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Some tools for thinking about AI in a time when the companies making them are trying to destroy teaching and learning itself... 1/n 🧵

Olivia Guest · Ολίβια Γκεστ@olivia.science

First off we pluralise L to accentuate there's no 1 approach. And we position ourselves in this interdisciplinary field of critically examining AI & reflecting on harms. We give 5 non-exhaustive angles: conceptual clarity, critical thinking, decoloniality, respecting expertise, slow science. 2/

27/1/26, 6:03 am41 reposts80 likes

Olivia Guest · Ολίβια Γκεστ

@olivia.science

27 Jan

For the academics, especially in the cognitive sciences: bsky.app/profile/oliv... 2/n

Olivia Guest · Ολίβια Γκεστ@olivia.science

"Just because a model correlates with neural and behavioral data, it is not sufficient for us to infer that the model is performing cognition: correlation does not imply cognition." On Logical Inference over Brains, Behaviour, and Artificial Neural Networks. doi.org/10.1007/s421... 3/n

doi.orgOn Logical Inference over Brains, Behaviour, and Artificial Neural Networks - Computational Brain & BehaviorIn the cognitive, computational, and neuro-sciences, practitioners often reason about what computational models represent or learn, as well as what algorithm is instantiated. The putative goal of such...

Olivia Guest · Ολίβια Γκεστ

@olivia.science

27 Jan

The concept of artificial intelligence is itself confusing and open to complete deflation. This bad for science and everyone as it is used to harm discussion and dissent. If you want to get what I mean watch this: www.youtube.com/watch?v=Fxyg... or read this: bsky.app/profile/oliv... 3/n

youtube.comCritical AI Literacy: Empowering people to resist hype and harms in the age of AI -- Symposium 2025YouTube video by Iris van Rooij

Olivia Guest · Ολίβια Γκεστ@olivia.science

I split AI into 3 non-mutually exclusive types (see Table 1 above): displacement (harmful), enhancement (beneficial), and/or replacement (neutral) of human cognitive labour. More later possibly, but see Tables 2 to 4 (attached or here: arxiv.org/pdf/2507.19960) for the worked through examples. 2/n

Olivia Guest · Ολίβια Γκεστ

@olivia.science

27 Jan

If you work with human data, you have to contend with this, and certainly not shirk your ethical responsibility to sadly engage with how harmful these models are. We cannot ignore this because it's not unrelated to the rising fascism. We risk science itself. 4/n bsky.app/profile/oliv...

Olivia Guest · Ολίβια Γκεστ@olivia.science

Without Critical AI Literacy (CAIL) in psychology (doi.org/10.31234/osf...) we risk the following: 1️⃣ misunderstanding statistical models, thinking correlation is causation; 2️⃣ confusing statistical models and cognitive models, undermining theory; 3️⃣ going against stated open science norms. 4/

Olivia Guest · Ολίβια Γκεστ

@olivia.science

27 Jan

If you work with theories in cognition, behaviour, psychology, etc., you also have to contend with this and actually really deeply think about what we're doing. Artificial intelligence companies (also see above paper) will thwart our ability to understand ourselves. 5/n bsky.app/profile/oliv...

Olivia Guest · Ολίβια Γκεστ@olivia.science

Guest, O. (2024). What Makes a Good Theory, and How Do We Make a Theory Good?. Computational Brain & Behavior. doi.org/10.1007/s421... bsky.app/profile/oliv... 9/n

doi.orgWhat Makes a Good Theory, and How Do We Make a Theory Good? - Computational Brain & BehaviorI present an ontology of criteria for evaluating theory to answer the titular question from the perspective of a scientist practitioner. Set inside a formal account of our adjudication over theories, ...