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Iris van Rooij 💭

@irisvanrooij.bsky.social

📝 Guest, O., Suarez, M., & van Rooij, I. (2025). Towards Critical Artificial Intelligence Literacies. Zenodo. doi.org/10.5281/zeno... ✨

Thread with my favourite quotes 👇

1/🧵

igure 1: The important dimensions of CAILs across research and education; clockwise from 12 o’clock: Con- ceptual Clarity is the idea that terms should refer. Critical Thinking is deep engagement with the relationships between statements about the world. Decoloniality is the process of de-centring and addressing dominant harmful views and practices. Respecting Expertise is the epistemic compact between professionals and society. Slow Science is a disposition towards preferring psychologically, techno-socially, and epistemically healthy practices. The lines between dimensions represent how they are interwoven both directly and indirectly.

December 7, 2025 at 5:41 PM

Reposted by Computational Cognitive Science

Iris van Rooij 💭

@irisvanrooij.bsky.social

Dimension 2:

CRITICAL THINKING 🤔

“Critical Thinking is deep engagement with the relationships between statements about the world.”

Guest, Suarez, & van Rooij (2025). Towards Critical Artificial Intelligence Literacies. doi.org/10.5281/zeno...

12/🧵

a square with a small square in the middle and the words critical thinking written above it

Alt: a square with a small square in the middle and the words “critical thinking” above it

media.tenor.com

December 7, 2025 at 7:17 PM

Reposted by Computational Cognitive Science

Iris van Rooij 💭

@irisvanrooij.bsky.social

Mark Blokpoel & I have maintain a living open interactive textbook “Theoretical Modeling for Cognitive Science and Psychology.”

Recently, Mark updated Ch 9 & 10 so they have embedded, running and editable, code again.

Check it out! ✨

computationalcognitivescience.github.io/lovelace/

Cover of texbook:

Blokpoel and van Rooij (2021-2025) Theoretical modeling for cognitive science and psychology.

December 27, 2025 at 8:43 PM

Reposted by Computational Cognitive Science

Olivia Guest · Ολίβια Γκεστ

@olivia.science

"Great man theorising requires the (re)orientation of the theory to direct all credit to one person (or a biased subset of a select few) reminiscent of monarchy—far from the pluralistic or meritocratic facade science often hides behind"

doi.org/10.1007/s421...

Great Man Theorising
Most of the work on women scientists fits the “history of great men” mold, with women simply substituted for men. Thus, we have many biographies of great women scientists. These biographical studies of women scientists, for the most part, place the achievements of Marie Curie or Rosalind Franklin within the male world, demonstrating that women have, in fact, made important contributions to what has been defined as mainstream science. Yet the focus remains on the woman as exceptional—the woman who defied convention to claim a prominent position in an essentially male world. One of the problems with this approach to history is that it retains the male norm as the measure of excellence.

Londa Schiebinger, 1987, p. 314

On the one hand, it is common knowledge that scholarly activities are the fruit of the labour of teams of so-called great men’s students, mentees, collaborators, and partners. While on the other hand, the formal archival record gives a dramatically different account attributing such scientific outputs solely to individual men, and more recently individual people generally. To wit, great man theorising is ‘an important obstacle to building theory’ (van Rooij , 2021; also see van Rooij , 2022). Great man theorists fabricate and/or obscure anecdotal and historical evidence to ensure the credit is directed at the target prestigious figure. For example, Pythagoras is overwhelmingly named and credited, but he had many women disciples, who were philosophers and mathematicians in their own right, whose writings survive to this day, and after his death, Theano (a woman, possibly his wife) took over as head of his academy (Schiebinger, 1987; Wider, 1986). To give another example, Lavoisier, the chemist credited with discovering oxygen, was also a woman: Marie Anne Paulze Lavoisier. She could have been credited jointly with her husband, but was not (Eagle & Sloan, 1998).

Great man theorising requires the (re)orientation of the theory to direct all credit to one person (or a biased subset of a select few) reminiscent of monarchy—far from the pluralistic or meritocratic facade science often hides behind—where few are in power and get credit, while many toil unseen (see Birhane and Guest , 2021, for more on how to deal with this). In reality, none of the research would be possible without all the laboratory members and students. Even if (in the unlikely case) the singular so-called great person is indeed generating all the ideas, the mental space they enjoy is only possible by the grace of their students, mentees, and employees.

December 30, 2025 at 8:46 PM

Reposted by Computational Cognitive Science

Vanessa @ Real Scientists DE

@realscientists.de

Ich beende den Thread aber mit etwas Positivem.

In diesem Preprint wird diskutiert, wie KI im Bereich Psychologie unterstützen kann:

📄 van Rooij, I., & Guest, O. (2025). Combining Psychology with Artificial Intelligence: What could possibly go wrong?

🔗 philpapers.org/rec/VANCPW

January 10, 2026 at 11:56 AM

Reposted by Computational Cognitive Science

Iris van Rooij 💭

@irisvanrooij.bsky.social

Relevant reading 1 📖 ✨

Guest, O. Suarez, M., et al. (2025). Against the Uncritical Adoption of 'AI' Technologies in Academia. doi.org/10.5281/zeno...

By @olivia.science @marentierra.bsky.social @altibel.bsky.social @lucyavraamidou.bsky.social @jedbrown.org @felienne.bsky.social, me & others

4/🧵

Figure 1. A cartoon set theoretic view on various terms (see Table 1) used when discussing the superset AI (black outline, hatched background): LLMs are in orange; ANNs are in magenta; generative models are in blue; and finally, chatbots are in green. Where these intersect, the colours reflect that, e.g. generative ad- versarial network (GAN) and Boltzmann machine (BM) models are in the purple subset because they are both generative and ANNs. In the case of proprietary closed source models, e.g. OpenAI’s ChatGPT and Apple’s Siri, we cannot verify their implementation and so academics can only make educated guesses (cf. Dingemanse 2025). Undefined terms used above: BERT (Devlin et al. 2019); AlexNet (Krizhevsky et al. 2017); A.L.I.C.E. (Wallace 2009); ELIZA (Weizenbaum 1966); Jabberwacky (Twist 2003); linear discrim- inant analysis (LDA); quadratic discriminant analysis (QDA).

January 16, 2026 at 8:58 PM

Reposted by Computational Cognitive Science

Andrea Reyes E (she/her)

@altibel.bsky.social

In our position paper we looked at how the core principles of the Netherlands Code of Conduct for #ResearchIntegrity applies to AI usage. @olivia.science @irisvanrooij.bsky.social @jedbrown.org @marentierra.bsky.social @lucyavraamidou.bsky.social @felienne.bsky.social
doi.org/10.5281/zeno...

Against the Uncritical Adoption of 'AI' Technologies in Academia

Under the banner of progress, products have been uncritically adopted or even imposed on users — in past centuries with tobacco and combustion engines, and in the 21st with social media. For these col...

doi.org

January 22, 2026 at 8:56 PM

Reposted by Computational Cognitive Science

Iris van Rooij 💭

@irisvanrooij.bsky.social

☀️ Summer School 📚

“Critical AI Literacies for Resisting and Reclaiming"

Organisers and teachers:
👉 @marentierra.bsky.social
👉 @olivia.science
👉 myself

Deadline for application:
🐦 31 March 2026 (early bird fee)

1/🧵

www.ru.nl/en/education...

Critical AI Literacies for Resisting and Reclaiming | Radboud University

This course is designed to foster critical AI literacies in participants to empower them to develop ways of resisting or reclaiming AI in their own practices and social context.

www.ru.nl

January 16, 2026 at 8:36 PM

Reposted by Computational Cognitive Science

Iris van Rooij 💭

@irisvanrooij.bsky.social

✨ Summer School: Critical AI Literacies for Resisting and Reclaiming 📚

👉 For many more relevant resources for Critical AI literacy, check out this website maintained by @olivia.science with videos, news, opinion pieces, blogs, articles, posters, and more. 👇

olivia.science/ai

6/🧵

Critical AI

On this page are some resources for Critical AI Literacy (CAIL) from my perspective.

olivia.science

January 16, 2026 at 10:13 PM

Reposted by Computational Cognitive Science

Iris van Rooij 💭

@irisvanrooij.bsky.social

Relevant reading 2 📖 ✨

Guest, O., Suarez, M., & van Rooij, I. Zenodo. Towards Critical Artificial Intelligence Literacies. Zenodo. doi.org/10.5281/zeno...

By @olivia.science @marentierra.bsky.social and me.

5/🧵

Figure 1: The important dimensions of CAILs across research and education; clockwise from 12 o’clock: Con- ceptual Clarity is the idea that terms should refer. Critical Thinking is deep engagement with the relationships between statements about the world. Decoloniality is the process of de-centring and addressing dominant harmful views and practices. Respecting Expertise is the epistemic compact between professionals and society. Slow Science is a disposition towards preferring psychologically, techno-socially, and epistemically healthy practices. The lines between dimensions represent how they are interwoven both directly and indirectly.

January 16, 2026 at 9:00 PM

Reposted by Computational Cognitive Science

Angelika Stefan

@ephemeralidea.bsky.social

Interested in discussing how Artificial Intelligence can be helpful or hurtful to academia? 🤖

As a newly minted UKRN (@ukrepro.bsky.social) Local Network Lead, I'm co-organizing a hybrid event about this next Friday (30th January)

Join us at @livunipsyc.bsky.social or online via Teams!

January 22, 2026 at 5:39 PM

Reposted by Computational Cognitive Science

Olivia Guest · Ολίβια Γκεστ

@olivia.science

From the moment we know a technology steals/obfuscates people's labour to perform task X, it matters 0 how effective the people's labour you steal to do X is. They will likely get better at X over time and you will get better at stealing. Nothing of this justifies the theft. bsky.app/profile/oliv...

In this paper, I turn this frame on its head (cf. Andersen et al., 2023; Bannon, 2011;
Bishop, 2021; Nick Dyer-Witheford, 2019; Rogers, 2022; Ryan, 2024; Schmager et al., 2025;
Shneiderman, 2022; Steinhoff, 2021; Walton & Nayak, 2021). Using a unique (re)definition of
AI that releases us from a correlationist1 grip, we then examine three triplets as case studies of
techno-social relationships between cognition and artifacts. To presage the coming analyses,
herein AI is any techno-social relationship that outsources to machines or algorithms some
part, however small, of human cognitive labour (cf. van Rooĳ et al., 2024). I demonstrate
that AI is human-centric, not because it behaves like or is designed to be like humans, but
because it requires a ghost in the machine, often literally an obfuscated human-in-the-loop
to properly function (also see Guest & Martin, 2025) because AI is humans albeit in fetishised
(Braune, 2020; Morris, 2017; Mota & Cosentino Filho, 2024), obfuscated forms (e.g. Erscoi
et al., 2023). That is, AI “is in reality produced by relations among people [even though
it] appears before us in a fantastic form as relations among things” (Pfaffenberger, 1988, p.
250). AI’s “technological veil” hides human cognitive labour (Mota & Cosentino Filho, 2024).
I bring this anthropological, sociotechnical and broadly computational cognitive scientific
angle to understanding AI, that is unlike “the skills and techniques of behavioural research,
previously applied to humans and other animals[, which claim to help us] better understand
the ‘psychology’ of complex black-box ANNs” (Wills, 2025, n.p. cf. Raley and Rhee, 2023). In
fact behavioural probing of such systems using such experimental techniques assumes their
psychological standing to be equal to, or comparable to, biological organisms: it begs the
question (see for relevant analyses: Forbes & Guest, 2025; Guest & Martin, 2023, 2024; Raji
et al., 2022).

There are no Rube Goldberg-like perpetual motion-like human-in-the-loop-free machines.
There never will be. We might crave such a machine, but the only entities we know that are
self-sustaining, autopoietic, is everything but machines. The only person who can decide to
turn a computer off and then on again is exactly that: a person (Polo, 2024). To effectively avoid
correlationism, obfuscation of cognition in AI, we must reject the current mainstream view:
no amount of high scores on benchmarks, or any other correlationary evidence, can ever pile
up high enough to graduate to a causal claim. To de-fetishise AI, we must accept that AI is any
relationship between technology, tools, models, machines and humans where it appears as if
some cognitive labour is offloaded onto such artifacts, and furthermore we must accept that
such a relationship requires methodical teasing apart to obviate the centrality of the human
(Bernardi, 2024; Braune, 2020; Morris, 2017; Mota & Cosentino Filho, 2024; Pfaffenberger,
1988). We cannot rid the machine from its ghost. But we can rid the concept of human from
ghostliness — the human “need not be degraded to a machine by being denied to be a ghost
in a machine.” (Ryle, 1949, p. 301)

January 19, 2026 at 7:04 AM

Reposted by Computational Cognitive Science

Cem Tabakci

@cem-tabakci.bsky.social

I spent the whole day to read 'What Does 'Human-Centred AI' Mean?' titled paper by @olivia.science. I sincerely think that it is the best paper I've ever read. Worth of time and highly recommended!

Olivia Guest · Ολίβια Γκεστ @olivia.science · Jul 29

Boiling here at home in Cyprus but I put the finishing touches a couple of days ago on this preprint: What Does 'Human-Centred AI' Mean? doi.org/10.48550/arX...

Wherein I analyse HCAI & demonstrate through 3 triplets my new tripartite definition of AI (Table 1) that properly centres the human. 1/n

title and abstract from https://arxiv.org/pdf/2507.19960

table 1 from https://arxiv.org/pdf/2507.19960

January 26, 2026 at 7:35 PM

Reposted by Computational Cognitive Science

Olivia Guest · Ολίβια Γκεστ

@olivia.science

Great interview of @lucyavraamidou.bsky.social in Greek Cypriot newspaper titled:

Οι μεγάλες εταιρείες ΑΙ αποτελούν ένα σύγχρονο παράδειγμα τεχνοφασισμού και συγκεντρωτισμού

The big AI companies are a modern example of technofascism and centralisation

www.philenews.com/politismos/p...

Λούση Αβρααμίδου: Οι μεγάλες εταιρείες ΑΙ αποτελούν ένα σύγχρονο παράδειγμα τεχνοφασισμού και συγκεντρωτισμού

Πώς τα μέσα κοινωνικής δικτύωσης «σαπίζουν» τον ανθρώπινο εγκέφαλο; Ποιες είναι οι επιπτώσεις στην ψυχική υγεία; Τι μπορεί να προσφέρει η τεχνητή

www.philenews.com

January 26, 2026 at 9:11 AM

Reposted by Computational Cognitive Science

Olivia Guest · Ολίβια Γκεστ

@olivia.science

Monthly reminder

Olivia Guest · Ολίβια Γκεστ @olivia.science · Oct 10

important on LLMs for academics:

1️⃣ LLMs are usefully seen as lossy content-addressable systems

2️⃣ we can't automatically detect plagiarism

3️⃣ LLMs automate plagiarism & paper mills

4️⃣ we must protect literature from pollution

5️⃣ LLM use is a CoI

6️⃣ prompts do not cause output in authorial sense

January 26, 2026 at 3:25 AM

Reposted by Computational Cognitive Science

Dr. Tanya Samman

@t-s-sci.bsky.social

PhD here answering the call. Resist the slop!!

eternalscientistmusings.wordpress.com/2025/12/03/r...

January 22, 2026 at 12:52 PM

Reposted by Computational Cognitive Science

Iris van Rooij 💭

@irisvanrooij.bsky.social

Do you want to be able to demystify AI and recognize hyped claims? Do you worry about the social and environmental harms of AI? Do you struggle with how to protect and foster your students’ skills in the age of AI? Do you want to develop your own skills in resisting AI and critical AI literacy?

2/🧵

Critical AI Literacies for Resisting and Reclaiming | Radboud University

This course is designed to foster critical AI literacies in participants to empower them to develop ways of resisting or reclaiming AI in their own practices and social context.

www.ru.nl

January 16, 2026 at 8:38 PM

Reposted by Computational Cognitive Science

Iris van Rooij 💭

@irisvanrooij.bsky.social

☀️ 📚

Summer School open for applications! www.ru.nl/en/education...

"This course is designed to foster critical AI literacies in participants to empower them to develop ways of resisting or reclaiming AI in their own practices & social context”

Learning goals 👇

3/🧵

Learning objectives

1 Gain conceptual clarity and deeper understanding of ‘AI’, its various forms and meanings

2 Understand how the science of human cognition can help resist AI hype

3 Critically uncover pseudoscientific claims about AI

4 Analyse critical intersectional theories (racism, sexism, ableism and more) to analyse social harms such as the exploitation of labor behind AI technologies

5 Critically grasp the relationship between AI technologies, sustainability, and environmental devastation

6 Perform a critical AI literacy project to start ways of resisting AI in a specific context

January 16, 2026 at 8:49 PM

Reposted by Computational Cognitive Science

Dr. Michael von Forstner 🇪🇺🇨🇭

@epiguy.bsky.social

Read this article! Truly important considerations!!

Olivia Guest · Ολίβια Γκεστ @olivia.science · Oct 24

I am close to tears; they listened and made an english version apache.be/2025/10/24/b...

@apache.be you rule 🖤

January 18, 2026 at 8:31 PM

Reposted by Computational Cognitive Science

NecroKuma

@necrokuma.bsky.social

When the rejection of AI includes AI researchers, you know that the phrases "adapt or die, Luddite" don't work.

Olivia Guest · Ολίβια Γκεστ @olivia.science · Oct 24

I am close to tears; they listened and made an english version apache.be/2025/10/24/b...

@apache.be you rule 🖤

January 18, 2026 at 3:46 PM

Reposted by Computational Cognitive Science

Olivia Guest · Ολίβια Γκεστ

@olivia.science

excellent stuff, also if you need more or at a uni-level bsky.app/profile/oliv...

Olivia Guest · Ολίβια Γκεστ @olivia.science · Sep 6

Finally! 🤩 Our position piece: Against the Uncritical Adoption of 'AI' Technologies in Academia:
doi.org/10.5281/zeno...

We unpick the tech industry’s marketing, hype, & harm; and we argue for safeguarding higher education, critical
thinking, expertise, academic freedom, & scientific integrity.
1/n

Abstract: Under the banner of progress, products have been uncritically adopted or
even imposed on users — in past centuries with tobacco and combustion engines, and in
the 21st with social media. For these collective blunders, we now regret our involvement or
apathy as scientists, and society struggles to put the genie back in the bottle. Currently, we
are similarly entangled with artificial intelligence (AI) technology. For example, software updates are rolled out seamlessly and non-consensually, Microsoft Office is bundled with chatbots, and we, our students, and our employers have had no say, as it is not
considered a valid position to reject AI technologies in our teaching and research. This
is why in June 2025, we co-authored an Open Letter calling on our employers to reverse
and rethink their stance on uncritically adopting AI technologies. In this position piece,
we expound on why universities must take their role seriously toa) counter the technology
industry’s marketing, hype, and harm; and to b) safeguard higher education, critical
thinking, expertise, academic freedom, and scientific integrity. We include pointers to
relevant work to further inform our colleagues.

Table 1. Below some of the typical terminological disarray is untangled. Importantly, none of these terms
are orthogonal nor do they exclusively pick out the types of products we may wish to critique or proscribe.

Protecting the Ecosystem of Human Knowledge: Five Principles

January 15, 2026 at 7:34 AM

Reposted by Computational Cognitive Science

Olivia Guest · Ολίβια Γκεστ

@olivia.science

Enjoyed writing this short but sweet [altho not necessarily in message] piece for @projectsyndicate.bsky.social w @irisvanrooij.bsky.social:
> While the AI industry claims its models can “think,” “reason,” and “learn,” their supposed achievements rest on marketing hype & stolen intellectual labor

🧵

Project Syndicate @projectsyndicate.bsky.social · Oct 17

Tech companies want us to outsource all cognitive labor to their models. Instead, academics must defend universities by barring toxic, addictive AI technologies from classrooms, argue @olivia.science and @irisvanrooij.bsky.social .
bit.ly/48FNcJj

AI Is Hollowing Out Higher Education

Olivia Guest & Iris van Rooij urge teachers and scholars to reject tools that commodify learning, deskill students, and promote illiteracy.

bit.ly

October 17, 2025 at 3:08 PM

Reposted by Computational Cognitive Science

Project Syndicate

@projectsyndicate.bsky.social

Tech companies want us to outsource all cognitive labor to their models. Instead, academics must defend universities by barring toxic, addictive AI technologies from classrooms, argue @olivia.science and @irisvanrooij.bsky.social .
bit.ly/48FNcJj

AI Is Hollowing Out Higher Education

Olivia Guest & Iris van Rooij urge teachers and scholars to reject tools that commodify learning, deskill students, and promote illiteracy.

bit.ly

October 17, 2025 at 2:38 PM

Reposted by Computational Cognitive Science

flere-imsaho 🇺🇦

@flere-imsaho.circumstances.run

(frankly speaking, any european company deepening its dependency on us tech, be it cloud or confabulation machinery, is acting unwisely.

we need to divest of the technology provided by a country that very obviously wants to be our enemy.)

January 12, 2026 at 10:41 AM

Reposted by Computational Cognitive Science

Daria Dayter

@coocho.bsky.social

Weekend reading!

iPad screen with the article in the foreground, snowy yard with a sleeping malamute in the back

January 10, 2026 at 10:56 AM

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