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AI Readiness: Step 6

  • Once machine learning has been applied to your data, and you’ve identified the patterns and relationships between data sources, you can use human intelligence to relate these patterns to the particulars of your challenge

  • For a simplified example: in an educational or educational business setting, that might mean discovering several different patterns, such as student profiles, through your application of machine learning, and then relating those profiles to answers from a student survey. You might discover:

  • 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

  • How confident each of the different student profiles are when in particular teaching sessions

  • Do some profiles interact or learn more in peer-to-peer sessions?

  • How do the different profiles respond to feedback; is there a correlation between engagement with feedback and increased knowledge retention?

  • Has any additional technology boosted student confidence based on those profiles?

  • Imagine how useful it might be to have all that siloedunconnected data that you had at the beginning of the whole process now working in concert to show you patterns you hadn’t even thought to look for in, for instance, budgeting and spending, or teacher or student confidence, learning behaviours, recruitment, marking, timetabling, or administration

  • Acting on those findings, and being confident at an organisational level that how you are tackling your challenge is supported by the data, is a great and rewarding feeling, and moreover, it can help you organise your institution in a tighter, more methodical manner, and understand the challenge better, so that you are not falling into traps, constructing misconceptions, or making mistakes with similar challenges in your school

  • Key Takeaway: Including human intelligence at all stages of the process of leveraging AI, from articulating your challenge, to cleaning and organising your data, will mean that at the end of the AI Readiness exercise, you’re able to materially address and improve the conditions in which your challenge appears, you’re able to understand it in greater depth, and actually do something to solve it for people

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

Step 6: Modules

What to do with the Findings from AI Applied to Data

Having found patterns in the data from an unsupervised machine learning algorithm, the next step is to ask how these patterns relate to the other data we have access to

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Identifying Relationships

An example of multimodal data added to our existing data

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Classification is a process of categorising a given set of data into classes, it can be performed on both structured or unstructured data

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Identifying Rules

There is no sole AI ‘silver bullet’ that will help solve a challenge or not, a combination of techniques will help yield the ideal result

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Learning from the Data and Searching for Relationships

Hunt for relationships in the combination of data derived from your machine learning algorithm and the other data held in the organisation

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Step 6: 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

Radio 4 Explores AI in Education

  • The Learning Revolution is a new three-part BBC Radio 4 series on the future of education, created by education expert Alex Beard. The second episode looks at how teaching itself iis becoming revolutionised by technology. As part of this, Century Tech’s Founder Priya Lakhani took Alex to Shireland Collegiate Academy in Birmingham to see how their AI is improving the way students learn

AI Readiness: Step 6 Webinar for Educators/Businesses

  • Two separate webinars introducing Step 6 of the AI Readiness Framework, one targeted toward educationalists, and the other targeted to educational businesses

A Systematic Review on Educational Data Mining

  • Despite a surge of empirical work on student participation in online learning environments, the causal links between the learning-related factors and processes with the desired learning outcomes remain unexplored. This study presents a systematic literature review of approaches to model learning in Massive Open Online Courses offering an analysis of learning related constructs used in the prediction and measurement of student engagement and learning outcome

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 6
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