Observatorio IA - The Learning Science Newsletter

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This week, I attended a round table discussion at the House of Commons with politicians and experts from across the education sector to feed into UK policy on AI in Higher Education. Unsurprisingly, one of the key areas of concern and discussion was the impact of AI on academic integrity: in a world where AI can write an essay, what does AI mean for what we assess and how we assess it? And how do we respond in the short term? In this week’s blog post I’ll summarise the discussion and share what we agreed would be the most likely new model of assessment in HE in the post-AI world.
Recently, I’ve been doing a lot of work helping businesses to explore how to integrate AI to improve the efficiency and effectiveness of their organisations, especially their HR and L&D teams. Fear of AI hallucinations along with concerns for data privacy means that most business leaders in start with the assumption that the power and potential of AI lies in building bespoke LLMs - i.e. building an internal search + retrieve engine based on internal data using a chat interface.
There’s been a lot of discussion in recent months about the risks associated with the rise of generative AI for higher education. Much of the conversation has centred around the threat that tools like ChatGPT - which can generate essays and other text-based assessments in seconds - pose to academic integrity. More recently, others have started to explore more subtle risks of AI in the classroom, including issues and equity and the impact on the teacher-student relationship. Much less work has been done on exploring the negative consequences that might result from not embracing AI in education.
There are a lot of AI-powered “summariser” tools on the market. These tools allow us to paste in unstructured text and have AI identify important sentences, extract key phrases and summarise the main points of the document. My research shows that lots of us are using AI summariser tools to help us to learn more from notes that we take in class, in work, while reading documents, watching videos and listening to podcasts etc. But, while summarising and giving structure to information can help to manage cognitive load and support basic recall, it doesn’t in itself help us to learn
My initial research suggests that just six months after Open AI gave the world access to AI, we are already seeing the emergence of a significant AI-Education divide. If the current trend that continues, there is a very real risk that - rather than democratising education - the rise of AI will widen the digital divide and deepen socio-economic inequality. In this week’s blog post I’ll share some examples of how AI has impacted negatively on education equity and - on a more positive note - suggest some ways to reverse this trend and decrease, rather than increase, the digital and socio-economic divide.

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