New book from UK colleagues: AI for School Teachers

I do love a new book on technologies for education in schools. This short post is not a review but a sharing by me of what I consider an important and timely book that needed to be written to support teacher learning in AI in schools. Thank you to all THREE education scholars ie Rose, Karine and Mutlu for writing it.

So, it is with delight that I give some quick details of the text. I know it’s just what teachers I have worked with in Australian schools are seeking in their STEM teaching and learning, and more broadly, as the requirements for data flood workloads and the expectations of school leaders and education systems.

AI for School Teachers (2022) co-authored by Rose Luckin, Karine George & Mutlu Cukurova looks back and looks forward at AI in schools**. It offers practical ideas, answers important questions and provides a thorough heads up on what to look out for.

The book opens with a foreword from Tabitha Goldstaub, Chair of the UK Government AI Council, she says:

    we don’t need to be experts or data scientists to be substantively engaged with AI, which is a relief as I myself am not and, indeed, most people won’t be. And this is pointedly not a book solely for STEM teachers – the influence of AI will touch teaching and learning tools across the piste, and the implications of its use have bearing across all subjects whether it be English, maths, geography, or philosophy. What is needed is a general understanding of the ingredients that comprise AI, and the ways we can combine them for the realisation of human potential.

From my perusal thus far, it certainly seems to tick all the boxes Tabitha mentions. The term AI scares many people. However, this conversation gives a practical edge and supports what many teachers are increasingly becoming curious about in the field of AI as it education in schools.

The book has an introduction, seven chapters and an index:

Introduction: Understanding the ingredients

1  What is AI and why might AI be useful in education?

2  Educational challenges and AI

3  Data, data everywhere

4  Looking at data differently

5  Applying AI to understand data

6  Learning from AI

7  Ethical questions and what IS next?

Luckin, George & Cukurova (2022) ask teachers to reflect on current challenges in schools and it makes for sobering reading when across the world impacts of COVID-19 continue to be severe. Here they note the pandemic effects on assessment:

    The COVID-19 pandemic casts an existing contemporary challenge for educators, the fairness and accuracy of assessments, particularly examinations-based summative assessments, into even sharper relief. With pupils unable to attend school, how could (and should) educators assess their progress and award grades? Exam assessments are delivered in a physical space, not online, so when that physical space was unavailable, what then? In England and Wales, an algorithm was used to calculate International Baccalaureate results and national A level and GCSE exam results for 2020. The algorithm made decisions about what exam grade to award in each subject and for each student. The key data that the algorithm used to make grading decisions were the predicted grades made by teachers, and data about the historical performance of the school attended by the student. The algorithm-produced results caused a massive uproar (p 17).

Impacts here in Australia have been less about assessment and more about what role should technology play in the delivery of K-12 education. The technology landscape will need to keep evolving to meet this demand. Also the aftermath on student support, wellbeing and engagement and what must be considered to reduce social isolation if modes other than those in face-to-face classrooms are increasingly used.

In a series of EXHIBITS Luckin, George & Cukurova (2022) ask useful questions that assist teachers to identify challenges they might face, for example:

  • Are the children in my class attending regularly?
  • Have the children developed good relationships amongst their peers?
  • Are the children in my class well nourished?
  • Have I planned lessons that meet the expected learning outcomes?
  • Does my marking and feedback effectively engage students such that they act to improve their learning?
  • How do I evaluate parental perceptions of me as a teacher?
  • How do I ensure that soft skills are developed by my students?
  • How do I know that we are meeting the expected standards for teaching and learning?
  • What should I do to close the gender gap in maths?

There is also an EXHIBIT that has helpful questions head teachers might want to ask (p.18) and then a further follow up with 10 questions to help focus which challenge or challenges to target.

Luckin has always championed human intelligence. In Table 2.1 the strengths of artificial and human intelligence are set out for the reader. Who has the power?

What can AI do better than us?

  • Pattern matching
  • Classification of objects into particular groupings
  • Automating and replicating repetitive tasks
  • Processing large amounts of data
  • Storing large amounts of data
  • Collecting and integrating data from multiple locations and sensors, some of which may be monitoring biological systems
  • Reduce complex phenomena to pieces that can be understood by people.

And, what can we do better than AI?

  • Interdisciplinary academic intelligence
  • Meta-knowing intelligence: the ability to recognise and generate good evidence on which decisions can be made about whether or not information is true
  • Social intelligence
  • Meta-cognitive intelligence: the ability to plan, monitor, and regulate our own thinking
  • Meta-subjective intelligence: the ability to recognise and monitor our development of emotional intelligence and that of others with whom we interact
  • Meta-contextual intelligence: the ability to move seamlessly between different locations, people, places, and environments
  • Perceived self-efficacy (p.19)

The chapters on where to look for data, how to look at data differently and apply AI to understanding data are helpful. How can we better use all the data that we have collected and collated then integrate and organize it? (pp 33-67).

The ethics of AI in teaching and learning is something that has always interested me – there are continued reminders throughout the book. Emphasis is given to its essential nature and how it must be central to any development and decision-making concerning its use in education. A core component of the authors’ concerns is their AI Readiness Framework ie EThICAL. The challenge for teachers is to have that front of mind when buying or applying AI. The Institute of Ethical AI in Education (IEAIED) has worked internationally to consult and collate views from multiple educational stakeholders to assist here. Have a look at their website.

There is a lot in this new book … but I will leave it there for now – if you want to buy it – and I highly recommend you do – follow this link.

Much appreciated Rose, Karine and Mutlu.

** Downloadable free summary of the book can be accessed here.