Courses (Future Genius)

Course Title: Future Genius AI/ML Explorers 2: Unlocking the Power of Intelligent Machines

Age Group: 10+ years

Course Synopsis: This 10-week course provides a practical and engaging exploration of Artificial Intelligence (AI), Machine Learning (ML), and Data Science. Students will learn fundamental concepts through hands-on projects using no-code and low-code tools like Google Teachable Machine, Jupyter Notebooks (Python basics), and AI Chat tools. The course progresses from understanding what AI is and how it learns to training their own AI models, analyzing real-world datasets, creating interactive AI tools, and critically examining the ethical implications of AI. Students will develop computational thinking, data literacy, problem-solving, communication, and teamwork skills, preparing them to be informed and responsible citizens in an AI-driven world.

Tools:

• Google Teachable Machine (web-based visual ML training platform)
• Jupyter Notebooks (web-based Python environment for data analysis and coding – e.g., Google Colab, or local Jupyter installation)
• AI Chat Tools (e.g., ChatGPT, Gemini, or similar accessible LLMs for interaction and project integration)
• Simple Datasets (provided datasets on weather, sports, classroom data examples, etc.)

Session Duration: 1 hour 30 minutes

Week 1: What IS Artificial Intelligence? – Demystifying AI and its Capabilities

• Learning Objective: Define Artificial Intelligence (AI) in simple terms, understand different types of AI (narrow vs. general AI), explore examples of AI in everyday life, and discuss the core concepts of AI and Machine Learning.
• (20 min) Concept Deep-Dive + Examples: What is AI? – Interactive Discussion & Examples
  • Interactive Discussion: Start with the question: “What comes to mind when you hear ‘Artificial Intelligence’?” Brainstorm student ideas and preconceptions about AI.
  • Define AI: Explain AI in age-appropriate language: “Making computers think and learn like humans (in some ways).” Focus on the idea of intelligence demonstrated by machines.
  • Narrow vs. General AI: Differentiate between narrow or “weak” AI (AI for specific tasks, like image recognition, chatbots) and general or “strong” AI (hypothetical AI with human-level intelligence across all domains). Emphasize that current AI is mostly narrow AI.
  • AI Examples in Everyday Life: Explore real-world examples of AI they already use or encounter:
    • Voice assistants (Siri, Alexa, Google Assistant)
    • Recommendation systems (Netflix, YouTube, Amazon)
    • Spam filters
    • Search engines
    • Self-driving car technology (briefly)
    • Image recognition in phones and apps
  • Introduce Machine Learning (ML) as a key approach to AI: “Teaching computers to learn from data without being explicitly programmed.” “AI often uses ML to become ‘smart’.”
  • Example: Show short videos or interactive demos showcasing AI applications and explaining basic AI concepts in a visual and engaging way.
• (35 min) Independent/Collaborative Coding Challenges: “AI Brainstorm & Concept Mapping”
  • Challenge 1 (Brainstorming – Individual): “AI Idea Jar”. Each student individually brainstorms and writes down at least 3 ideas for how AI could be used to solve a problem or make something better in their daily life, school, or community. Put ideas in a virtual or physical “idea jar.”
  • Challenge 2 (Concept Mapping – Pair Programming): Pairs take 2-3 AI examples discussed earlier (or from their brainstormed ideas). Create a simple concept map or diagram for each example, identifying:
    • What is the AI doing? (Task)
    • What kind of data might it use? (Data Input)
    • What is the output or result? (Output/Prediction)
    • Is it narrow or general AI? (Type)
  • Challenge 3 (Team Discussion): Teams share their concept maps and discuss: What common elements do they see across different AI examples? What are the key characteristics of AI?
• (25 min) Real-world Problem-Solving (Mini-Project): “AI in My School/Community – Problem Identification”
  • Mini-project: “AI in My School/Community – Problem Identification”.
  • Challenge: Students work in teams to identify a real-world problem or opportunity in their school or local community that could potentially be addressed or improved using AI.
    • Examples: Improving school lunch ordering process, optimizing bus routes, helping students find resources, making learning more personalized (brainstorm broadly, feasibility is not the primary focus yet).
    • Teams define the problem clearly, identify the needs it addresses, and brainstorm potential AI-based solutions (at a high level, no coding yet).
  • Teams should document their problem definition and initial AI solution ideas.
• (10 min) Peer Feedback + Q&A: “AI Idea Sharing & Defining AI”
  • Teams briefly share their identified problems and initial AI solution ideas.
  • Peer feedback: Positive comments and suggestions on problem relevance and potential AI application.
  • Q&A: Open floor for questions about the definition of AI, types of AI, real-world AI examples, and the feasibility of using AI for different problems.

Week 2: Data Detective – Collecting and Analyzing Classroom Data with Spreadsheets and Simple Visualizations

• Learning Objective: Understand the importance of data for AI/ML, learn to collect simple data, organize data in spreadsheets (or similar tools), and perform basic data analysis and visualization to find patterns in data.
• (20 min) Concept Deep-Dive + Examples: Data is the Fuel for AI – Data Collection & Importance
  • Review from Week 1: AI learns from data. Emphasize that data is the “fuel” for AI and ML. “No data, no AI learning.”
  • Discuss different types of data: Numbers, text, images, sounds, sensor readings – all can be data for AI.
  • Focus on simple, structured data for this week (tabular data that can be in a spreadsheet).
  • Data Collection Methods: Brainstorm simple ways to collect data in the classroom or school environment: Surveys, observations, counting, recording measurements.
  • Data Organization: Introduce spreadsheets (e.g., Google Sheets, Excel) as a tool for organizing data in rows and columns. Explain basic spreadsheet concepts: cells, rows, columns, headers.
  • Basic Data Analysis: Explain simple data analysis tasks: finding averages, sums, maximum/minimum values, identifying trends, and patterns.
  • Data Visualization: Introduce basic charts and graphs (bar charts, pie charts, line graphs) for visually representing data and making patterns easier to see.
  • Example: Show a sample spreadsheet with simple data (e.g., favorite colors of students in the class). Demonstrate basic spreadsheet functions (SUM, AVERAGE, MAX, MIN) and creating a bar chart to visualize the data.
• (35 min) Hands-on Activity: “Classroom Data Collection & Spreadsheet Exploration”
  • Data Collection Activity: As a class, decide on a simple question to collect data about from classmates (e.g., “Favorite type of pet?”, “Number of siblings?”, “Distance from home to school?”).
  • Data Collection: Students collect data from each other (or use pre-provided sample classroom data, if time is limited) and record it in a shared spreadsheet (e.g., Google Sheet).
  • Spreadsheet Exploration: Students explore the spreadsheet interface, learn to enter data, format cells, use basic spreadsheet functions (SUM, AVERAGE, COUNT), and create simple charts (bar chart, pie chart) to visualize their collected data.
  • Guided practice: Teacher guides students through basic spreadsheet operations and chart creation.
• (25 min) Real-world Problem-Solving (Mini-Project): “Data Detective Challenge – Find Patterns in Classroom Data”
  • Mini-project: “Data Detective Challenge”. Using the classroom data they collected and entered into the spreadsheet, students work in teams to:
    • Analyze the data using spreadsheet functions.
    • Create at least 2 different charts or graphs to visualize the data.
    • Identify at least 2 interesting patterns or insights they can find in the data based on their analysis and visualizations. (e.g., “Most popular pet is dogs,” “Average number of siblings is 1.5,” “Distribution of distances from home to school”).
    • Document their findings and visualizations.
  • Teams present their data analysis findings to the class.
• (10 min) Peer Feedback + Q&A: “Data Insights Showcase & Data Importance Discussion”
  • Teams share their data visualizations and insights from the classroom data.
  • Peer feedback: Focus on clarity of visualizations, insightful patterns identified, and effective use of spreadsheets for data analysis.
  • Q&A: Discuss the importance of data quality, limitations of small datasets, how data analysis helps us understand information and make decisions, and the connection between data analysis and Machine Learning (ML needs data to learn patterns).

Week 3: Train Your First AI – Creating an Image Classifier with Google Teachable Machine (No-Code ML)

• Learning Objective: Introduce Machine Learning (ML) through a hands-on, no-code experience using Google Teachable Machine. Students will train their first image classification AI model to distinguish between “cats” and “dogs.”
• (20 min) Concept Deep-Dive + Examples: Introduction to Machine Learning & Image Classification
  • Review from Week 2: AI learns from data. Focus on Machine Learning (ML) as a type of AI that learns from data to make predictions or classifications.
  • Explain Image Classification: What is image classification? (Teaching a computer to recognize and categorize images). Examples: Recognizing cats vs. dogs, identifying objects in photos, medical image analysis.
  • Introduce Google Teachable Machine: Explain it as a no-code tool that makes it easy to train your own ML models, especially for image, sound, and pose classification, without writing any code. Emphasize its user-friendliness and accessibility for beginners.
  • Walkthrough Teachable Machine Interface: Briefly demonstrate the Teachable Machine website interface: Image project type, Class setup (Cat, Dog), Training, Preview, Export.
  • Data Collection for Image Classification: Explain the need for training data (images of cats and dogs) to teach the AI model. Discuss the importance of diverse and representative training data for good model performance.
  • Example: Show a short video tutorial or live demo of training a simple image classifier in Teachable Machine to classify cats vs. dogs. Highlight the steps: Data collection (uploading images), Training, Testing, Exporting (briefly mention exporting, focus on online preview for now).
• (35 min) Hands-on Activity: “Train Your Cat vs. Dog Classifier with Teachable Machine”
  • Teachable Machine Project Setup: Students access Google Teachable Machine website and create a new “Image Project”.
  • Class Setup: Create two classes: “Cat” and “Dog”.
  • Data Collection: Students collect images of cats and dogs. Suggest using online image search (with appropriate filters for safe search and usage rights) or pre-provided image datasets. Emphasize collecting a reasonable number of diverse images for each class (at least 20-30 per class for a basic model). Upload images to Teachable Machine, assigning them to the correct classes (“Cat” or “Dog”).
  • Training: Students train their image classification model in Teachable Machine by clicking the “Train your model” button. Observe the training process.
  • Testing and Preview: Test their trained model using the “Preview” section in Teachable Machine. Use webcam to show new images of cats and dogs (or upload test images) and see how well the model classifies them. Observe the confidence scores.
• (25 min) Real-world Problem-Solving (Mini-Project): “Improve Your Cat vs. Dog Classifier – Data & Iteration”
  • Mini-project: “Improve Your Cat vs. Dog Classifier”.
  • Challenge: Test their initial cat vs. dog classifier more thoroughly. Try to “trick” the model with challenging images (images that are blurry, partially obscured, unusual breeds, images with both cats and dogs, etc.). Observe where the model makes mistakes.
  • Iteration: Based on testing results, identify areas for improvement. Iterate on their model by:
    • Collecting more training data, especially for classes where the model is struggling or for challenging image types.
    • Reviewing their training data – are there any biases or issues in the dataset?
    • Retrain the model with the new data.
    • Re-test and observe if performance has improved.
  • Document their testing process, model weaknesses, and iteration steps.
• (10 min) Peer Feedback + Q&A: “AI Model Showcase & Model Performance Discussion”
  • Students showcase their trained cat vs. dog classifiers in Teachable Machine. Demonstrate its performance using webcam or test images.
  • Peer feedback: Focus on model accuracy, ability to classify different images, and improvements made through iteration.
  • Q&A: Discuss factors affecting model performance (data quality, dataset size, model complexity – briefly), limitations of their simple model, and real-world applications of image classification AI (image search, object recognition in self-driving cars, medical image analysis, etc.).

Week 4: Patterns and Predictions – Predicting Weather Trends Using Simple Linear Regression in Jupyter Notebooks (Intro to Data Science)

• Learning Objective: Introduce basic Data Science concepts, learn to use Jupyter Notebooks (Python environment) for simple data analysis, and perform a basic linear regression analysis to predict weather trends using a provided weather dataset.
• (20 min) Concept Deep-Dive + Examples: Introduction to Data Science & Prediction with Linear Regression
  • Introduce Data Science: What is Data Science? (Using data to answer questions, solve problems, make predictions). How is it related to AI/ML? (Data Science often uses ML techniques for analysis and prediction).
  • Data Science Process (Simplified): Data Collection -> Data Cleaning -> Data Analysis -> Data Visualization -> Insights/Conclusions -> Predictions. Focus on Data Analysis and Prediction for this week.
  • Introduce Jupyter Notebooks: Explain Jupyter Notebooks as an interactive coding environment (combining code, text, visualizations). Explain it’s a tool used by Data Scientists. Introduce Google Colab as a free, web-based Jupyter Notebook environment.
  • Basic Python in Jupyter Notebooks: Briefly review basic Python syntax (variables, print statements – from Python 1 course, or provide quick intro if students are new to Python). Emphasize that they will use Python for data analysis in Jupyter.
  • Introduce Linear Regression: Explain linear regression as a simple ML technique for finding relationships between variables and making predictions. Simplified explanation: “Drawing a line of best fit through data points to see trends and predict future points.” Example: Predicting ice cream sales based on temperature.
  • Example: Show a simple example of linear regression concept visually (graph with data points and line of best fit). Briefly demonstrate a pre-written Jupyter Notebook performing linear regression on a simple dataset (e.g., ice cream sales vs. temperature dataset) to predict sales for a given temperature. Highlight the Python code used (very basic, focus on understanding the concept of linear regression and data analysis in Jupyter).
• (35 min) Hands-on Activity: “Weather Data Analysis and Prediction with Jupyter Notebooks”
  • Jupyter Notebook Setup: Students access Google Colab (or local Jupyter Notebook environment) and open a pre-provided Jupyter Notebook template that contains:
    • Loading a simple weather dataset (e.g., sample dataset with columns: Day, Temperature, Rainfall, Sunshine Hours). Provide a simplified, cleaned dataset for this initial activity.
    • Basic Python code to load data, perform linear regression analysis (using a simple library like numpy for linear regression – provide pre-written code, students use it, not write it from scratch this week), and visualize the results.
  • Data Exploration: Students run the pre-written Jupyter Notebook code cells to:
    • Load and examine the weather dataset (using Python code in Jupyter).
    • Perform linear regression to find the relationship between “Day” and “Temperature” (or another relevant pair of variables in the dataset).
    • Visualize the data and the regression line using charts (generated by Python code in Jupyter).
  • Guided Practice: Teacher guides students through running the Jupyter Notebook cells, understanding the Python code (at a high level, focus on understanding the data analysis process and the output, not writing complex Python code this week), and interpreting the visualizations.
• (25 min) Real-world Problem-Solving (Mini-Project): “Weather Trend Prediction Challenge”
  • Mini-project: “Weather Trend Prediction Challenge”. Using the weather dataset and the Jupyter Notebook template, students work in teams to:
    • Perform linear regression to predict weather trends for a different pair of variables in the dataset (e.g., “Day” vs. “Rainfall,” “Day” vs. “Sunshine Hours,” or “Temperature” vs. “Sunshine Hours” – choose pairs that might show some linear relationship).
    • Modify the Jupyter Notebook code (by changing variable names in pre-written code – guided modifications) to perform the new linear regression analysis and generate visualizations for their chosen variables.
    • Analyze their visualizations and linear regression results. What trends do they observe? Can they make any predictions about future weather based on these trends? (e.g., “Is temperature generally increasing over time?”, “Is rainfall decreasing over time?”, “Is there a relationship between temperature and sunshine hours?”).
    • Document their analysis, visualizations, and weather trend predictions.
  • Teams present their weather trend predictions and data analysis findings to the class.
• (10 min) Peer Feedback + Q&A: “Data Prediction Showcase & Data Science Discussion”
  • Teams share their weather trend predictions and data visualizations from Jupyter Notebooks.
  • Peer feedback: Focus on clarity of visualizations, reasonable trend interpretations, and effective use of Jupyter Notebooks for data analysis.
  • Q&A: Discuss limitations of simple linear regression (not always perfect for complex data), other types of ML for prediction (briefly mention more advanced ML techniques), the role of Data Science in real-world prediction and decision-making, and ethical considerations in using data for predictions (potential biases in data, responsible use of predictions).

Week 5: Ethics of AI – Role-Play and Discussion: Bias, Fairness, and Responsible AI

• Learning Objective: Introduce the ethical implications of AI, discuss concepts like bias, fairness, and transparency in AI systems, and explore the responsible development and use of AI through role-playing and ethical dilemma discussions.
• (20 min) Concept Deep-Dive + Examples: Ethics of AI – Bias, Fairness, Transparency
  • Introduce AI Ethics: Why is ethics important in AI? (AI systems can have a big impact on people’s lives, so they need to be fair, responsible, and benefit society).
  • Bias in AI: Explain AI bias: “AI models can learn biases from the data they are trained on.” Give concrete examples of AI bias:
    • Facial recognition bias: AI systems being less accurate at recognizing faces of people of color or women if trained on datasets that are not diverse enough.
    • Recruitment AI bias: AI systems for resume screening favoring certain demographics if trained on historical hiring data that reflects existing biases.
    • Language model bias: Chatbots sometimes generating biased or offensive language if trained on biased text data from the internet.
    • Discuss the sources of bias: Biased training data, biased algorithms, biased design choices, societal biases reflected in data.
  • Fairness in AI: What does “fair AI” mean? (Treating everyone equally, not discriminating against certain groups). Discuss different types of fairness (equality of opportunity, equal outcomes – simplified).
  • Transparency and Explainability: Why is it important for AI to be transparent and explainable? (To understand how AI makes decisions, identify and fix biases, build trust). “Black box” AI vs. “Explainable AI” (XAI) – simplified explanation.
  • Responsible AI Development and Use: Discuss principles of responsible AI: Fairness, accountability, transparency, safety, privacy, beneficence.
  • Example: Show short videos or articles discussing real-world examples of AI bias and ethical concerns in AI. Present ethical dilemmas related to AI.
• (35 min) Hands-on Activity: “Role-Play – Ethical Dilemmas in AI Decision Making”
  • Role-Playing Scenarios: Present students with 2-3 ethical dilemma scenarios involving AI decision-making (prepare scenarios beforehand, age-appropriate and relatable):
    • Scenario 1: AI Grading System: Should AI be used to automatically grade student essays or assignments? What are the potential benefits and risks? What are the fairness considerations? (Scenario from course outline).
    • Scenario 2: AI in Hiring: Should companies use AI to screen job applications and select candidates for interviews? What are the potential biases that could arise? How can fairness be ensured?
    • Scenario 3: AI in Healthcare: Should AI be used to diagnose diseases or recommend treatments? What are the risks of errors or biases in AI medical systems? How can patient safety and trust be maintained?
  • Role Assignment: Divide students into small groups. Assign roles within each group (e.g., “AI Developer,” “Student/Job Applicant/Patient,” “Ethics Advisor,” “Policy Maker”).
  • Role-Play & Debate: Each group role-plays their assigned scenario. Debate the ethical dilemmas, consider different perspectives, and try to come to a group consensus on the responsible use of AI in that scenario.
• (25 min) Real-world Problem-Solving (Mini-Project): “AI Ethics Checklist and Recommendations”
  • Mini-project: “AI Ethics Checklist and Recommendations”. For one of the ethical dilemma scenarios they role-played, teams work together to create:
    • An “AI Ethics Checklist”: A list of 3-5 key ethical considerations that should be taken into account when developing or using AI in that scenario (e.g., for AI Grading System: “Check for bias in training data,” “Ensure transparency in grading criteria,” “Allow for human review of AI grades,” “Consider impact on student motivation and learning”).
    • “Recommendations for Responsible AI”: 3-5 concrete recommendations for how to develop or use AI in that scenario in a more ethical and responsible way (e.g., for AI in Hiring: “Use diverse training data,” “Audit AI system for bias regularly,” “Provide human oversight in hiring decisions,” “Ensure transparency about AI use to job applicants”).
  • Teams present their AI Ethics Checklists and Recommendations to the class.
• (10 min) Peer Feedback + Q&A: “Ethics Checklist Showcase & Responsible AI Discussion”
  • Teams share their AI Ethics Checklists and Recommendations.
  • Peer feedback: Focus on the relevance and completeness of the ethical considerations identified, and the practicality and effectiveness of their recommendations for responsible AI.
  • Q&A: Discuss the complexity of AI ethics, the lack of easy answers, the ongoing debate about AI ethics in society, and the importance of considering ethical implications in all AI projects they might create in the future.

Week 6: Chatbots (LLMs) – Designing a Homework Help Chatbot Using ChatGPT Prompts (Introduction to Large Language Models)

• Learning Objective: Introduce Chatbots and Large Language Models (LLMs) as a type of AI, explore the capabilities and limitations of LLMs using AI Chat tools (e.g., ChatGPT, Gemini), and design a simple homework help chatbot by crafting effective prompts for an LLM.
• (20 min) Concept Deep-Dive + Examples: Chatbots and Large Language Models (LLMs)
  • Introduce Chatbots: What are chatbots? (AI programs that can have conversations with humans). Examples: Customer service chatbots, virtual assistants, educational chatbots.
  • Introduce Large Language Models (LLMs): Explain LLMs as a powerful type of AI that powers many modern chatbots (like ChatGPT, Gemini). Simplified explanation: “LLMs are trained on HUGE amounts of text data from the internet and learn to understand and generate human-like text.” “They can answer questions, write stories, translate languages, and more.”
  • Capabilities of LLMs: Discuss what LLMs can do: Answer questions, generate text, summarize information, translate languages, write different kinds of creative content.
  • Limitations of LLMs: Discuss limitations of LLMs: Can sometimes be inaccurate or make up information (hallucinations), can be biased (reflecting biases in training data), lack true understanding or consciousness. Emphasize the importance of critical evaluation of LLM outputs.
  • Introduce AI Chat Tools (ChatGPT, Gemini, etc.): Explain that they will be using AI chat tools to interact with LLMs and design their own chatbot. Emphasize responsible use of these tools and ethical considerations (not for cheating, but for learning and exploration).
  • Example: Show a live demo of interacting with ChatGPT or Gemini, showcasing its capabilities (asking questions, requesting text generation). Discuss examples of educational chatbots and their potential uses.
• (35 min) Hands-on Activity: “Chatbot Interaction and Prompt Engineering with AI Chat Tools”
  • AI Chat Tool Exploration: Students access an AI chat tool (e.g., ChatGPT, Gemini – free version). Explore its interface and basic functionalities.
  • Chatbot Interaction: Students interact with the AI chatbot by asking it different types of questions and giving it different prompts:
    • General knowledge questions (“What is the capital of France?”)
    • Homework questions (simple math problems, science questions, history questions – age-appropriate). Emphasize ethical use – not to get answers to cheat, but to understand how chatbots work and their capabilities/limitations.
    • Creative writing prompts (“Write a short story about a robot,” “Write a poem about the stars”).
    • Summarization tasks (“Summarize this short article” – provide a short text).
    • Translation tasks (“Translate ‘Hello, world!’ to Spanish”).
  • Prompt Engineering: Experiment with different ways of phrasing questions or prompts to get better or more specific answers from the chatbot. “Prompt Engineering” – learning to craft effective prompts to guide LLM behavior.
  • Document their chatbot interactions and observations: What types of questions does it answer well? Where does it struggle? How does prompt phrasing affect the responses?
• (25 min) Real-world Problem-Solving (Mini-Project): “Design a Homework Help Chatbot with ChatGPT Prompts”
  • Mini-project: “Design a Homework Help Chatbot”. Teams design a chatbot that can help students with homework questions in a specific subject area (e.g., Math, Science, History, English – choose a subject relevant to the curriculum).
  • Challenge: Teams work together to:
    • Define the subject area for their homework help chatbot.
    • Brainstorm types of homework questions students might ask in that subject.
    • Craft a set of effective prompts for ChatGPT (or their chosen AI chat tool) that, when given to the chatbot, would generate helpful and accurate answers to those homework questions. Focus on prompt clarity, specificity, and guidance.
    • Test their prompts by entering them into the AI chat tool and evaluating the chatbot’s responses. Refine prompts as needed to improve response quality.
    • Document their best prompts and example chatbot interactions.
  • Teams present their “Homework Help Chatbot” prompt designs and demonstrate example chatbot interactions.
• (10 min) Peer Feedback + Q&A: “Chatbot Prompt Showcase & LLM Capabilities Discussion”
  • Teams share their homework help chatbot prompt designs and demonstrate example chatbot interactions.
  • Peer feedback: Focus on the effectiveness of their prompts in generating helpful and accurate chatbot responses, clarity of prompts, and creativity in chatbot design.
  • Q&A: Discuss the capabilities and limitations of LLMs, prompt engineering techniques, ethical considerations in using chatbots for education (potential for misuse, accuracy concerns, etc.), and potential future applications of chatbots in learning and other domains.

Week 7: AI in the Real World – Exploring Diverse Applications and Future Trends of AI

• Learning Objective: Broaden understanding of AI by exploring a wider range of real-world AI applications beyond those already discussed, investigate emerging trends in AI, and discuss the potential future impact of AI on society and different industries.
• (20 min) Concept Deep-Dive + Examples: AI in Diverse Fields – Beyond the Basics
  • Expand beyond familiar AI examples to explore AI applications in diverse fields:
    • Healthcare: AI in medical diagnosis, drug discovery, personalized medicine, robotic surgery.
    • Transportation: Self-driving cars, traffic optimization, logistics and delivery drones.
    • Agriculture: Precision agriculture, crop monitoring, automated farming.
    • Manufacturing: Industrial robots, quality control, predictive maintenance.
    • Environment: Climate change modeling, wildlife conservation, pollution monitoring.
    • Arts and Creativity: AI art generation, AI music composition, AI-assisted design.
    • Accessibility: AI for assistive technologies, speech recognition for accessibility, image recognition for visually impaired.
  • Emerging Trends in AI: Discuss current and future trends in AI:
    • Advancements in Large Language Models (LLMs) and Generative AI (text, images, code).
    • AI in robotics and autonomous systems.
    • AI in personalized learning and education.
    • AI in scientific discovery and research.
    • Ethical AI and Responsible AI practices becoming increasingly important.
  • Example: Show short videos, articles, or case studies showcasing AI applications in different fields and highlighting emerging trends.
• (35 min) Independent/Collaborative Coding Challenges: “AI Application Deep Dive – Research & Presentation Planning”
  • AI Application Research: Students choose one real-world AI application from the diverse fields discussed (or find another AI application that interests them with teacher approval).
  • In-Depth Research (Independent or Pairs): Students research their chosen AI application in more detail. Focus on understanding:
    • What problem does this AI application solve?
    • How does AI/ML contribute to this application? (What type of AI/ML techniques are used – if information is accessible and age-appropriate, otherwise focus on general AI role).
    • What are the benefits and potential challenges/ethical considerations of this AI application?
    • What are some real-world examples of this AI application in use?
  • Presentation Planning (Teams): Students form teams (groups of 3-4) based on related or complementary AI application choices (or pre-assigned teams to ensure diversity of application coverage). Teams plan a short presentation to share their research findings with the class. Presentation format can be:
    • Short verbal presentation with visuals (slides, posters, drawings).
    • Interactive demo or simulation (if feasible and relevant).
    • Short video or multimedia presentation.
    • Focus on clear communication of key information about their chosen AI application.
• (25 min) Real-world Problem-Solving (Mini-Project): “Future of AI – Brainstorming and Visioning”
  • Mini-project: “Future of AI – Brainstorming and Visioning”. Teams brainstorm and discuss:
    • What are some positive future impacts AI could have on society and different industries in the next 10-20 years? (Brainstorm optimistic scenarios).
    • What are some potential challenges or negative impacts of AI in the future that we need to be aware of and address responsibly? (Brainstorm potential risks and ethical concerns).
    • What skills and knowledge will be most important for “Future Genius” students to have to thrive and contribute positively in an AI-driven world? (Focus on future skills – coding, data literacy, critical thinking, ethics, creativity, collaboration, problem-solving).
    • Document their brainstorming ideas and future visions for AI.
  • Teams prepare to share their “Future of AI” visions and brainstorming outcomes with the class in the next week’s session.
• (10 min) Peer Feedback + Q&A: “AI Application Choices & Future Vision Ideas”
  • Teams briefly share their chosen AI applications for research and their initial brainstorming ideas for the “Future of AI” visioning project.
  • Peer feedback: Focus on the diversity of AI applications chosen, the relevance of research questions, and the creativity and thoughtfulness of their “Future of AI” visions.
  • Q&A: Address any questions about AI applications, future trends in AI, research strategies, and preparation for their presentations and future visioning project.

Week 8: AI in the Real World – Project Presentations and Future Visions of AI

• (Learning Objective): Students will present their research findings on diverse AI applications, share their visions for the future of AI, and engage in discussions about the broader impact and future implications of AI technology.
• (20 min) Concept Review & Presentation Prep: Review of AI Applications and Presentation Guidelines
  • Briefly review the diverse AI applications and emerging trends discussed in Week 7.
  • Review presentation guidelines for the “AI Application Deep Dive” presentations: Clarity, conciseness, highlighting key findings, engaging presentation style.
  • Provide any last-minute presentation tips or Q&A for presentation preparation.
• (35 min) Project Showcase & Demos: “AI Application Deep Dive Presentations”
  • Teams present their “AI Application Deep Dive” research findings to the class.
  • Each team presents their chosen AI application, explaining:
    • What problem it solves.
    • How AI/ML is used in this application.
    • Benefits and challenges/ethical considerations.
    • Real-world examples.
  • Encourage engaging presentation styles, visuals, and clear communication of key information.
  • Classmates and teacher provide positive feedback and ask clarifying questions after each presentation.
• (25 min) Real-world Problem-Solving (Mini-Project) Showcase & Discussion: “Future of AI Vision Sharing & Debate”
  • Teams share their “Future of AI” visions and brainstorming outcomes with the class.
  • Each team presents their brainstormed ideas about:
    • Positive future impacts of AI.
    • Potential challenges/negative impacts of AI.
    • Future skills needed for an AI-driven world.
  • Facilitate a class discussion and debate based on the teams’ future visions:
    • Compare and contrast different teams’ optimistic and cautionary visions of AI’s future.
    • Discuss common themes and diverging viewpoints.
    • Debate the likelihood of different future scenarios and the factors that will shape AI’s future impact.
    • Focus on encouraging thoughtful discussion, critical thinking about AI’s future, and considering both the potential benefits and risks.
• (10 min) Peer Feedback + Q&A: “AI Application & Future Vision Feedback and Reflection”
  • Peer feedback: Provide constructive feedback on team presentations – clarity, content, engagement. Feedback on the thoughtfulness and depth of their “Future of AI” visions.
  • Q&A: Open floor for final questions about AI applications, future trends, ethical implications, and reflections on the course so far.

Week 9: AI/ML Project Design and Development – Brainstorming and Planning Your Own AI Project

• (Learning Objective): Students will transition from learning about pre-built AI/ML examples to designing their own simple AI/ML project. They will brainstorm project ideas, define project scope, and plan the key components of their AI/ML project, incorporating ethical considerations into their design.
• (20 min) Concept Review & Project Inspiration: Review of AI/ML Concepts & Project Idea Brainstorming
  • Briefly review key AI/ML concepts covered in the course so far: Image classification, prediction (linear regression – conceptually), chatbots (LLMs), AI ethics.
  • Show examples of simple AI/ML projects that students could potentially create (age-appropriate and feasible with no-code/low-code tools):
    • Enhanced image classifier (extend cat vs. dog classifier to other categories).
    • Simple prediction tool (extend weather prediction to other datasets – e.g., sports stats prediction, sales prediction).
    • Interactive chatbot for a specific purpose (extend homework chatbot to a different domain – e.g., story chatbot, fact chatbot).
    • AI-powered game (simple game with AI opponent or AI-driven elements).
    • Data visualization project with enhanced interactivity or analysis.
  • Brainstorming Session: Lead a class brainstorming session to generate more AI/ML project ideas. Encourage students to think about problems they care about, areas they are interested in, and how AI/ML could be applied creatively and ethically.
• (35 min) Independent/Collaborative Coding Challenges: “AI Project Idea Pitch – Concept and Scope”
  • Project Idea Brainstorming (Individual): Each student individually brainstorms at least 2-3 potential AI/ML project ideas. Think about:
    • What problem or task will your AI project address?
    • What type of AI/ML technique might be relevant (image classification, prediction, chatbot, data analysis)?
    • What data might you need (or simulate) for your project?
    • What are the potential ethical considerations for your project?
  • Project Idea Pitch (Pairs): Students pair up and “pitch” their best project idea to their partner. Explain their project concept, intended functionality, and why it’s interesting or valuable. Get feedback from their partner.
  • Project Idea Selection (Individual): Based on brainstorming and feedback, each student selects one AI/ML project idea to develop for the final project showcase in Week 10.
• (25 min) Real-world Problem-Solving (Mini-Project): “AI Project Planning – Defining Scope and Features”
  • Mini-project: “AI Project Planning”. Students begin planning their chosen AI/ML projects in detail. Focus on defining project scope and key features:
    • Project Goal: Clearly define the goal of their AI/ML project. What will it do? What problem will it solve or what will it demonstrate?
    • Key Features: List 2-3 key features or functionalities their project will have. Keep it realistic and achievable within the time constraints (1 week for development). Prioritize core features over ambitious scope.
    • Tools and Technologies: Specify which tools they will use (Teachable Machine, Jupyter Notebooks, AI Chat tools, etc.).
    • Data Source (if applicable): Identify data they will use or how they will simulate data for their project (if needed).
    • Ethical Considerations: Briefly outline 1-2 key ethical considerations relevant to their specific project and how they plan to address them.
    • Basic Project Plan: Create a simple plan or outline of the steps they will take to develop their project.
  • Students document their project plans and scope definitions.
• (10 min) Peer Feedback + Q&A: “AI Project Plan Sharing & Feasibility Check”
  • Students briefly share their AI project plans and scope definitions.
  • Peer feedback: Focus on the clarity of project goals, feasibility of the scope, and the thoughtfulness of their ethical considerations.
  • Q&A: Address any questions about project scope, tool usage, data needs, and project feasibility. Provide guidance on refining project scope and focusing on achievable goals for the final project week.

Week 10: AI/ML Project Development and Showcase – Future Genius AI/ML Creators!

• (Learning Objective): Students will dedicate the session to developing their individual or paired AI/ML projects, implementing their project plans, and preparing for a final project showcase to demonstrate their AI/ML creations.
• (35 min) Project Work Time – AI/ML Project Development: Building Your AI Creation
  • Students work individually or in pairs to develop their chosen AI/ML projects based on their project plans.
  • Provide dedicated project work time in class.
  • Teacher circulates, provides individual guidance, answers questions, and helps students overcome technical challenges in using Teachable Machine, Jupyter Notebooks, AI Chat tools, or other chosen platforms for their projects.
  • Encourage iterative development – build core features first, test, and then add enhancements if time allows.
  • Emphasize focusing on creating a functional and demonstrable project within the limited time frame, rather than aiming for overly complex or fully polished projects.
• (25 min) Project Showcase & Demos: “Future Genius AI/ML Creator Showcase!”
  • Students showcase their completed AI/ML projects to the class.
  • Each student/team presents their project, demonstrates its functionality, and explains:
    • What is their AI/ML project?
    • What problem does it address or what does it demonstrate?
    • Which AI/ML tools and techniques did they use?
    • What were the key features of their project?
    • What ethical considerations did they address in their project design (if applicable)?
    • What were the biggest challenges they faced and what did they learn from the project development process?
  • Encourage engaging demos and clear communication of their AI/ML creations and learning journey.
  • Classmates and teacher provide positive feedback and enthusiastic applause for each project demonstration.
• (10 min) Course Recap & Rewards: “Future Genius AI/ML Creators!”
  • Course recap: Summarize key skills and concepts learned in AI/ML Explorers 2: What is AI/ML, Data Science basics, Image Classification (Teachable Machine), Linear Regression (conceptually in Jupyter), Chatbots/LLMs (ChatGPT prompts), AI Ethics, and the process of designing and developing simple AI/ML projects. Congratulate students on completing AI/ML Explorers 2 and becoming “Future Genius AI/ML Creators!”.
  • Encourage continued AI/ML exploration and learning: Suggest resources for further learning (online courses, websites, books about AI/ML for teens), encourage them to continue experimenting with AI tools and exploring AI applications in their interests.
  • Award certificates of completion for AI/ML Explorers 2. Recognize outstanding projects and achievements (optional awards, e.g., “Most Creative AI Project,” “Best Data Analysis,” “Most Ethically Conscious Design,” “Most Innovative Use of AI Tools”). Provide celebratory rewards (e.g., AI/robot-themed stickers, small STEM toys, coding resources, books about AI/ML for teens, AI-themed treats).
• (20 min) (Optional – if time allows, can be shortened or omitted if needed): “Future of AI – Open Discussion and Q&A”
  * Open class discussion about the future of AI, building upon the “Future Visioning” from Week 7 and the project experiences in Week 10.
  * Discuss: What are they most excited about for the future of AI? What are they still concerned about regarding AI’s future impact? What role do they see themselves playing in an AI-driven world? What are their next steps for learning more about AI/ML?
  * Final Q&A: Open floor for any final questions about AI/ML, the course, or future learning pathways.

This “Future Genius AI/ML Explorers 2” curriculum is designed to provide a hands-on, engaging, and ethically informed introduction to the world of AI, ML, and Data Science for 10+ year olds, using accessible no-code and low-code tools. Remember to adapt the complexity of projects and challenges based on the students’ progress and interests, and emphasize creativity, collaboration, and responsible AI practices throughout the course!

Course Title: Future Genius Drone Explorers: My First Drone Adventures

Age Group: 10+ years

Course Synopsis: This 10-week introductory course provides an exciting exploration of drones as aerial robots using CoDrone EDU. Students will learn fundamental concepts of flight, drone mechanics, and control systems. The course begins with hands-on flight practice and transitions into programming using both block-based (Snap!/Blockly) and text-based (Python) environments. Students will develop computational thinking skills, problem-solving abilities, and teamwork through engaging activities, flight challenges, and creative drone projects. The course emphasizes drone safety, responsible use, and building a strong foundation for understanding aerial robotics and programming.

Tools:

• CoDrone EDU Drones
• Snap!/Blockly Programming Environment (CoDrone EDU compatible, e.g., web-based or app)
• Python Programming Environment (CoDrone EDU compatible, e.g., Thonny, VS Code with CoDrone extension)
• Indoor Flight Area (gymnasium, large classroom, or open indoor space)
• Flight Obstacles (hoops, cones, targets – for flight challenges)

Session Duration: 1 hour 30 minutes

Week 1: Welcome to Drone World! – Introduction to Drones, Flight Basics, and Safety

• Learning Objective: Introduce drones as aerial robots, understand basic drone components, learn fundamental concepts of flight (lift, thrust, drag, gravity), and emphasize drone safety rules and responsible operation.
• (20 min) Concept Deep-Dive + Examples: What are Drones? & Basics of Flight
  • Introduce Drones: What are drones? (Unmanned Aerial Vehicles – UAVs, aerial robots). Real-world drone applications (photography, delivery, agriculture, search and rescue, etc.). Discuss the increasing importance of drone technology.
  • Drone Components: Identify key parts of CoDrone EDU: frame, propellers, motors, battery, flight controller, sensors (brief overview).
  • Basics of Flight: Explain the four forces of flight: Lift (upward force), Thrust (forward force), Drag (air resistance), Gravity (downward pull). How do drones achieve lift and control? (Propeller speed and angles).
  • Drone Safety & Regulations: Emphasize drone safety rules: always fly in safe, open areas, maintain visual line of sight, never fly near people or obstacles, responsible drone operation, respecting privacy. Review any local drone regulations (simplified for age group).
  • Example: Show videos or images of different types of drones and their applications. Demonstrate the basic components of CoDrone EDU.
• (35 min) Hands-on Activity: CoDrone EDU Inspection and Pre-Flight Checks & Basic Flight Practice (Take-off and Landing)
  • CoDrone EDU Inspection: Students carefully inspect their CoDrone EDU drones, identify components (props, motors, battery, LEDs), learn about battery charging and handling. Pre-flight checklist review.
  • Basic Flight Practice (Pairs/Small Groups, one drone per group initially): In a safe, open indoor area, practice basic drone take-off and landing. Focus on gentle take-offs, controlled hovering at low altitude, and smooth landings. Emphasize safety and controlled movements. Students take turns piloting under supervision.
  • Start with very basic remote control using the CoDrone EDU remote controller (if included) or a mobile app controller (if applicable) – focus on basic throttle (up/down) and yaw (rotation).
• (25 min) Real-world Problem-Solving (Mini-Project): “Safe Landing Zone Challenge”
  • Mini-project: “Safe Landing Zone Challenge”. Mark designated “safe landing zones” on the floor (using mats or tape).
  • Challenge: Students practice taking off and landing their drones within the designated safe landing zones. Focus on precision landing and controlled descent.
  • Discuss challenges of controlling a drone and the importance of practice and smooth control inputs.
• (10 min) Peer Feedback + Q&A: “Flight Experience Sharing & Safety Review”
  • Students share their initial flight experiences, discuss challenges and successes.
  • Peer feedback: Positive encouragement and sharing flight tips with each other.
  • Q&A: Review drone safety rules, address flight control questions, and discuss initial impressions of drone flight.

Week 2: Mastering Drone Control – Hovering, Yaw, Pitch, and Roll

• Learning Objective: Learn to master basic drone flight controls: throttle (altitude), yaw (rotation), pitch (forward/backward), and roll (sideways movement), and practice controlled hovering and basic maneuvers.
• (20 min) Concept Deep-Dive + Examples: Drone Flight Controls – Throttle, Yaw, Pitch, Roll
  • Review basic flight concepts. Focus on explaining the four primary drone controls in detail:
    • Throttle: Controls motor speed and altitude (up/down).
    • Yaw: Controls rotation around the vertical axis (left/right rotation).
    • Pitch: Controls forward and backward movement (tilting the drone forward/backward).
    • Roll: Controls sideways movement (tilting the drone left/right).
  • Explain how these controls are used in combination for complex maneuvers.
  • Example: Demonstrate each control individually and then in combination using a drone simulator (if available) or by visually demonstrating with a model drone. Show videos of drone maneuvers highlighting these controls.
• (35 min) Hands-on Activity: Flight Practice – Controlled Hovering, Yaw, Pitch, Roll
  • Controlled Hovering Practice: Students practice maintaining stable hover at a consistent altitude, minimizing drift. Focus on throttle control.
  • Yaw Control Practice: Practice controlled yaw rotations (360-degree turns, precise rotations to specific angles). Focus on yaw stick control.
  • Pitch and Roll Control Practice: Practice controlled forward/backward (pitch) and sideways (roll) movements. Start with short, controlled movements and gradually increase complexity.
  • Progressive Flight Exercises: Introduce a series of progressive flight exercises (e.g., hovering in place, controlled yaw rotations, figure-eights, squares) to practice combining controls.
  • Students practice individually or in pairs, taking turns piloting and providing feedback to each other under supervision.
• (25 min) Real-world Problem-Solving (Mini-Project): “Precision Hovering Challenge”
  • Mini-project: “Precision Hovering Challenge”. Mark a target area on the floor (e.g., a circle or square).
  • Challenge: Students practice hovering their drones precisely within the target area for a sustained period (e.g., 10 seconds). Focus on maintaining position and minimizing drift using throttle, pitch, and roll adjustments. Measure hovering time and accuracy.
  • Discuss challenges of precise control and strategies for improving hovering accuracy.
• (10 min) Peer Feedback + Q&A: “Flight Control Showcase & Maneuvering Tips”
  • Students demonstrate their hovering and basic maneuvering skills.
  • Peer feedback: Focus on flight smoothness, control precision, and stable hovering. Share flight control tips and maneuvering advice.
  • Q&A: Address flight control questions, discuss common piloting challenges, and review best practices for smooth and controlled drone flight.

Week 3: Introduction to Block-Based Programming – Snap!/Blockly for Drone Control (Basic Movement)

• Learning Objective: Introduce block-based programming for CoDrone EDU using Snap! or Blockly-based environment, learn to connect the drone to the programming environment, and use basic movement blocks to program drone take-off, landing, and simple flight paths.
• (20 min) Concept Deep-Dive + Examples: Block-Based Programming for CoDrone EDU (Snap!/Blockly)
  • Introduce block-based programming for drones. Explain Snap!/Blockly as visual, drag-and-drop programming languages suitable for beginners.
  • Introduce the CoDrone EDU programming environment (web-based or app using Snap!/Blockly). Tour the interface: Block palettes (Motion, Control, Events, etc., specific to CoDrone EDU), programming area, drone connection interface.
  • Demonstrate connecting CoDrone EDU to the programming environment via Bluetooth.
  • Introduce basic CoDrone EDU blocks for movement control (take-off, land, forward, backward, left, right, up, down, yaw left, yaw right, hover). Explain units (seconds, meters, degrees).
  • Example: Show a simple block-based program to make CoDrone EDU take off, move forward, turn, and land.
• (35 min) Independent/Collaborative Coding Challenges: “First Programs – Block-Based Drone Flight”
  • Challenge 1 (Programming – Independent): Write a block-based program to make CoDrone EDU take off, hover for 3 seconds, and land.
  • Challenge 2 (Programming – Pair Programming): Pairs write a block-based program to make CoDrone EDU take off, move forward 2 meters, then backward 2 meters, and land. Experiment with different distances and directions.
  • Challenge 3 (Combined Flight & Programming – Teamwork): Teams program CoDrone EDU to fly a simple square path using block-based programming (take off, move forward, turn right, repeat 4 times, land).
• (25 min) Real-world Problem-Solving (Mini-Project): “Programmed Landing Challenge”
  • Mini-project: “Programmed Landing Challenge”. Mark a target landing zone on the floor (similar to Week 1).
  • Challenge: Teams program CoDrone EDU using block-based programming to take off and land autonomously within the designated target landing zone. Focus on using takeoff and land blocks and controlling horizontal movement to reach the landing zone (using timed movements for now – sensor-based landing later).
  • Discuss challenges of programmed landing and factors affecting landing accuracy (drift, wind – even indoors).
• (10 min) Peer Feedback + Q&A: “Block-Based Code Showcase & Programming Tips”
  • Teams showcase their block-based drone programs and programmed landings.
  • Peer feedback: Focus on code clarity, efficient use of block-based commands, smooth drone movements, and programmed landing accuracy. Share block-based programming tips and troubleshooting advice.
  • Q&A: Open floor for questions about block-based programming, CoDrone EDU specific blocks, connecting drones to the programming environment, and challenges faced during block-based coding.

Week 4: Control Flow in Blockly/Snap! – Loops and Sequences for Drone Choreography

• Learning Objective: Learn to use control flow blocks in Snap!/Blockly: ‘repeat’ loops and sequences of commands to create more complex and efficient drone programs, and program basic drone choreographies.
• (20 min) Concept Deep-Dive + Examples: Control Flow Blocks – Loops and Sequences
  • Review sequences of commands. Introduce ‘repeat [number]’ loop block in Snap!/Blockly. Explain how loops automate repetition and simplify code.
  • Demonstrate using ‘repeat’ loop to make CoDrone EDU fly in a square path (repeating move forward and turn actions in block-based code).
  • Explain how to create longer, more complex sequences of commands using block-based programming and loops.
  • Example: Show a block-based program using loops to create a simple drone “figure-eight” flight path or a repeating dance move.
• (35 min) Independent/Collaborative Coding Challenges: “Block-Based Drone Choreography”
  • Challenge 1 (Programming – Independent): Write a block-based program using a repeat loop to make CoDrone EDU perform a repeating back-and-forth movement pattern (e.g., move forward, backward, repeat).
  • Challenge 2 (Programming – Pair Programming): Pairs write a block-based program using nested repeat loops to make CoDrone EDU fly in a grid pattern (similar to Week 3 Robot Explorers 1, but now with drones and block-based programming).
  • Challenge 3 (Creative Choreography – Teamwork): Teams design and program a short drone choreography routine (a “drone dance”) using sequences of block-based movement commands and repeat loops to create repeating dance steps or patterns. Encourage creativity in dance moves and flight paths.
• (25 min) Real-world Problem-Solving (Mini-Project): “Programmed Flight Path Challenge”
  • Mini-project: “Programmed Flight Path Challenge”. Set up a more complex flight path using cones or hoops as waypoints (e.g., a zig-zag path, a figure-eight path with waypoints).
  • Challenge: Teams program CoDrone EDU using block-based programming and loops to autonomously fly through the defined flight path, navigating through or around the waypoints. Focus on using sequences and loops to create a planned flight path.
  • Encourage precise programming and adjusting code for accurate flight path execution.
• (10 min) Peer Feedback + Q&A: “Drone Choreography Showcase & Loop Usage Tips”
  • Teams showcase their block-based drone choreographies and programmed flight paths.
  • Peer feedback: Focus on code clarity, efficient use of loops for repetition, smooth drone choreography, and accuracy in following programmed flight paths. Share block-based programming tips for choreography and path planning.
  • Q&A: Discuss nested loops, creating more complex patterns with loops and sequences, and advantages of using loops for efficient drone programming.

Week 5: Introduction to Python Programming for Drones – Basic Python Syntax for Drone Control

• Learning Objective: Transition from block-based to text-based Python programming for CoDrone EDU, learn basic Python syntax for drone control, and write simple Python programs for drone take-off, landing, and basic movement.
• (20 min) Concept Deep-Dive + Examples: Python Programming for CoDrone EDU
  • Review block-based programming. Discuss its limitations for more advanced control and flexibility.
  • Introduce Python as a text-based alternative for CoDrone EDU programming. Reiterate Python’s power and versatility.
  • Compare Snap!/Blockly blocks to equivalent Python code for drone control (take-off, land, movement). Show direct code equivalents for key blocks.
  • Introduce basic Python syntax for CoDrone EDU control: Connecting to drone (if needed – specific syntax depends on environment), drone.takeoff(), drone.land(), drone.move_forward(distance), drone.turn_left(angle), etc. (Provide basic Python command examples).
  • Setting up Python environment (if not already done): Guide students to set up a Python environment compatible with CoDrone EDU (LEGO Education Python environment or VS Code with extension) if they haven’t already.
  • Example: Show a simple Python program to make CoDrone EDU take off, move forward, turn, and land using Python drone control commands.
• (35 min) Independent/Collaborative Coding Challenges: “First Python Drone Flights”
  • Challenge 1 (Programming – Independent): Write a Python program to make CoDrone EDU take off, hover for 3 seconds, and land (Python version of Week 3 Challenge 1).
  • Challenge 2 (Programming – Pair Programming): Pairs write a Python program to make CoDrone EDU take off, move forward 2 meters, then backward 2 meters, and land (Python version of Week 3 Challenge 2). Program entirely in Python.
  • Challenge 3 (Code Translation to Python – Teamwork): Teams take their block-based square path program from Week 3 and translate it into equivalent Python code using Python motion commands and loops (provide basic loop structure example in Python). Run the Python program to make the drone fly a square path.
• (25 min) Real-world Problem-Solving (Mini-Project): “Python Programmed Landing Challenge”
  • Mini-project: Recreate the Programmed Landing Challenge from Week 3, but now program it entirely in Python.
  • Challenge: Teams program CoDrone EDU using Python programming to take off and land autonomously within the designated target landing zone. Focus on using Python takeoff() and land() commands and controlling horizontal movement using Python motion commands to reach the landing zone (using timed movements for now).
  • Compare programming in Blockly/Snap! vs. Python for the same task – discuss differences and initial impressions of Python text-based coding for drones.
• (10 min) Peer Feedback + Q&A: “Python Drone Code Showcase & Syntax Transition Discussion”
  • Teams showcase their Python drone programs and programmed landings.
  • Peer feedback: Focus on correct Python syntax, basic drone control commands in Python, and comparing Python code to Blockly/Snap! blocks. Share Python coding tips and initial impressions of text-based drone programming.
  • Q&A: Open floor for questions about Python syntax for drone control, differences between block-based and text-based drone programming, challenges faced during the Python transition, and advantages/disadvantages of each approach.

Week 6: Python Control Flow for Drones – Python Loops and Sequences for Advanced Flight Paths

• Learning Objective: Learn to use Python control flow structures (for loops, while loops) for drone programming, creating more efficient and complex Python drone programs and enabling more advanced flight paths and routines in Python.
• (20 min) Concept Deep-Dive + Examples: Python Loops for Drone Control
  • Review Python for and while loops from Python 1 & 2 courses (or briefly introduce if students are new to Python loops). Explain how to use loops to repeat drone actions in Python code.
  • Demonstrate Python code examples using for loops to repeat drone movement sequences (e.g., square path in Python) and while loops for conditional drone actions (e.g., move forward until a condition is met – although sensor-based conditions are introduced later, could use time-based condition for now).
  • Compare Python loop syntax to Blockly/Snap! loop blocks and discuss advantages of text-based loops for more flexible control.
  • Example: Show a Python program using a for loop to make the drone fly a square path in Python code.
• (35 min) Independent/Collaborative Coding Challenges: “Python Looping Drone Flights”
  • Challenge 1 (Programming – Independent): Write a Python program using a for loop to make CoDrone EDU perform a repeating back-and-forth movement pattern in Python (Python version of Week 4 Challenge 1, now in Python).
  • Challenge 2 (Programming – Pair Programming): Pairs write a Python program using nested for loops to make CoDrone EDU fly in a grid pattern in Python (Python version of Week 4 Challenge 2, now in Python). Program entirely in Python.
  • Challenge 3 (Creative Python Choreography – Teamwork): Teams enhance their drone choreography routine from Week 4, now programming it in Python using loops and sequences of Python movement commands to create more complex and efficient drone dance routines in Python.
• (25 min) Real-world Problem-Solving (Mini-Project): “Python Programmed Flight Path Challenge (Advanced)”
  • Mini-project: Revisit the Programmed Flight Path Challenge from Week 4.
  • Challenge: Teams program CoDrone EDU using Python programming and loops to autonomously fly through a more complex defined flight path (more waypoints, curves, or patterns) using Python loops for efficient path execution. Focus on using Python for loops and sequences to create precise and repeatable flight paths in Python code.
  • Encourage precise Python programming and adjusting code for accurate flight path execution using text-based Python.
• (10 min) Peer Feedback + Q&A: “Python Loop Code Showcase & Advanced Path Planning Discussion”
  • Teams showcase their Python drone programs using loops and advanced flight paths.
  • Peer feedback: Focus on code clarity, efficient use of Python loops for repetition, smooth drone flight paths programmed in Python, and accuracy in following programmed paths using Python code. Share Python loop programming tips and advanced path planning advice in Python.
  • Q&A: Discuss nested loops in Python for drone control, different uses of range() in Python for loops for flight paths, advantages of Python loops for creating complex and efficient drone flight programs, and real-world applications of programmed flight paths for drones (mapping, surveying, automated tasks).

Week 7: Python Functions for Drone Control – Reusable Python Code for Complex Maneuvers

• Learning Objective: Learn to define and use functions in Python for drone programming, creating reusable Python code blocks for common drone maneuvers and routines, to structure Python drone programs and improve code organization and readability for more complex flight tasks.
• (20 min) Concept Deep-Dive + Examples: Python Functions for Drone Programming
  • Review functions concept from Python 1 & 2 courses. Emphasize the importance of functions for code organization, reusability, and modularity in text-based drone programming.
  • Demonstrate defining Python functions for common drone maneuvers: takeoff_and_hover(duration), fly_square_path(side_length), spin_degrees(angle). Show Python function definition and calling syntax in drone control context.
  • Explain how to use parameters in Python functions to make them more flexible and reusable for different flight scenarios (e.g., move_forward(distance_meters), turn_degrees(angle_degrees)).
  • Example: Show Python code examples defining and calling functions for drone movement, take-off/landing, and other drone tasks in Python.
• (35 min) Independent/Collaborative Coding Challenges: “Python Function-Based Drone Routines”
  • Challenge 1 (Function Creation – Independent): Write Python functions for takeoff_safely() (take-off and hover for a short time), land_smoothly() (land gently), and move_forward_timed(seconds) (move forward for a specified duration). Use these functions to create a simple drone routine.
  • Challenge 2 (Function Combination – Pair Programming): Pairs write Python functions for more complex drone maneuvers, like circle_around_point(radius) (simulated circle for now, precise circle with GPS/sensors later), figure_eight_pattern(size). Combine these functions to create a drone flight pattern routine using Python functions.
  • Challenge 3 (Code Refactoring – Teamwork): Teams take their Python programmed flight path program from Week 6 and refactor it to use functions to encapsulate movement sequences and path segments (e.g., fly_straight_section(distance), turn_corner_degrees(angle)). Improve Python code structure and readability using functions for path planning.
• (25 min) Real-world Problem-Solving (Mini-Project): “Python Function-Based Drone Show”
  • Mini-project: Students create a short “drone show” program in Python using functions.
  • Challenge: Design a drone show routine with at least 3-4 different drone maneuvers or flight patterns. Define Python functions for each maneuver (e.g., fly_square(), circle_pattern(), vertical_climb(), spin_sequence()). Combine these functions in a sequence to create a coordinated drone show routine programmed in Python. Incorporate drone LEDs for visual effects if possible within Python programming.
  • Focus on using Python functions to organize and structure complex drone flight routines, improve code readability, and create reusable code for drone show choreography in Python.
• (10 min) Peer Feedback + Q&A: “Python Function Drone Code Showcase & Advanced Choreography Discussion”
  • Teams showcase their Python drone programs using functions and drone show routines.
  • Peer feedback: Focus on effective use of Python functions for drone control, clear function definitions with parameters, well-structured Python code, and smooth drone choreography programmed with Python functions. Share Python function programming tips and advanced drone choreography design advice.
  • Q&A: Discuss function scope in Python drone programming (revisit and expand if needed), benefits of functions for creating libraries of reusable drone maneuvers, and real-world applications of function-based drone programming in automated drone tasks, drone shows, and complex flight missions.

Week 8: Introduction to Drone Sensors – Reading and Using Sensor Data in Python (Simplified)

• Learning Objective: Introduce drone sensors available on CoDrone EDU (if any are readily accessible and programmable via Python in a simplified manner for this introductory level – otherwise, focus on conceptual understanding and simulated sensor data for this week, and real sensor integration in Robot Explorers 3). Learn conceptually about how drones use sensors and explore basic sensor data reading in Python (if feasible and simplified).
• (20 min) Concept Deep-Dive + Examples: Drone Sensors – Enabling Autonomous Flight
  • Introduce drone sensors: What sensors do drones use? (Barometer, accelerometer, gyroscope – IMU, magnetometer, GPS, optical flow, distance sensors, cameras – depending on drone model). Focus on sensors relevant to CoDrone EDU and its Python programmability (e.g., IMU, barometer, potentially optical flow or distance sensor if accessible in simplified Python API).
  • Explain how sensors allow drones to perceive their environment and enable autonomous flight and intelligent behaviors.
  • Focus conceptually on:
    • IMU (Inertial Measurement Unit): Accelerometer (measures acceleration, tilt), Gyroscope (measures rotation rate). How IMU data is used for drone stabilization and orientation.
    • Barometer: Measures air pressure, used for altitude estimation and control.
    • Optical Flow Sensor (if applicable and simplified access): Measures visual flow, used for precise hovering and position hold indoors.
    • Distance Sensor (if applicable and simplified access): Measures distance to objects, used for obstacle avoidance or distance-based tasks.
  • If simplified Python access to one sensor (e.g., barometer for altitude) is feasible and age-appropriate: Demonstrate Python code for initializing and reading data from that sensor (e.g., drone.get_altitude()). Show example of printing sensor data to the console in Python. If direct sensor access in simplified Python is too complex for this introductory level, focus on conceptual understanding of sensor types and their roles in drone autonomy, and proceed with simulated sensor data or sensor-reactive behaviors based on timed actions for this week, and defer real sensor data integration to Robot Explorers 3.
• (35 min) Independent/Collaborative Coding Challenges: “Drone Sensor Data Exploration (Conceptual or Simplified Python)”
  • Challenge 1 (Conceptual – Sensor Types): Students research and create a presentation (poster, slides, or verbal) explaining different types of drone sensors and their uses (based on information provided in deep-dive and online research if needed – focus on conceptual understanding, not deep technical details).
  • Challenge 2 (Sensor Data Reading – Simplified Python, if feasible): If simplified Python sensor access is feasible: Pairs write a Python program to continuously read and print data from one accessible sensor (e.g., barometer altitude) to the console. Observe how sensor readings change as the drone moves or environment changes.
  • Challenge 3 (Simulated Sensor Reactions – Teamwork, if direct sensor access is too complex): If direct sensor access is too complex for this week, teams create a drone routine where drone simulates reacting to a sensor input. Example: “Simulate” barometer – use time as a proxy for altitude. Program drone to fly upwards for 5 seconds (simulating climb), then say “Altitude reached!” (using print or drone display – if feasible), then hover for 3 seconds (simulating altitude hold), then land. Use comments in Python code to indicate “simulated sensor input” and intended sensor reaction.
• (25 min) Real-world Problem-Solving (Mini-Project): “Drone Altitude Display (Conceptual or Simplified Python)”
  • Mini-project: “Drone Altitude Display” program.
  • Challenge:
    • Conceptual Focus (if sensor access too complex): Design a conceptual interface (drawing, mock-up) for displaying drone altitude information to a user. Consider what information to display (altitude value, units), how to visualize it (numbers, bar graph, etc.), and why altitude information is important for drone pilots.
    • Simplified Python Focus (if sensor access feasible): If simplified Python sensor access is feasible, program CoDrone EDU in Python to: 1) Continuously read altitude data from the barometer sensor using Python code. 2) Display the current altitude value on the screen (console or Hub display) in Python code.
  • Focus on understanding the concept of drone sensors, how they provide data, and (if feasible) basic Python code for accessing and displaying sensor data.
• (10 min) Peer Feedback + Q&A: “Sensor Concept Showcase & Drone Autonomy Discussion”
  • Teams showcase their sensor presentations (conceptual focus) or Python sensor data display programs (simplified Python focus).
  • Peer feedback: Focus on clarity of sensor explanations (conceptual focus), correct Python sensor code (simplified Python focus), and understanding of sensor roles in drone autonomy. Share tips for sensor understanding and basic sensor data handling.
  • Q&A: Discuss different types of drone sensors, how sensor data enables autonomous drone behaviors, limitations of simplified sensor access (if applicable), and prepare for more advanced sensor integration in Robot Explorers 3 (or next level curriculum).

Week 9: Advanced Robotics Challenge – Autonomous Drone Flight (Capstone Project – Part 2)

• Learning Objective: Continue developing the advanced robotics capstone challenge project, focusing on implementing more advanced autonomous drone flight behaviors, integrating sensor data (if feasible and simplified), and refining Python code for complex drone control.
• (20 min) Project Work Time & Individual Guidance: Capstone Project – Advanced Programming & Autonomous Behaviors
  • Teams continue working on their capstone drone projects. Focus on implementing more advanced autonomous flight behaviors using Python programming.
  • If simplified sensor access is feasible: Begin integrating sensor data into Python code to create sensor-reactive drone behaviors (e.g., altitude hold using barometer data, basic obstacle avoidance using distance sensor – if applicable and simplified access).
  • If direct sensor access is too complex: Focus on creating more complex timed autonomous flight routines in Python, simulating sensor-based behaviors using timed sequences and conditional logic based on time or simulated events.
  • Teacher circulates, provides individual guidance on Python code development, sensor integration (if applicable), and helps teams overcome programming challenges.
  • Encourage teams to break down complex autonomous behaviors into smaller, manageable Python functions and code modules.
• (35 min) Collaborative Coding Challenges (Optional, if needed): Focused Python Skill Practice during Project Work
  • If teams are facing specific Python programming challenges related to their capstone projects (e.g., function design, loop implementation, conditional logic in Python drone control, basic sensor data handling in Python – if applicable), provide short, focused Python coding challenges or mini-exercises to help them practice those skills within the context of their drone projects.
  • These challenges can be done individually or in pairs, as needed.
• (25 min) Project Work Time – Continued Development & Refinement: Implementing Autonomy and Testing
  • Teams continue working on their Python capstone drone projects, implementing more advanced autonomous features, refining Python code, and beginning initial testing of their autonomous drone behaviors.
  • Encourage iterative Python code development – write code, test in simulation (if available) or cautiously in flight (in safe area), debug, and refine.
  • Teacher continues to provide support and guidance on Python programming and debugging.
• (10 min) Peer Feedback + Q&A: “Capstone Project Progress – Python Code & Autonomy Check-in”
  • Teams briefly share their capstone project progress – Python code development, implementation of autonomous behaviors, and initial testing experiences.
  • Peer feedback: Focus on Python code structure, implementation of autonomous features in Python, and progress towards project goals. Share Python coding tips for advanced drone control and autonomy.
  • Q&A: Address any questions about Python programming challenges for autonomous drone flight, sensor integration in Python (if applicable), and next steps for project completion in the final week.

Week 10: Capstone Project Completion, Showcase, and Drone Explorer Graduation – Aerial Robot Grand Finale!

• Learning Objective: Complete the capstone drone projects, showcase projects to the class, celebrate learning in drone technology and Python programming, and encourage continued advanced drone and STEM exploration.
• (15 min) Project Work Time – Final Polish: Completing and Preparing Capstone Demos
  • Teams spend the beginning of the session adding final touches to their Python drone projects, completing code, debugging, optimizing flight routines, and preparing for final project demonstrations and presentations.
• (35 min) Project Showcase & Demonstrations: “Drone Explorer Grand Finale Showcase!”
  • Each team presents their completed capstone drone project to the class.
  • Teams demonstrate their CoDrone EDU performing the capstone challenge task autonomously (or semi-autonomously, depending on project complexity and sensor integration).
  • Teams explain their drone’s design, Python code, sensor integration strategies (if applicable), the challenges they faced during development, the Python programming concepts they used, and what they learned about drone technology and programming throughout Drone Explorers.
  • Encourage clear communication, highlighting their Python programming skills for drone control and their achievements in aerial robotics.
  • Positive peer feedback and enthusiastic applause for each team’s impressive drone solution and presentation. Emphasize drone safety during demonstrations and ensure flights are conducted responsibly and in a controlled manner.
• (25 min) Drone Technology and Python Course Review & Advanced STEM Pathways: “Drone Explorer Graduates!”
  • Course recap: Summarize key skills and advanced concepts learned in Drone Explorers: Drone flight principles, drone control, block-based and Python programming for drones, Python control flow, Python functions, introduction to drone sensors (conceptually or simplified Python), Engineering Design Process applied to drone projects. Congratulate students on completing Drone Explorers and becoming “Drone Explorer Graduates!”.
  • Advanced STEM Pathways: Briefly introduce potential next steps in drone technology and STEM learning (more advanced drone programming with Python, exploring drone SDKs and APIs for more control, computer vision with drones, autonomous drone navigation algorithms, drone applications in various fields, competitive drone challenges, drone engineering and design principles, potential career paths in drone technology and related STEM fields). Build excitement for continued drone and STEM exploration.
  • Award certificates of completion for Drone Explorers. Recognize outstanding projects and achievements (optional awards, e.g., “Most Creative Drone Design,” “Best Python Code,” “Most Autonomous Drone,” “Best Flight Performance”). Encourage continued practice, advanced drone learning, and participation in drone clubs or competitions.
• (10 min) Recap + Rewards: “Future Genius Drone Explorers – Python Graduates!”
  • Final words of encouragement and immense appreciation for their hard work, creativity, and progress in drone technology and Python programming throughout the Drone Explorers course.
  • Provide celebratory rewards (e.g., drone-themed stickers, small drone toys, STEM resources, books about drones, Python programming, or aerial robotics for teens, drone-themed treats). Consider small drone-related gifts or resources to encourage continued drone exploration if budget allows.

This “Drone Explorers” curriculum is designed to provide an exciting and progressive introduction to drones, flight principles, and Python programming for 10+ year olds. Remember to prioritize drone safety, adapt the complexity of capstone projects and challenges based on student progress and interests, and emphasize hands-on learning, collaboration, and the Engineering Design Process throughout the course!