The AI-ready educator

The AI-ready educator: Concept note: 

Attributes of an AI-ready educator:

  • 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 for deploying AI in education and their best use situations.
  • Can be actively involved in creating be-spoke solutions for a specific context. 

Goals : 

To produce about 500 AI-ready educators over 2020 to 2022.

Year 1: 2020 : 100

Yesr 2: 2021 : 150

Tear 3: 2022 : 250

A year long program structured as 2 terms of 24 weeks each. 

The remaining 4 weeks for catching up time, vacations and other holidays.

One theme per week. 

List of themes : 

Term 1: 24 weeks ( 6 clusters) 

Broad features :

  • Two 24 week terms 
  • Structured as 1 week modules
  • Aggregated as 12 clusters, spread over 2 terms, a cluster being capable of pursued in about a month. 

Prerequisites: 

1: Learning how to learn? A one month  program on becoming an effective self-directed learner.

2: The AI4educators course : a one month awareness course for all educators.

Proposed list of weeklong modules:

Term 1: 

Cluster A: AI in education: applications and implications

  • A1: Why teachers must know AI?
  • A2: The AI empowered Teacher
  • A3: AI in Assessment
  • A4: Levels of AI implementation

Cluster B:  Mathematical Foundations

  • B1: Linear Algebra : scalars, vectors, matrices and tensors
  • B2: Calculus for Machine Learning 
  • B3: Statistics and Probability : Bayesian 
  • B4: Optimisation Techniques

Cluster C: The tools of AI/ML

  • C1: Machine Learning : the main algorithms
  • C2: Artificial Neural Networks and Deep Learning
  • C3: Reinforcement Learning 
  • C4: Ensemble learning 

Cluster D: Python for Machine Learnintg 

  • D1: Basics of Python syntax
  • D2: Why Python for Machine Learning ?
  • D3: numpy, scipy and scikit-learn
  • D4: Python for KNN classification 

Cluster E: Chatbots in Education

  • E1: Possible usage of chatbots in education
  • E2: Chatbot as a teaching agent
  • E3: Creating your own chatbot 
  • E4: Deploying your chatbot

Cluster F: Special topics :

  • F1: Reinforcement Learning 
  • F2: Convolutional Neural Networks
  • F3: Ensemble Learning
  • F4: Generative Adversarial Networks 

Term 2: 

Cluster G: AI empowered assessment

  • G1: Limitations of traditional assessment
  • G2: Features of AI empowered assessment
  • G3: Readymade AI assessment products
  • G4: AI assessment and ethics

Cluster H: AI empowered content personalisation 

  • H1: Traditional education promotes homogenisation 
  • H2: Features of content personalisation
  • H3: Off the shelf content personalisation products
  • H4: Interpretation and explain ability of Machine Learning

Cluster I: The IBM Watson and other cloud services

  • I1: Watson content curation services
  • I2: Watson natural language processing 
  • I3: Microsoft Cognitive Toolkit
  • I4: Google TensorFlow

Cluster J: Solutions

  • J1: Amazon Sagemaker
  • J2: Amazon DeepLens 
  • J3: Google AIY
  • J4: Raspberry Pi for AI

Cluster K: Unlocking the Commercial Value of Knowledge : 

  • K1: Fundamentals of Solo Entrepreneurship 
  • K2: Habits and dispositions of successful leaders
  • K3: The path to a millionaire educator
  • K4: The path to a billion dollar company 

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

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