At Hello AI, we provide a platform that is engaging, accessible, and relevant to children's learning needs. 
We have academic collaborations with prestigious institutions such as University College London, Stanford University-SPARK, the  Indian Institute of Technology, and the Indian Institute of Management. We also work with researchers from the Cochin University of Science and Technology to use our emission data for meaningful research. 

These collaborations validate the quality and relevance of our content and ensure that our platform is aligned with the latest advancements in the field of data science.

The Curriculum

This curriculum is designed by AI and ML practitioners; the objective is to provide a comprehensive introduction to data, machine learning (ML), artificial intelligence (AI), ethics, and application development. It is divided into 10 levels, each focusing on different capabilities, activities, and hands-on projects. We believe everyone needs to be AI and data literate!

Are there any prerequisites?

No specific prerequisites are required for this course. It is designed to provide a comprehensive introduction to the topics of data, machine learning, AI, ethics, and application development. However, having a basic understanding of math, statistics, computer literacy, and programming concepts would be beneficial. Students should be comfortable using a computer, have basic problem-solving skills, and be willing to learn and explore new concepts. The course is designed to be accessible and suitable for varying levels of prior knowledge and experience.


Pathways ( outcomes )



What, Why and Platform orientation

How to use HAI Labs


Introduction to ML and AI that you interact with often and influences it has in day to day life

Inspire and excite

Understanding ML and the difference between ML and traditional programming

Big picture view and the need, What it is and what it is not

What is classification

How do we classify, and how the machine does?

Introduction to Data

Concepts about Data

Introduce basic concepts of collection, labelling & cleaning

hands on

Introduce stages/phases of ML

Big picture view

Refresh - Scratch


Use classification-based machine learning models in applications


Level 2

Undestanding the basic difference between Supervised & Unsupervised ML

The big picture of classification vs Clustering with examples

Activitiy to understand clustering vs classification


More on classification,Different forms of data: image,text,audio,numbers

Data: image,text,sound/audio or numbers

Classifiction :scratch projects based on text & audio


Understanding of data set through number classification problem

Getting used to data set,numbers through projects

Level 3

More on supervised learning :Classification & Regression

Types of supervised learning Classification & Regression ,its diffrence , how to decide which type to be used; with examples

Introduction to operations on numbers ,basic statistical operations (mean,median ,mode etc)

Numbers ,Mathamatical oprations on numbers in ML aspect

Mathematical operations on numbers


Inroduction to Python (Turtle) Block vs Text Based programming

Understanding text based programming(Python) by comparing with scratch blocks

Get familair with Python:Simple drawing programs : shape/patterns , simple games in Python (turtle)

Hands on

Level 4

Understanding Data Visualization ,Its importance,different ways of presenting data(plot,bar graph etc)

Big picture of Data representation /visuallization

Activitiy to understand Data Visualization (plots,graphs)


Python modules /libraries used for data plotting & data manupulation (mean,mode,median etc)

Python:Libraries/Packages used for graphs/plotting & mathamatical(statistics) calculation

Quiz : Python graph/plot/math modules


Python projects for visualzation of data and manupulation of data (mean,mode,median)

Hands on

Level 5

More on Regression :Prediction,Predictor varaibles

Understanding predictor varaibles with examples

Activitiy to identify Predictor varaibles


Python modules /libraries used for regression problems

Python:Libraries/Packages used for regression problems

Quiz : Python regression modules


Python projects for Regression type problems

Hands on

Learning Design Framework

Every child is unique, personalization is in the core of our design; addressing the needs of all kind of learners – Visual, conversational, kinesthetic etc..