Course Curriculum & Learning Progression
The PictoBlox curriculum at Axilearn is hands-on, project-based learning aligned with NEP 2020. Each level builds on the one before:
- Level 1: core coding concepts (sequence, loops, events, conditions and variables) through animations and games
- Level 2: PictoBlox's ready-made AI extensions to add vision, speech, text and chatbot abilities to projects
- Level 3: the PictoBlox Machine Learning Environment, where learners collect data, train their own models and use them in real-world AI applications
Live + Lab Learning Format:
Learners get interactive instructor-led live sessions paired with practical hands-on lab practice and competition-ready capstone project guidance.
Structured Level Breakdown
Level 1
LIVE + LAB SESSIONS
Code Quest 🎮
What PictoBlox is, a tour of its interface, and core block-coding concepts, learned by animating characters and building fun interactive games.
Prerequisites: None
Blocks & Extensions Used: Motion, Looks, Sound, Events, Control, Sensing, Operators, Variables, My Blocks, Pen, Music
Topics Covered:
- What is PictoBlox? A Scratch-based block and Python coding platform by STEMpedia that supports languages like Hindi and Gujarati
- Getting Started: installing PictoBlox, choosing Block Coding, and a tour of the stage, sprites, block palette and scripting area; your first script to make Tobi move
- Motion: X–Y coordinates, move, turn, glide and bounce
- Looks: costumes, backdrops, say/think and colour effects
- Sound & Music: the sound library, recording your own sounds, and composing beats
- Events & Loops: green flag, key presses, sprite clicks, repeat and forever
- Conditions & Sensing: if/else, touching, key pressed and ask-and-answer
- Variables & Operators: score, lives, timers and random numbers
- Broadcasts & Clones: messages between sprites, and spawning objects for games
- Creative Tools: the Paint Editor for your own sprites, and Pen for drawing patterns
Mini Projects:
Dancing Tobi · Animated Greeting Card · Walking Animation · Spirograph Art · Maze Escape · Balloon Pop (clones) · Maths Quiz
Project Output:
My First Arcade Game, a complete game (such as Catch the Fruit or Space Shooter) with an animated start screen, score, lives, levels and sound effects
Level 2
LIVE + LAB SESSIONS
AI Superpowers ⚡
Adding real AI to block-coded projects. Using PictoBlox's AI extensions, learners' projects detect faces and emotions, track hands and bodies, recognise objects and text, listen, speak, translate and chat.
Prerequisites: Code Quest, or basic Scratch/PictoBlox skills
Extensions Used: Text to Speech, Translate, Speech Recognition, Face Detection, Human Body, Object Detection, Text Recognition, Computer Vision, Recognition Cards, QR Scanner, Video Sensing, ChatGPT
Topics Covered:
- What is AI? How computers see, hear and understand, with everyday examples
- Voice AI: making sprites speak with Text to Speech and speak other languages with Translate
- Listening AI: Speech Recognition for voice commands and voice-controlled games
- Face AI: face detection, expressions, age and emotion estimation, facial landmarks, and recognising known faces
- Body & Hand AI: tracking body pose and hand landmarks to control sprites without touching the keyboard
- Object AI: detecting and counting everyday objects with the camera
- Reading AI: recognising printed and handwritten text, and scanning QR codes and Recognition Cards
- Scene AI: the Computer Vision extension for identifying landmarks, brands and objects in images
- Chatbot AI: the ChatGPT extension for asking questions, getting AI responses and building a chatbot
- Responsible AI: privacy, consent for camera and face data, and when AI gets it wrong
Mini Projects:
Talking Translator Tobi · Voice-Controlled Car Game · Emotion Mirror · Face Filter Studio · Air Canvas (finger drawing) · Smart Object Counter · Handwriting Reader · QR Code Treasure Hunt · ChatGPT Storyteller
Project Output:
My AI Buddy, a virtual assistant sprite that recognises its user's face, greets them by name, reacts to their mood, answers spoken questions using ChatGPT, and replies aloud in the language they choose
Level 3
LIVE + LAB SESSIONS
ML Makers Lab 🧠
Training your own AI in the PictoBlox Machine Learning Environment. Learners collect data, train, test and improve all seven model types (image, object detection, hand pose, pose, audio, text, and number/regression), then use them in block-coded AI apps.
Prerequisites: AI Superpowers
Tools Used: PictoBlox ML Environment (all 7 model types), NLP extension, ML with Teachable Machine extension
Topics Covered:
- How Machine Learning Works: learning from examples, classes and labels, and training vs testing
- Opening the ML Environment: creating a project, choosing a model type, and the train → test → export workflow
- Building Good Datasets: webcam capture, uploading and importing images, balanced classes, and "background" classes
- Image Classifier: recognising categories of images
- Object Detection (ML): drawing bounding boxes and training a detector for your own objects
- Hand Pose Classifier: recognising hand gestures and signs
- Pose Classifier: recognising full-body poses and activities
- Audio Classifier: recognising sounds and spoken words
- Text Classifier & NLP: understanding intent and sentiment in text
- Number Classifier & Regression: making predictions from tables of numbers
- Testing & Improving Models: training settings (epochs, batch size, learning rate), finding wrong predictions, adding better data and retraining
- Using Your Models: exporting to block coding and connecting predictions to sprites, sounds and speech
- Teachable Machine: loading an externally trained model by link and comparing it with the ML Environment
Mini Projects:
Smart Waste Sorter (Image) · Safety Helmet Detector (Object Detection) · Rock-Paper-Scissors AI (Hand Pose) · Yoga Pose Coach (Pose) · Clap & Whistle Commander (Audio) · Movie Review Mood Meter (Text) · Exam Score Predictor (Regression)
Project Output:
AI for Good Capstone, an original app that solves a real problem using at least one custom-trained ML model together with Level 2 AI extensions (for example a Sign Language Interpreter, a Plant Disease Detector or an AI Fitness Coach). Learners submit it with their dataset, test results and a demo video, ready for AI competitions such as Codeavour.
Course Summary
Target Age:
Ages 8–16
Levels:
3 Levels: Code Quest → AI Superpowers → ML Makers Lab
Total Duration:
30 Weeks (~60 hours)
Format:
Live + Lab
Coding Mode:
100% Block Coding
Platform:
PictoBlox Desktop App
Requirements:
Laptop/Desktop, Webcam & Mic
Certificate:
Axilearn Certified (Each Level)
Key Highlights:
- A clear path: games & animation → AI extensions → machine learning
- Learn in English, Hindi or Gujarati with PictoBlox's multilingual interface
- 12+ AI extensions: face, body, object & text recognition, speech, translation & ChatGPT
- Train all 7 ML model types in the PictoBlox ML Environment
- Responsible AI: privacy, consent and AI limitations
- 23 mini projects and 3 level-end projects, including an AI for Good capstone
Batch & Technical Notes:
- Desktop App Requirement: Level 3 ML Environment model creation requires PictoBlox Desktop App (Windows, macOS, Linux).
- Cloud Extension Credits: Extensions like ChatGPT, Computer Vision & Speech Recognition operate on credit allocation.
- Multilingual Option: Native support for English, Hindi, and Gujarati interfaces.
- Young Learners (6–8): Junior track option available via PictoBlox Junior.
