AI fluent SmartEducator:

The AI-fluent SmartEducator:

As we prepare for the 3rd decade of the 21st Century, we are witnessing the impact of the twin revolutions, the 4th Industrial revolution ( Klaus Schwab) and the 4th education revolution ( Anthony Seldon). Sir Anthony discussed how Artificial Intelligence will change learning and teaching in universities, and the world into which our students will go. He called on educators everywhere to open their eyes to the fast approaching revolution in Artificial Intelligence, and asked if we are ready to embrace this evolution and shape AI to the best advantage of education and humanity as a whole. The new education policy 2019 has also acknowledged that it is being formulated at a time when an unquestionably disruptive technology AI has emerged, but not pursued the matter any further.

Recent research in neuroscience and psychology suggests that the feeling of uncertainty, fear of rejection or a diminished status all activate the same region in the brain that  physical pain does ( https://www.forbes.com/sites/nicolefisher/2015/12/25/rejection-and-physical-pain-are-the-same-to-your-brain/#6431253f4f87 ). Teachers at School, College, University or Professional institutions will  all be suffering this pain as they realise their lack of familiarity with AI, the driver of both the 4th Industrial revolution and the 4th education revolution. 

We propose here a plan to equip and empower progressive educators to become familiar with AI and leverage it to fulfil the goals of a high quality and equitable education. 

Qualities essential for a modern ( AI-fluent) teacher: 

The most important quality of a modern educator is one of being a lifelong learner. The other important attribute is the appreciation that imparting  the knowledge of subjects is less important than fostering a desire to learn and the ability of learning to learn, and building of learning power. 

The best pro-active response to meet the emerging challenges is to seize the opportunity and become an AI-fluent SmartEducator. Such a person has a Deep Learning mindset ( well versed in deep learning techniques of Machine Learning as well as pedagogy of mastery learning and learning in depth). 

  • Is familiar and well acquainted with commercially available solutions ( both in the cloud and at the edge) for deploying AI in education and their best use situations.
  • Can be actively involved in creating be-spoke solutions for a specific context. 
  • Is well versed with the triple helix of AI-readiness: The Concepts ( theory); The tools; The applications ( educational)
  • Is ready for an era of prosperity and affluence, being ready for ‘deep teaching’, the sexiest job of the future ( 
  • https://medium.com/intuitionmachine/why-teaching-will-be-the-sexiest-job-of-the-future-a-i-economy-b8e1c2ee413e )

This  program has been designed as a six month long program structured as 2 terms of 3 months each.  At the end of each term, the educator-learner will earn a completion certificate. 

At the end of the first term : AI-familiar SmartEducator

At the end of both terms: AI-fluent SmartEducator

The first batch of the program begins from September 2020, around India’s Teachers Day. But new candidates can join at the beginning of every month in what may be called a ‘rolling admission’ process. The International version of the program will be launched on International Teachers Day 2020, that is on Monday 5th October.

First term:

By the end of this term, the learner will have an appreciation of the landscape of AI and allied technologies in education and be able to distinguish hype from hope. During this period, there will be 3 WhatsApp courses of one month ( 4 weeks Monday to Friday) on the following themes :

AIFSE01: The Landscape of AI and education 

AIFSE02: From Data to Learning algorithms

AIFSE03: Deep Learning for educators

Award at the end of first term : “ The AI-familiar SmartEducator”

Second Term:

This is the transformational term. The first month builds an appreciation of the key mathematics such as linear algebra, multivariate calculus, probability, Bayesian models and optimisation. 

During this term, the participants will get into details of the most important methods of AI, that will make them confident about being able to play a meaningful role in the field. In particular they will be able to make their own chatbots. The 3 WhatsApp courses during this term will be :

AIFSE04: Mathematics for Artificial Intelligence and Machine Learning

AIFSE05: Learning to learn AI/ML

AIFSE06: Flourishing and thriving as an educator in the Gig economy 

Award at the end of fourth  term : “ The AI-fluent SmartEducator”

The themes and topics may be tweaked to align to new developments, as well as the needs of the learners ( educators)

To know more, contact Prof. MM Pant by sending him a WhatsApp message at : +919810073724

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Course details :

Each course is structured as 5 sessions per week for 4 weeks, with a total of 20 sessions per course. The week-wise theme for each course is as follows: 

AIFSE01: The Landscape of AI and education :

Week 1: The two big revolutions

Week 2: Impact of AI on education

Week 3: Educational applications of AI

Week 4: Implementation of AI in education

AIFSE02: From Data to Learning algorithms

Week 1: Data

Week2: Learning Analytics

Week 3: Educational Data Mining

Week 4:Learning algorithms ( Machine Learning) 

AIFSE03: Deep Learning for educators

Week 1: Introduction to Artificial Neural Networks

Week 2: Types of Deep Learning Networks

Week 3: Training of Deep Learning Networks

Week 4:  Educational Applications of Deep Learning 

AIFSE04: Mathematics for Artificial Intelligence and Machine Learning

Week 1: Linear Algebra 

Week 2: Calculus for Machine Learning 

Week 3: Statistics and Probability : Bayesian 

Week 4: Optimisation Techniques

AIFSE05: Learning to learn AI/ML:

Week 1: Learning How to Learn?

Week 2: Ultralearning

Week 3: Resources for self-learning AI

Week 4: Python: Self-taught

AIFSE06: Flourishing and thriving as an educator in the Gig economy:

Week 1: The emergence and features of the Gig economy

Week 2: Awaken the solo entrepreneur within 

Week 3: Developing a personal Brand

Week 4: How to flourish and thrive as an educator in the Gig economy?

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Detailed structure of some courses: some gaps are yet to be filled.

AIFSE 01: The Landscape of AI in education: 

Week 1: The two big revolutions 

Session 1.1: The disruptions by the two big revolutions of the present times

Session 1.2: The 4th Industrial Revolution ( Klaus Schwab)

Session 1.3: The 4th Education Revolution ( Anthony Seldon) 

Session 1.4: Technologies of the 4th Industrial Revolution 

Session 1.5: The promises of and expectations from an AI powered education 

Week2: Impact of AI on education

Session 2.1: The elements of an AI enabled educational eco-system

Session 2.2: What can AI do today that has an impact on education?

Session 2.3: Personalisation of learning 

Session 2.4: The 5 levels of AI in education 

Session 2.5: A robot-proof education 

Week 3: Educational applications of AI

Session 3.1: The relationship between Artificial Intelligence, Machine Learning and Deep Learning

Session 3.2: Learning Algorithms: the various approaches

Session 3.3: Intelligent Tutoring Systems

Session 3.4: Chatbots and their applications in education 

Session 3.5: AI and assessment of essay type questions

Week 4: Implementation of AI in education

Session 4.1: The role of the educator in an age of intelligent machines

Session 4.2: The AI empowered autonomous learner

Session 4.3: The AI experience centre: resources for learning AI

Session 4.4: The personal AI readiness Quotient

Session 4.5: Attributes of an AI learning community

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AIFSE 02: From data to Learning Algorithms

Week 1: Data

Session 1.1: The significance of data : Garbage in, Garbage out

Session 1.2: Structured, unstructured and Big Data

Session 1.3: Public sources of datasets

Session 1.4: Bias in data

Session 1.5: Data visualisation

Week 2: Computational Thinking

Session 2.1: The significance of Computational Thinking and its components

Session 2.2: Abstraction

Session 2.3: Pattern Recognition

Session 2.4: Algorithms

Session 2.5: Evaluation

Week 3: Learning Analytics and educational data mining:

Session 3.1: What is learning analytics?

Session 3.2: What is EDM ( Educational Data Mining)?

Session 3.3: Analysis of learning logs 

Session 3.4: Learner types in exploratory learning environments 

Session 3.5: Assessment of motivation in online learning 

Week 4 : Learning Algorithms

Session 4.1: What is an algorithm: it’s attributes and features 

Session 4.2: What is a learning algorithm?

Session 4.3: Common machine learning algorithms 

Session 4.4: Training machine learning algorithms 

Session 4.5: Limitations of learning algorithms 

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 AIFSE3: Deep Learning for educators

Week 1: Introduction to Artificial Neural Networks

Session 1.1: The human neural system

Session 1.2: The Perceptron

Session 1.3: The elements of an artificial neural network

Session 1.4: The evolution of Deep Learning

Session 1.5: The Universal Approximation Theorem

Week 2: Types of Deep Learning Networks: 

Session 2.1: Recursive and recurrent neural networks

Session 2.2: Convolutional Neural Networks

Session 2.3: Introduction to Ensemble Learning 

Session 2.4: Advanced Ensemble techniques

Session 2.5: Adversarial Networks

Week 3: Training of Deep Learning Networks

Session 3.1: Components of the  Learning Algorithm

Session 3.2: Learning networks weights is hard

Session 3.3: Neural Nets Learn a Mapping Function

Session 3.4: Techniques for efficient training of Deep Learning Networks

Session 3.5: Why are deep learning networks hard to train?

Week 4: Educational Applications of Deep Learning 

Session 4.1: Face recognition

Session 4.2: Machine Translation

Session 4.3: Assessments

Session 4.4: Personalised recommendation systems 

Session 4.5: Chatbots for learning

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AIFSE04: Mathematics for Artificial Intelligence and Machine Learning

Week 1: Linear Algebra :

Session 1.1: Scalars, vectors and tensors

Session 1.2: Euclidean vector spaces

Session 1.3: Matrices and operations with matrices

Session 1.4: Eigenvalues and eigenvectors

Session 1.5: Singular value decomposition

Week 2: Calculus for Machine Learning 

Session 2.1: Introduction to calculus 

Session 2.2: Multivariate calculus

Session 2.3: Chain rule and its applications

Session 2.4: Taylor series and linearisation

Session 2.5: Regression

Week 3: Statistics and Probability : Bayesian 

Session 3.1: Combinatorics

Session 3.2: Probability Rules & Axioms

Session 3.3: Bayes’ Theorem

Session 3.4: Standard Distributions

Session 3.5: Sampling methods

Week 4: Optimisation Techniques

Session 4.1: The Simplex method

Session 4.2: Convexity

Session 4.3: Loss functions

Session 4.4: Gradient descent

Session 4.5: Stochastic gradient descent

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AIFSE05: Learning to learn AI/ML:

Week 1: Learning How to Learn?

Session 1.1: Identify your own learning needs

Session 1.2: Set learning goals to address those needs

Session 1.3: Identify resources (human, as well as material) to help you achieve your learning goals

Session 1.4: Apply appropriate learning strategies

Session 1.5: Evaluate the outcomes of your learning

Week 2: Ultralearning 

Session 2.1: What is ultra-Learning?

Session 2.2: Why Ultralearning is a good approach to learning AI/ML?

Session 2.3: The key steps to ultralearn

Session 2.4: Managing your Ultralearning project

Session 2.5: How to ultra-learn AI/ML ?

Week 3: Resources for self-Learning AI

Session 3.1: Books

Session 3.2:Communities: GitHub

Session 3.3: Courses

Session 3.4: MOOCs

Session 3.5: AIY

Week 4: Python: self-taught 

Session 4.1:  Why Python for Machine Learning ?

Session 4.2: Basics of Python syntax

Session 4.3: numpy, scipy and scikit-learn

Session 4.4: Python for KNN classification 

Session 4.5: Case studies of self-taught Python programmers

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AIFSE 6: Flourishing and thriving as an educator in the Gig economy:

Week 1: Emergence and features of the Gig economy

Session 1.1: A brief history of the Gig economy 

Session 1.2: The re-engineering movement 

Session 1.3: Drivers of the Gig economy 

Session 1.4: Challenges of the Gig economy 

Session 1.5: AI and the Gig economy 

Week 2: Awaken the solo entrepreneur within

Session 2.1: The process of waking up:as an entrepreneur 

Session 2.2: Myths and realities about entrepreneurship. Entrepreneurship is about creating value.

Session 2.3: Business ideas : how to record and filter business ideas. 

Session 2.4: The straightforward business plan

Session 2.5: Educators as Entrepreneurs 

Week 3: Developing a personal Brand

Session 3.1: The principles of personal branding

Session 3.2: Your Digital footprint

Session 3.3: Conversational Capital

Session 3.4: Celebrity educators

Session 3.5: Managing the commercial value of your personal Brand

Week 4: How to flourish and thrive as an educator in the Gig economy?

Session 4.1: The skills for success in 2020 according to the World Economic Forum

Session 4.2: Flourishing and thriving in the Gig economy 

Session 4.3: The most paying Gigs in the year 2020

Session 4.4: Some illustrative examples 

Session 4.5: The future of the Gig economy : Blockchains 

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