve global challenges (climate change, disease, poverty).
  • Lifelong Learning in the Age of AI.
  • Careers in AI and related fields.
• Activities:
  • Researching and presenting on an emerging AI technology.
  • Brainstorming "AI for Good" project ideas.
  • Guest speaker (virtual or in-person) working in the AI field.
• Project Idea: Develop a proposal for an "AI for Good" project, outlining the problem, how AI could help, and potential ethical considerations.

Final Project (Culminating Experience) 🏆

Students can choose from a variety of projects, potentially drawing from their work in previous modules, such as:

• Developing a more advanced application using a tool like Teachable Machine or other no-code/low-code AI platforms.
• Creating an in-depth research report on a specific AI topic, application, or ethical issue.
• Designing and prototyping (e.g., through mockups or storyboards) an innovative AI-powered solution to a real-world problem.
• Producing a public service announcement or educational campaign about a key aspect of AI (e.g., bias, privacy).

Assessment:

• Class Participation and Discussions
• Module Activities and Smaller Assignments
• Quizzes on Key Concepts
• Module Projects
• Final Project

Tools and Resources (Examples):

• Google's Teachable Machine: For hands-on ML model training without code.
• MIT App Inventor: For building mobile apps with AI components (block-based coding).
• Various online AI tools: AI image generators (e.g., Craiyon), chatbots (e.g., Character.ai for understanding, not for generating unsupervised content), NLP tools.
• Educational websites and videos: Code.org, AI4K12.org, YouTube channels explaining AI concepts.
• News articles and documentaries on AI.

This curriculum is a template and can be adapted based on student interest, available time, and resources. The key is to foster curiosity, critical thinking, and a foundational understanding of this transformative technology.