The Curriculum

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.

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.

Levels	Pathways ( outcomes )	Purpose
Intro	What, Why and Platform orientation	How to use HAI Labs
Level1	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	Hands-on
	Use classification-based machine learning models in applications	Hands-on
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	Activity
	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	Hands-on
	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	Quiz
	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)	Activity
	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	Quiz
	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	Activity
	Python modules /libraries used for regression problems	Python:Libraries/Packages used for regression problems
	Quiz : Python regression modules	Quiz
	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..