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Mary Richardson.

Since its launch in November 2022, the Open AI chatbot, ChatGPT, has been flexing its artificial intelligence and causing moral and practical panics on university campuses across the world. It is unsurprising that universities are concerned about the ramifications of using Large Language Models (LLMs) to create responses to assessments because this:

1. Challenges reliable identification of academic standards; and
2. Initiates detailed reviews of certain types of assessment and their future applicability.

The ability of a faceless, brainless machine to answer questions, write poetry, compose songs (although musician, Nick Cave disagrees) and create visual art, all in a matter of seconds, presents us with some astonishing food for thought. The future of not just how we write, but what we might wish to say, is looking potentially very different as more free LLMs become available and ‘learn’ how to write for us. As someone who researches educational assessment, and who teaches and assesses students, there are many questions that I’m grappling with in relation to this new landscape in education, but here I’m focusing on some that academics might wish to consider with their students:

1. Does the creation and release of LLMs mean students will be more tempted to let an AI model do their academic work for them?
2. What characterizes cheating when using LLMs in assessment?

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IOE Events.
Although estimates of the impact of automation on the labour market vary widely, it is generally agreed that the ‘fourth industrial revolution’, and especially the advance of AI, is set to transform how we live and work. The question we wanted to address in the next in our debates series was what this means for education – particularly, for how we prepare the next generation of citizens and workers to thrive in a very different context.  Will the addition of a few more classes on coding and machine learning suffice?  To help us in our quest we brought together experts from the fields of education and technology: Rose Luckin, Professor of Learner Centred Design at the UCL Knowledge Lab; Gi Fernando founder and CEO of Freeformers; Professor Mark Bailey, High Master of St Paul’s School; and Baroness Sally Morgan, whose engagement with the education sector ranges across the compulsory and post-compulsory phases. (more…)

‘Slow’ human intelligence must be valued more holistically if we are to really benefit from the power of AI, says Rose Luckin. 
The end of 2017 brought some worrying observations about the progress of Artificial Intelligence with respect to UK Education. It illustrated that many people are far too willing to equate speed and reduced cost with success. We are in danger of missing what really matters in education; in danger of missing the meaning of what education should about. This is dangerous for education and for the progress of learners and educators of all ages.
First came the publication of the first report from the Data Science Behavioural Insights Team (BIT); this stressed the value of speed. Second came the 19^th evidence session of the House of Lords select committee on AI, which focused on AI and education.[1] This revealed the potential for machine learning AI to reduce the cost of delivering the current school curriculum, and at the same time reduce the value of human intelligence.
The BIT report marks the first anniversary of the data science team and is the demonstration of its raison d’être and the value of data science for policy. In the report, (more…)