The OU Framework for the Learning and Teaching of Critical AI Literacy Skills

I found this a really helpful approach from the Open University. Firstly, identify the elements of critical digital literacy the institution wants staff and students to cultivate:

Then offer staff a practical list of the actions associated with each of the elements, such as those listed for AI ethics in the report:

• Use key criteria to evaluate AI tools (e.g. functionality, accessibility, privacy,
  security, copyright).
• Engage with ethical issues related to AI, such as bias, deepfakes, copyright
  infringement, data security and privacy, with a focus on AI- based creativity.
• Consciously take account of ethical issues by acting in a way that promotes responsible use of AI, e.g. asking for consent before using personal data,
  refraining from spreading misinformation.

I think this is how you integrate programme development and staff development. The criteria can provide ILOs which can be incorporated into programme development (i.e. what does ’embedding critical AI literacy’ into teaching actually mean?) while also providing workable objectives for staff development.

If AI accounts for only 20% of data center use, why is it the justification for nearly every new data center? I did the math and suspect Big Tech is using AI as a cover for unpopular uses like surveillance advertising.

https://blog.still-water.net/what-if-data-centers-arent-about-ai

#DataCenter #AIliteracy #AIethics #Google #Meta #Amazon #Meta

Italy moves on #AIAct implementation: the Council of Ministers gave preliminary approval to two draft decrees implementing Law 132/2025 — governance, policing, liability, AI literacy.

My analysis: the parameters to watch, from Art. 70 and 74(8) on authorities and the DPA's role, to training as the condition for the effectiveness of the whole framework.

🔗 https://www.nicfab.eu/en/posts/italy-ai-decrees/

#AIGovernance #AI #AIAct #AILiteracy #DataProtection #GDPR

🚀 Heute startet die Bewerbungsphase für die #CivicCoding-Projektberatung.

☝️ Wir bieten euch wieder bis zu 45 Stunden 1-zu-1-Beratung in den Themenbereichen #KI, #Daten & #Dateninfrastrukturen, #Strategie & #Organisation sowie #AILiteracy & #Governance an.

🤝 Bewerben können sich zivilgesellschaftliche Organisationen mit KI- und Gemeinwohlbezug. #KMU und #Startups sind willkommen, wenn sie ihr Vorhaben gemeinsam mit einer zivilgesellschaftlichen Organisation oder einer #Kommune umsetzen.
🧵👇

What Happens When The Machine Has Never Heard of You?

https://islandinthenet.com/what-happens-when-the-machine-has-never-heard-of-you/

The ethical reasoning of students about AI is an untapped resource

I thought this was an incredibly thought-provoking framing in WonkHe’s recent report concerning how students are already deliberating about AI in complex and sophisticated way. The incoherence of the sector response to these challenges has forced students to deliberate about them in ways which are often complex and sophisticated:

But many students have gone further than working through confusing policy. They are constructing sophisticated ethical frameworks of their own – often more considered than anything their institution has produced. A computer science student described his governing principle: “I make sure my use of AI doesn’t inhibit my understanding of the topic. For essay submissions, the final text is written by myself – I don’t want to lose the ability to report on my findings.” An engineering student had arrived at a principle of “augmentation, not replacement” – using AI for repetitive tasks while retaining responsibility for core logic and final validation. A graphic design student drew a line that was philosophically precise: “Using AI to do a final work for me, I say no, but to help me make a final work as a tutor or a supporter or a friend, I will say yes.”

These are not students waiting for better policy. They have outrun it. But they are doing this intellectual and moral work largely in isolation – and often in silence. One engineering student said she hadn’t heard others discuss their AI use “as it might look like cheating or like they do not understand the assignment.” The furtiveness is itself a cost. Universities have an untapped resource in students’ own ethical reasoning – and creating structured opportunities for that reasoning to be surfaced and shared would do more than another round of policy revision.

We urgently need to create a forums in which that reasoning can shared and elaborated upon. In fact I think this needs to be at the heart of any adequate institutional strategy. If we don’t account for the complexity of these existing students culture of AI use then policy and practice will be at a remove from the real needs and interests of students.

It means that we shouldn’t conceive of critical AI literacy in terms of a deficit which we need to fill with knowledge. Instead it could be seen as designing occasions through which existing reasoning could be elicited, elaborated and systematised in a way that makes it visible to others. The institutional response has been inadequate and an adequate response needs to be grounded in taking student responses to that prior inadequacy seriously.

Helping students out of AI-spirals

There are two ideas in this pre-print by Favero et al which I find very powerful. They concern how student use of AI might develop over time, suggesting spirals in which students might find themselves trapped in ways that could be immensely costly for them. The first relates to self-efficacy and self-esteem:

Students with low academic self-efficacy or self-esteem are more likely to rely on AI to compensate for what they see as their own shortcomings [5, 12, 16]. This reliance can create a harmful cycle: the more students use AI to avoid academic challenges, the less confident they become in their own abilities. This loss of confidence reduces their willingness to take initiative, which in turn increases dependence on AI and further weakens self-belief [15, 37]. Such students also tend to feel more stress and face unrealistic academic expectations, which pushes them even more toward the use of AI. As a result, their ability to think critically, be creative, and learn independently may decline over time [16].

When students see AI as faster and more capable than themselves, they may begin to undervalue their own efforts and knowledge. One student said, “I will never be better than AI” [5], illustrating how AI can unintentionally lower the students’ motivation and belief in their potential, leading to the Impostor Syndrome [37]. Students who have a better understanding of how AI systems work, including what they can and cannot do, show higher levels of academic self-efficacy [33]. Thus, demystifying AI will contribute to support the students’ trust in their own abilities.

The second relates to ‘AI guilt’ and cognitive dissonance:

Relying on AI for academic work can lead to AI guilt: feelings of shame, anxiety, and moral discomfort tied to the use of AI tools [37]. Students express sentiments like: “I feel like I am not being truthful when I use it,” and describe feeling “lazy” or afraid of being judged by peers and instructors [37]. These emotions affect not only their well-being but also their sense of identity, self-worth and personal agency. Such feelings often lead to cognitive dissonance, i.e., the psychological discomfort that occurs when actions conflict with deeply held beliefs [38]. Cognitive dissonance helps explain the tension students feel when they value originality and personal effort, yet use AI tools that may undermine these ideals. For instance, a student may feel proud of an AI-assisted essay but also guilty that it does not reflect their own independent thinking [37]. This internal conflict can be intense. When students believe that genuine academic work should come from human creativity and effort, using AI challenges their core values. The result is often stress, anxiety, and a weakened sense of authenticity in their learning journey [5].

These suggest to me a deeper way in which we might think about critical AI literacy. It’s not just passing technical knowledge onto students, or building critical evaluative capacity on top of that technical knowledge, it’s helping them regulate their use of AI over time. In other words helping them live and work well with AI, or without it, through an awareness of how that iterative action can prove to be corrosive and even destructive.

Föderierter Datenpool goes @DataWeekLeipzig - Denn: Für die #DigitaleTransformation und einen strategischen Einsatz von Daten und KI für den Sozialstaat braucht es eine enge Zusammenarbeit von #Verwaltung und #Zivilgesellschaft.
Gemeinsam haben heute Fachkräfte aus allen Bereichen der Sozialen Arbeit auf kommunaler Ebene diskutiert, wie KI im Arbeitsalltag gewinnbringend einzusetzen ist.

🪴Was es dafür braucht: Grundverständnis von KI (Stichwort #AILiteracy), #Datenverfügbarkeit und die passenden #Prozesse sind die Voraussetzungen für das Gelingen, genau wie große #Visionen und Ideen für klare #Anwendungsfälle.

💓Danke an Hannes Meinhardt von Social Impact gGmbH für die Einladung und die tolle Workshopkonzenption!

@ethanz @mike your work has directly inspired the animating ideas behind a new project I’m building:

AI Literacy Link Commons (AILC) – an open-web commons for sharing AI literacy links, human-curated, federated, and built as a deliberate alternative to algorithmic closed platforms.

https://github.com/wfryer/ai-literacy-link-commons

#OpenWeb #Fediverse #ReclaimNews #MediaLit #AI #AILiteracy