AIIE Digital Tree

01 · Six learning areas

Explore how AIIE works.

Open any area for the detail: the framework, the Intelligence Hub, STEM foundations, hardware projects, the learner dashboard and project-based learning.

The learning framework

From foundations to a finished project

The Digital Tree gives learners a progression to follow. Each stage builds on the previous one, with activities suited to the learner group and programme.

Learning pathway

Learn: Start with core ideas and a clear question.

Explore: Try tools, sensors and examples to see how things work.

Experiment: Change one input at a time and record what happens.

Build: Connect a circuit, write code or assemble a prototype.

Test: Compare the result with what you expected.

Improve: Use the test evidence to refine the design.

Innovate: Combine ideas to address a different problem.

Showcase: Present your project and explain the evidence behind it.

Technology branches

AI: Explore how examples and data help a model recognise patterns.

STEM: Investigate the science and mathematics behind a working system.

Coding: Write instructions, then test whether the program behaves as intended.

Electronics: Connect components and trace how current and signals move.

Sensors: Measure light, distance, temperature or motion.

Embedded Systems: Program a small board to sense an input and control an output.

Robotics: Combine movement, sensing and decisions in a model.

IoT: Explore the journey from a device reading to a connected dashboard.

Computer Vision: Use images as input and examine what a model can recognise.

Roots — foundations and awareness. Develop safe habits around electronics, the environment and responsible technology use.
Trunk — core practical skills. Practise mathematics, science, coding, measurement and basic electronics.
Branches — technology exploration. Connect those skills with sensors, embedded systems, robotics, IoT, data and AI.
Leaves — communication and teamwork. Explain decisions, document observations and improve through feedback.
Fruits — projects and showcase. Present a completed build, the test results and what you would improve next.

Institutions can discuss a three-day workshop, a three-month programme or a year-long learning journey, depending on the time, facilities and depth required.

Browse programmes Discuss a learning plan

Practice and exploration

AIIE Intelligence Hub: turn an idea into an experiment

Begin with a question, make a prediction and try a small activity. Guided course material helps learners connect an explanation with something they can observe, measure or change.

The hub pipeline

Data
AI model
Computer vision
Decision
Action

Choose a learning zone to explore an example workflow. Activities and equipment are planned around your programme.

Explore the lab zones

AI Lab

Data
Model
Prediction
Application

Compare predictions with labelled examples and discuss where a model makes mistakes.

STEM Lab

Question
Experiment
Measure
Conclusion

Test a prediction with a simple experiment and explain the measurements.

Robotics Lab

Sense
Plan
Move
Improve

Explore how a distance reading can change the motion of a small robot.

IoT Lab

Sensor
Wi-Fi
Cloud
Dashboard

Trace how a reading travels from a sensor to a connected display.

Embedded Lab

Code
Flash
Run
Debug

Change a small program and observe how a board responds.

Computer Vision Lab

Camera
Image
Model
Detection
Action

Compare camera inputs and discuss whether a recognition result is reliable.

Hardware Lab

Sensor
Microcontroller
Processing
Actuator

Follow a signal from a physical input to a light, buzzer or motor.

Innovation Lab

Idea
Prototype
Test
Showcase

Combine skills in a prototype and use testing to decide what to improve.

Explore a concept. Use the notes, examples and activities provided in your course.
Practise coding. The learner platform includes a Python playground with exercises in variables, conditions, loops and functions.
Investigate a result. Change an input, compare the output and explain why it changed.
Connect to a build. Apply the idea in a circuit, sensor activity or programme project.

Find a course Open my dashboard

Science · Technology · Engineering · Mathematics

Understand how things work by testing them

STEM connects a classroom concept with a practical question. A wind-energy model, for example, brings together motion, electricity, measurement and the design of a working system.

Four disciplines, one practical question

Science

Energy · Motion · Electricity · Environment

Observe how light changes with distance, or compare temperature readings over time.

Technology

Coding · AI · Sensors · Digital systems

Use a program to turn a reading into useful information.

Engineering

Circuits · Prototypes · System design

Build a model, find a fault and test an improvement.

Mathematics

Measurement · Logic · Data · Geometry

Plot observations, calculate a value and explain a pattern.

Try it: LED brightness

Voltage 3.3 V

Current: 5.9 mA

A simplified model: I = (V − 2 V) ÷ 220 Ω, with no current below the assumed 2 V LED drop. Move the slider to compare voltage and current.

Science: observe energy, motion, materials and the environment.
Technology: use coding, electronics and sensors to collect or process information.
Engineering: assemble a model, troubleshoot it and test an improvement.
Mathematics: measure, compare, calculate and interpret the results.

The outcome is a learner who can explain both the concept and the evidence from an activity.

Explore learning topics

Examples from AIIE practical learning

See the connection between an input and an action

These project examples connect physical components with the ideas behind them. Learners can trace what is sensed, how it is processed and what the system does next.

Explore project categories

Project idea

Smart Object Counter

Question: How can we count objects in an image?
AI or logic: Object detection
Hardware: Camera and a small computer

Camera
Detection
Count
Display

Explore the build

Choose the input and define a useful output.
Collect suitable components for a supervised prototype.
Connect the hardware and write or train the logic.
Test with different inputs and record the results.
Improve one part, then explain what changed.

Project idea

Sensor Pattern Detector

Question: How can we spot an unusual reading?
AI or logic: Pattern recognition or threshold logic
Hardware: Microcontroller, light or temperature sensor

Sensor
Data
Pattern check
Alert

Explore the build

Choose the input and define a useful output.
Collect suitable components for a supervised prototype.
Connect the hardware and write or train the logic.
Test with different inputs and record the results.
Improve one part, then explain what changed.

Project idea

Obstacle-Avoiding Robot

Question: How can a model avoid an obstacle?
AI or logic: Distance-based decisions
Hardware: Ultrasonic sensor, microcontroller and motors

Distance
Decision
Motor driver
Wheels

Explore the build

Choose the input and define a useful output.
Collect suitable components for a supervised prototype.
Connect the hardware and write or train the logic.
Test with different inputs and record the results.
Improve one part, then explain what changed.

Project idea

Connected Room Monitor

Question: How do room conditions change during the day?
AI or logic: Data comparison and trend analysis
Hardware: ESP32 with temperature and humidity sensors

Sensor
ESP32
Wi-Fi
Dashboard

Explore the build

Choose the input and define a useful output.
Collect suitable components for a supervised prototype.
Connect the hardware and write or train the logic.
Test with different inputs and record the results.
Improve one part, then explain what changed.

Project idea

Tiny Gesture Recogniser

Question: How can a gesture become a command?
AI or logic: Small on-device model
Hardware: Microcontroller and motion sensor

Motion
Model
Controller
LED action

Explore the build

Choose the input and define a useful output.
Collect suitable components for a supervised prototype.
Connect the hardware and write or train the logic.
Test with different inputs and record the results.
Improve one part, then explain what changed.

Project idea

Waste Sorting Assistant

Question: How could a prototype distinguish materials?
AI or logic: Image classification
Hardware: Camera, controller and a servo

Camera
Classifier
Controller
Gate

Explore the build

Choose the input and define a useful output.
Collect suitable components for a supervised prototype.
Connect the hardware and write or train the logic.
Test with different inputs and record the results.
Improve one part, then explain what changed.

Project idea

Smart Irrigation

Question: How can we respond to dry soil?
AI or logic: Sensor thresholds or a data-based decision
Hardware: Soil moisture sensor, controller and model pump

Soil sensor
Data
Decision
Controller
Pump

Explore the build

Choose the input and define a useful output.
Collect suitable components for a supervised prototype.
Connect the hardware and write or train the logic.
Test with different inputs and record the results.
Improve one part, then explain what changed.

Project idea

Air Quality Monitor

Question: How can we compare environmental readings?
AI or logic: Data logging and pattern analysis
Hardware: Air sensors and a microcontroller

Air sensor
Data log
Comparison
Display

Explore the build

Choose the input and define a useful output.
Collect suitable components for a supervised prototype.
Connect the hardware and write or train the logic.
Test with different inputs and record the results.
Improve one part, then explain what changed.

Project idea

Posture Reminder

Question: How could a model recognise a change in posture?
AI or logic: Motion classification
Hardware: Motion sensor, microcontroller and buzzer

Motion
Classifier
Controller
Reminder

Explore the build

Choose the input and define a useful output.
Collect suitable components for a supervised prototype.
Connect the hardware and write or train the logic.
Test with different inputs and record the results.
Improve one part, then explain what changed.

A learning prototype for exploring motion sensing, not a medical assessment.

Project idea

Model Car Alert

Question: How can a model respond to a nearby obstacle?
AI or logic: Distance or image-based detection
Hardware: Camera or distance sensor with a model vehicle

Input
Detection
Controller
Alert

Explore the build

Choose the input and define a useful output.
Collect suitable components for a supervised prototype.
Connect the hardware and write or train the logic.
Test with different inputs and record the results.
Improve one part, then explain what changed.

Air & Water Quality: take sensor readings and display the measured environmental conditions.
Water Tank Alert: use an ultrasonic sensor, a microcontroller and a display to show the water level and trigger an alert.
Wind Energy: connect a small turbine to a meter and investigate how movement becomes electrical output.
Hand Gesture Control: use camera input and AI classification to turn a hand movement into an LED command.

Discuss the learner level, equipment and programme scope with us to choose a suitable build.

Discuss a practical project

Your learner account

Pick up where you left off

Sign in to your GlobalScalar account to return to your learning. The learner dashboard and My Learning pages use your enrolments and saved lesson activity to show your next steps.

My learning journey

Ask: Start with a question you can investigate.

Think: Consider what you already know and what to find out.

Learn: Work through the ideas and examples in your course.

Experiment: Try an activity and record what happens.

Build: Apply your skills in a small working prototype.

Test: Check whether the result matches your aim.

Improve: Use evidence and feedback to refine the work.

Project: Bring the pieces together and document your decisions.

Showcase: Present the result and explain your learning.

Explore your learner workspace

My profile

View your learner profile and learning activity.

Open my profile

My learning

Return to enrolled courses and programmes, resume lessons and review recorded progress.

Open My Learning

Coding practice

Practise Python through the learner playground.

Open coding playground

My projects

View your projects and continue an available project submission.

Open my projects

My certificates

View certificates issued for completed eligible learning.

View my certificates

Find a course

Browse currently published courses and programmes.

Browse the catalogue

Find your enrolled courses and programmes.
Resume a lesson and see the next lesson in your learning path.
Review recorded progress, completed lessons and recent activity.
Explore available courses and use the coding playground.

Open my dashboard Create a learner account

Use the AIIE UID issued by your school, college or GlobalScalar. If you need help, contact us to arrange access.

A method for meaningful work

Build, test, explain, improve

A project starts with a question the learner can investigate. For a water-tank alert, that question might be: how can we detect a rising water level and warn someone in time?

The building cycle

Real-world problem: Choose something worth investigating.

Ask: Define a focused question and a useful outcome.

Research: Look at existing ideas and collect relevant observations.

Learn: Develop the knowledge needed for the next step.

Experiment: Compare a few possible approaches.

Build: Assemble a prototype that you can test.

Test: Record evidence under different conditions.

Improve: Change one part and repeat the test.

Final project: Document the build, results and remaining limits.

Showcase: Present your work and explain your decisions.