Day 1 — Understand & Explore
AI Foundations, Data & Python
Students begin by understanding how smart systems work and how data becomes the foundation of machine learning.
Students Learn
- What makes a system artificially intelligent
- Human intelligence vs machine intelligence
- Sense → Process → Respond
- AI vs Machine Learning
- Everyday AI systems
- Types of data
- Data vs information
- How machines learn from examples
- Features, labels and training data
- Classification vs prediction
- Python variables and data types
- Input and output
- Lists
- if / elif / else
- for and while loops
- Functions
Day 1 includes six Python labs, taking students from basic variables to building their own quiz program.
Day 1 Activities
Spot the AI Around You • Human vs Machine • Draw with Numbers • Data to Decision • Be the Model • Classification or Prediction • Train a Human Machine • Python Coding Challenges • AI + Python Knowledge Check
Students finish the day able to work with lists, decisions, loops and functions in Python.
Day 2 — Code & Create
Python, Machine Learning & Generative AI
Day 2 moves from basic programming to working with real datasets, patterns and Generative AI.
Students Learn
- Analysing datasets with Python
- Strings and text processing
- Finding patterns with functions
- Supervised learning
- Unsupervised learning
- Training Data → Model → Prediction
- Data quality and model errors
- Generative AI
- Chatbots
- Prompt Engineering
- AI hallucinations
- Privacy
- Bias and deepfakes
- Verifying AI-generated information
Students learn the Class 8 prompt structure:
Instruction + Context + Goal
Day 2 Activities
Dataset Analysis • Review Sorter • Pattern Finding • Student Marks Analyzer • Build a Classifier by Hand • Generative AI Studio • Prompt Challenge • Catch AI Mistakes • Responsible AI Activities • Level 2 AI Quiz
The Generative AI section covers the creation of text, images, music and video.
Day 3 — Build & Present
From AI Learner to AI Builder
Day 3 is focused mainly on building.
Students explore:
- Computer Vision
- Image Classification
- Natural Language Processing
- Chatbots
- Robotics
- Smart Devices
- AI project planning
- Flowcharts
- Testing and improvement
- Prototype development
- Young Innovator Pitch
- AI careers
- AI portfolio development
Students then use these skills in the AI Innovation Challenge, where they create a prototype for a real user and present it to judges.
Projects Students Build & Explore
1. AI Quiz Program
Students combine Python lists, loops, conditions, input and functions to create an interactive AI-themed quiz.
The program asks questions, checks answers, keeps score and provides a final result. Students can extend it by adding their own questions, hints and personalised greetings.
Implementation Tools:
Python
Recommended Platform: Replit / Google Colab
2. Student Marks Analyzer
Students build a Python program that takes real student marks and produces a useful class report.
The program:
- Stores at least eight marks
- Finds the highest and lowest
- Calculates the average
- Calculates the spread
- Assigns grades
- Counts students above average
- Gives one useful observation for the teacher
Implementation Tools:
Python
Recommended Platform: Replit / Google Colab
3. Review Sorter
Students work with text as data. They create a simple Python system that reads short reviews and attempts to classify them as positive, negative or unclear using words and conditions.
Implementation Tools:
Python
Recommended Platform: Replit / Google Colab
4. Image Classifier
Students design an image-classification system for a real problem.
The workbook example is a system that sorts photographs of waste into categories such as:
Paper • Plastic • Food Waste • Other
Students decide the classes, training data, variation in images and what the system should do when it makes a wrong prediction.
Recommended Implementation Tool:
Google Teachable Machine
Students can collect images using a phone or webcam, train the classifier and test how well it handles new images.
5. Study Assistant Chatbot
Students design a chatbot specifically for helping with study-related questions.
They define a small set of intents, plan different ways students might ask the same question, write appropriate responses and decide what the chatbot should do when it does not understand.
They also decide what requests the chatbot must refuse and clearly tell users that they are interacting with a bot.
Recommended Implementation Tools:
ChatGPT / Gemini – for conversational AI prototyping
Botpress – for building a structured chatbot with defined intents and responses
6. Smart Device
Students invent a smart device for a real task at school or home using the model:
Sense → Think → Act
They identify the sensors, decision-making process and actuators, and also specify something the device must never do.
The workbook’s example is a classroom air monitor that senses CO₂ level, temperature and occupancy, then provides an alert when the room needs attention.
Recommended Implementation Tools:
micro:bit • MakeCode
For a more advanced demonstration:
micro:bit + Sensors such as temperature, light or distance sensors.
Computer Vision Exploration
Students learn how a computer converts an image into an AI decision:
Image → Pixels → Patterns → Answer
They also understand the difference between image classification and object detection, with examples from farming, healthcare, traffic and banking.
Recommended Implementation Tools:
Google Teachable Machine – image classification
Phone / Webcam – capturing training and testing images
NLP & Chatbot Exploration
Students learn how AI processes language through:
Question → Words → Intent → Response
They explore:
- Keyword spotting
- Sentiment analysis
- Text classification
- Chatbot intent detection
Recommended Implementation Tools:
Python – simple keyword and sentiment activities
ChatGPT / Gemini – modern conversational AI
Botpress – structured chatbot prototype
Creative Generative AI Activities
AI Text Creation
Students explore Generative AI for explanations, summaries, stories, emails, quiz questions and Python code.
Recommended Tools:
ChatGPT • Claude • Gemini
AI Image Creation
Students explore how AI can generate illustrations, posters, diagrams and concept art from text descriptions.
Recommended Tools:
DALL·E • Canva AI
AI Music Creation
Students explore creating backing tracks and jingles using descriptions of style, tempo and instruments.
Recommended Tool:
Suno
AI Video Creation
Students explore creating short video clips from written scene descriptions.
Recommended Tool:
Runway
Final Project — AI Innovation Challenge
The main Class 8 challenge asks every team to:
Build Something a Real Person Could Use
Students have three hours to identify a real problem, develop a prototype, test it on someone outside their team, improve it and prepare their pitch.
Project Process
- Problem & User
Identify a real problem and speak to the person experiencing it. - Design & Flowchart
Plan the system using Input → Process → Output and select the technology. - Build the Prototype
Create the smallest working version that demonstrates the idea. - Test It
Give it to someone outside the team without explaining how to use it. - Improve It
Fix at least one problem discovered during testing. - Prepare the Pitch
Practise the presentation and ensure every team member participates.
Suggested Tools for the Innovation Challenge
Teams can choose tools according to the problem they decide to solve:
Python Project: Python + Replit / Google Colab
Image Classifier: Teachable Machine
Study Chatbot: ChatGPT / Gemini / Botpress
Smart Device: micro:bit + MakeCode
AI Text: ChatGPT / Claude / Gemini
AI Images: DALL·E / Canva AI
Presentation: Canva / Google Slides
Prototype Design: Canva / Figma
Phone Camera: for image-data or computer-vision prototypes
The workbook itself allows teams to choose technologies such as Python, no-code AI, Generative AI, computer vision or NLP, and its project worksheet asks students to record all the tools they used.
Young Innovator Pitch
After building and testing the prototype, teams present their project to judges.
The pitch covers:
- The Problem & User
Who has the problem? - The Solution
What does the project do? - How It Works
Demonstrate the prototype. - Benefits
Who does it help and how can success be measured? - Future Improvements
What could be improved and what can the current prototype not yet do?
Projects are judged on problem clarity, AI solution, prototype, creativity, teamwork and presentation.
Skills Students Develop
Through the three-day workshop, students develop:
AI Understanding • Python Programming • Data Analysis • Machine Learning Concepts • Classification & Prediction • Prompt Engineering • Generative AI Skills • Computer Vision Awareness • NLP & Chatbot Design • Robotics Thinking • Testing & Debugging • Responsible AI Use • Problem-Solving • Teamwork • Prototype Development • Presentation Skills
Understand → Code → Create → Build → Present
3 Days. From Understanding AI to Building a Working Prototype.
The Class 8 programme progresses from AI foundations and Python, through machine learning and Generative AI, and concludes with computer vision, chatbots, robotics, a real-world AI Innovation Challenge and a Young Innovator Pitch.
SKK Educational Foundation
AI Learning + Python Programming + Machine Learning + Project Execution