Three goals for Australian education and training in the age of AI AI is fundamentally reshaping the capabilities our learners need, the work they will do, and the society they will build. Multiple national bodies have called for urgent action. The Castlereagh Statement is a vision for the coordination and collective courage to deliver transform […]
Australia may have just set the benchmark for AI in education. The Castlereagh Statement is one of the most serious and comprehensive frameworks I have come across. It brings together over eighty… | Dr Will Van Reyk | 18 comments
Australia may have just set the benchmark for AI in education.
The Castlereagh Statement is one of the most serious and comprehensive frameworks I have come across. It brings together over eighty educators, researchers, school leaders, university vice-chancellors, industry figures, and students, following a national summit at the University of Sydney in 2025.
It is worth reading wherever you are in the world.
At its core is a ‘unified national vision’. Ambitious, but necessary if education and AI are to be meaningfully integrated across society.
It also confronts an uncomfortable truth:
‘systems were designed for an era in which access to information and cognitive labour was scarce...education and training systems still largely prioritise information transmission and reward the reproduction of knowledge’.
Six principles underpin the vision:
1. Redefine what it means to be educated.
A commitment to cultivating ‘the enduring dispositions and capabilities that we will always value humans exhibiting’. This includes ‘compassion, curiosity, creativity, collaboration, and courage’ alongside metacognitive skills. At the same time, ‘deep domain expertise and practical experience are necessary’ and learners must also ‘understand the affordances and limitations of AI’.
2. Institutional and individual humility.
Many inherited structures were designed for a different era and ‘would benefit from being transformed, consolidated, or retired, recognising that institutions exist primarily to serve learning and learners’.
3. Reconceptualise learning and assessment.
‘A commitment to reconceptualising the processes of learning and assessment to prioritise deep understanding, human connections and visible skill development over the mere production and evaluation of outputs.’
4. Design an agile, capability-focused curriculum.
‘Integrating all education and training sectors and industry’ and ‘shift the focus from content transmission to development of valued capabilities and dispositions’. It also requires guidance on ‘when, how, and why to use AI at each developmental stage’.
5. Empower teachers and redefine teaching.
No transformation is possible without addressing time, funding, and workload. AI must serve teachers, not sideline them.
6. Place technology in service of pedagogy and trust.
‘We commit to ensuring technology serves education, not the reverse, with developmentally appropriate applications of AI tools matched to learners' age, stage, and needs’.
Alongside this, a three-horizon framework sets out a practical, operational approach to educational reform in the coming months and years.
It is hard to think of many national systems taking this kind of joined-up approach.
Ultimately, what is even more important here is that this starts to tackle the first principles question many are still avoiding:
what is education really for in a world of AI?
(Link in comments, and thanks to Vince Wall for highlighting this.) | 18 comments on LinkedIn
AFT made plenty of folks sad when they decided to jump on the AI bandwagon last year (kind of reminds me of the days they were resolutely on the wrong side of Common Core).
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#ailiteracy #aiethics #aiineducation #environment #climate #sustainability | Jon Ippolito
Thirsty for nonpartisan info on AI’s environmental impact? Watch us debunk misleading claims on both sides and demo a student project that quantifies your AI footprint.
The recording is now live for “How Green Is Your Prompt,” organized last Thursday by FromThePage in conjunction with UMaine’s UMaine Digital Curation program. Ben Brumfield, Greg Nelson, Ethan Morin, and I demo tools that help you compare the environmental impact of your chatbot queries to other tasks. This includes new calculations of the footprints of non-AI workplace actions, like digitizing a photo or storing data in the cloud.
I also challenge seven frequently cited claims that I believe either exaggerate or underestimate AI’s environmental impact, from the water required for an individual prompt to what’s really driving data center construction. (Hint: I don’t think it’s AI.)
One of the most exciting moments of the webinar for me was lifting the veil on a prototype chat interface that reports your energy and water use as you chat with AI. Dubbed CollaborAITE, this system draws from data I’ve compiled for the What Uses More app to insert real-time feedback on the environmental impact of the prompts you type as well as ways you might offset them (measured in units like miles spent on public transport or number of hamburgers not eaten).
CollaborAITE faculty lead Greg Nelson and student contributor Ethan Morin explain their motivation for this work in progress; I hope it will eventually be a tool that anyone can use to understand the impact of their AI use and make informed decisions about how to reduce it.
Links in comments.
#AIliteracy #AIethics #AIinEducation #Environment #Climate #Sustainability
Towards the Permissive and Transparent use of Generative AI in Education - Stoo Sepp
Ever since Generative AI (GenAI) chatbots and content creation tools hit the scene back in 2022, their implications for education have been profound. On the ground, I’ve seen a lot of discussion from teachers and university professors about the nature of assessment and what they might do to either integrate this new technology into their […]