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AI Readiness: Step 3

  • In education and training, data is everywhere. You might have data about your teacher’s and learner’s physical learning environment, the virtual learning environment, the curriculum, the pedagogy, the use of resources, and much more besides

  • In addition, the connections that exist between these things are also a form of data, as are the connections between these things and the people who are learning

  • Ask yourself:

    • Can I learn about the sort of data I should collect for my challenge from existing research on the topic?​

    • What relevant data can I currently access?

  • Data can be unimodal or multimodal, quantitative or qualitative, structured or unstructured

  • It is also important to avoid the ‘streetlight effect’ searching for data under an illuminated spot because that’s the only area in which you have visibility, rather than searching in the surrounding shade, which happens to be a much larger area

  • Key Takeaway: There is an understandable reticence about data collection within education: a worry that people who should not be able to access the data that’s been collected will end up seeing it, or that it will be misused. It is therefore extremely important that ethics is at the forefront of our thinking when data collection and collation are being considered

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

Step 3: Modules

What is data??

Use some of the existing data in your organisation to double-check you’ve picked an appropriate challenge with which AI might help

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Sources of Data

When considering sources of data, think about where we can get data from, how easily we can get it, who can get it, and its access points

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Accessing and Using Existing Data

Think about what it would take to access your current datawho can access it, and in what form it is, and how usable that might be

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Introducing Multimodal Data

Multimodal data is data from multiple sources connected in some way through meaning, providing complimentary information

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What Multimodal Data Reveals

Collecting and connecting your data can tell you about what you observe, but it can also tell you about what you can’t observe

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Different Data Types

A good place to start is to think about your existing data - is it structured or unstructured? One is a lot easier to understand and search, but the other is more revealing

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What happens in education is not static, the relationship with an intervention changes over time or place, for instance, and it is unlikely such changes are simply binary

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The Streetlight Effect

A type of observational/cognitive bias, where there’s a propensity for people to look for whatever they’re after in the most convenient places

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Step 3: 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

How Educators Can Help Future Learners Outwit the Robots

  • On Machine Learning and EdTech, Professor Rose Luckin’s keynote speech at the Cambridge Summit of Education asked whether education is ready for AI, and suggests how educators can help future learners outwit the robots

Collecting Data in Schools

  • Collecting data in schools is known to improve teaching and learning — but what sort of information would be needed for solutions involving the use of artificial intelligence?

AI Readiness: Step 3 Webinar for Educators/Businesses

  • Two separate webinars introducing Step 3 of the AI Readiness Framework, one targeted toward educationalists, and the other targeted to educational businesses

What Oak National Academy Usage Tells Us About Education During Covid

  • A SchoolDash blog covering analysis of Oak’s usage data in 2021, providing an example of the kinds of insights possible with the available data

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 3 Modules
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