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AI Readiness: Step 5

  • The kind of machine learning AI that has been discussed in all the recommendations for the AI Readiness Diagnostic Findings has so far been supervised machine learning. This is the type of machine learning used when you want to train the AI to find something specific in the data, such as a child’s face, or particular grade of exam script

  • However, we do not always know exactly what we want the AI to find in data, so we need another type of machine learning that can find patterns in the data: unsupervised machine learning

  • This is the tool we use in a situation where we do not know what we are looking for and so we cannot get the algorithm to learn what the target data we want to find looks like

  • With unsupervised machine learning, the algorithm looks for patterns, searching for similarities that might surprise us

  • Data that might be fed into an unsupervised machine learning algorithm could be:

  •    Log data from interactions with an online learning platform such as mouse clicks

  •    Audio from student conversations in breakout rooms in Zoom​

  •    Performance data from tests and exams

  •    Eye-Tracking data from live online teaching

  •    Survey responses

  • Preparing this data is key and deciding what machine learning AI technique to apply to it will depend on the context

  • You may not want to use all the data you have with just one AI technique anyway. You may end up applying some machine learning, and with the remaining data, using some more traditional methods not based in AI

  • Human intelligence will need to be used to help clean – label – the data as well, in removing errors, and feature engineering

  • Feature engineering is where humans help describe patterns so that the AI isn’t scrambling about identifying commonalities with the data that make absolutely no sense

  • Key Takeaway: Unpacking what AI can do with the data that you’ve got will let you make greater sense of both the data you’ve collected, and the challenge itself. It may even reveal something in the data you had no idea was there. But it takes a lot of time to prepare the data, and if it isn’t clean, you can get a lot of nonsense information out the other end. With an increased understanding of your challenge, you will be in a much better position to select the AI tools and products you need to make your life easier in your educational setting or business

 - Professor Rose Luckin, Founder, EDUCATE Ventures Research, July 2022

Step 5: Modules

Approaches to Applied AI, Part 1

Help contextualise the process of applying AI by likening it to the process of cooking. Cooking methods, ingredients, and washing and chopping all map on to the steps needed to prepare data for AI

Click on the video to watch the presentation

Approaches to Applied AI, Part 2

Help contextualise the process of applying AI by likening it to the process of cooking. Cooking methods, ingredients, and washing and chopping all map on to the steps needed to prepare data for AI

Click on the video to watch the presentation

Types of AI that can be Applied, Part 1

A quick introduction to supervised and unsupervised machine learning, and how the data for either should be prepared

Click on the video to watch the presentation

Types of AI that can be Applied, Part 2

A quick introduction to supervised and unsupervised machine learning, and how the data for either should be prepared

Click on the video to watch the presentation

Preparing to Apply the Algorithm

An overview of the principles of collation,

cleaning, organisation, transformation, feature identification and engineering

Click on the video to watch the presentation

Reducing Complexity & Feature Engineering

An approach to understanding the differences

in interactions between teachers and learners, using a technique designed to find patterns in data without knowing exactly what to search for

Click on the video to watch the presentation

Cluster Analysis

A technique to examine patterns within the data relating to the features we have

selected as our best indicators

Click on the video to watch the presentation

Step 5: Further Reading

Below you can find a selection of resources, books, podcasts, webinars, and research papers appropriate to your stage of AI Readiness. Good luck!

Download this step as a PDF here.

AI for School Teachers, Byte-Sized Edition

  • An easy-to-read 10-page byte-sized summary of the book of the same name, written by Professors Rose Luckin, Mutlu Cukurova, and Headteacher Karine George, members of the senior team actively developing and using the AI Readiness Framework from which these recommendations derive

Alan Turing Institute: Three Questions

  • The Turing Lecture mini-series is designed to reflect on the use of AI and data science in a post-lockdown world. Professor Luckin’s lecture centred on the use of AI and tech in education - particularly in a virtual setting due to the pandemic. In addition, she gives her personal perspective on the use of data and tech to decide exam results across the UK

China's Grand Experiment in Education

  • An MIT Technology Review article on the country’s intelligent education revolution

AI Readiness: Step 5 Webinar for Educators/Businesses

  • Two separate webinars introducing Step 5 of the AI Readiness Framework, one targeted toward educationalists, and the other targeted to educational businesses

A Systematic Review on Educational Data Mining

  • Presently, educational institutions compile and store huge volumes of data, such as student enrolment and attendance records, as well as their examination results. Mining such data yields stimulating information that serves its handlers well. Rapid growth in educational data points to the fact that distilling massive amounts of data requires a more sophisticated set of algorithms. This issue led to the emergence of the field of educational data mining (EDM)

AI for School Teachers

  • The complete book on the AI Readiness Framework, specifically for teachers and headteachers in schools. It will help teachers and heads understand enough about AI to build a strategy for how it can be used in their school. Though it is pitched to teachers and contains familiar examples, the approach should still be used by education and training businesses working with technology

AIRC Step 5 Modules
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