Every gun that is made, every warship launched, every rocket fired signifies, in the final sense, a theft from those who hunger and are not fed, those who are cold and are not clothed. (Dwight D. Eisenhower)
Each World Cup brings some interesting flights, but Santiago de Compostela to Nashville has to be near the top of the all-time list. https://t.co/vFupxlenyf
yesterday, I was in a big celebration over lunch
on my side of the table, out of 14 people, there were nine nationalities and eight mother tongues
that feels very much like my environment
😀💓
This chart from Anthropic is useful, since Agent Teams and Workflows are both very new and very powerful (and token hungry).
On the other hand, maybe it doesn't matter as a lot of the decisions about which approach to use is from the AI itself & it often uses them in combination
nel XXI secolo, dovremmo preoccuparci di curare il cancro e sviluppare il teletrasporto
invece dobbiamo dire alla gente di non bere latte crudo
che altro? spiegare che saltare da un palazzo non è una bella idea? che non si mette l'ananas sulla pizza?
AI is forcing universities to rethink assessment and in a fundamental way.
In this paper, Moorhouse et al. (2023) look at how the world’s top-ranking universities responded to generative AI tools such as ChatGPT.
The paper shows that university guidelines tend to focus on three major areas:
Academic integrity
Assessment design
Communication with students
First, academic integrity.
We can no longer reduce the conversation to “students are cheating with AI.” That is too simplistic. Of course, misuse is real. But the bigger issue is that AI has blurred the boundaries between assistance, authorship, collaboration, and plagiarism.
If a student uses AI to brainstorm, is that misconduct? If they use it to improve grammar, is that allowed? If they use it to generate an outline, where is the line?
If they submit AI-generated text as their own, that is clearly a problem. But many other uses sit in a grey zone, and that is where schools need clarity.
Second, assessment design.
One important recommendation from the paper is that teachers should test their own assignments with AI tools.
Put the prompt into ChatGPT and see what it produces. Then ask yourself: What exactly am I assessing here?
If AI can complete the whole task with little student thinking, then maybe the problem is not only the tool. Maybe the assessment itself needs redesign.
The authors highlight several useful directions:
Focus more on process.
Break large assignments into stages.
Ask for drafts, notes, reflections, and explanations.
Create tasks that connect to class discussions, lived experiences, local contexts, and real-world problems.
Give students opportunities to critique AI outputs rather than simply avoid AI altogether.
This is an important shift. Assessment should not only produce a final answer. It should make learning visible.
Third, communication with students.
This is probably the most practical takeaway for teachers. Students need clear expectations.
Definitely not “AI is forbidden” statements that no one knows how to apply. They need to know what is allowed, what is limited, what must be disclosed, and what crosses the line.
The paper also makes a powerful point: teachers now need a new kind of competence, what the authors call generative AI assessment literacy.
I like this idea.
Teachers do not only need AI literacy in general. They need to understand how AI changes assessment specifically.
That means knowing how AI affects academic integrity, how to redesign tasks, how to guide students toward responsible use, and how to keep assessment meaningful in a world where AI can generate polished work instantly.
Astro Data Lab goes multilingual 🌎
NSF's @NOIRLabastro now offers 60+ Jupyter training notebooks in Spanish—expanding access to big-data astronomy for a growing global community.
Open-source, community-driven, and ready to explore the Universe. 🔭
https://t.co/8bNz1l8c1V
@emollick I start to be suspicious of almost any picture now
any tweet with too many emoticons raises an eyebrow
not sure if it's good because it forces me to think or it's bad because I feel like I cannot trust anyone
Most people, including really accomplished people, don't have an accurate mental model of how LLMs operate (and why would they?)
You see this in wide beliefs that AI is just copying from known sources, or that it only produces average answers, or that it can't generate new ideas