The Friction I Don't Want to Lose

FRICTIONAL AI WORKSHOP

Topics of the Workshop In its third edition, this workshop builds on and expands its exploration of friction-in-design in AI systems, hosting a full-day event that challenges the pursuit of seamless, rapid interactions. In contrast to the conventional narrative that human over-reliance on AI stems

Close to the Metal — Real Life

The value of reintroducing friction into our interactions with computers

Introducing beginners to the mechanics of machine learning – Miriam Posner

Every year, I spend some time introducing students to the mechanics of machine learning with neural nets. I definitely don’t go into great depth; I usually only have one class for this. But I try to unpack at least some of the major concepts, so that ML isn’t quite such a black box.

choosing friction

In 2018, legal scholar Tim Wu wrote in the New York Times that: Today’s cult of convenience fails to acknowledge that difficulty is a constitutive feature of human experience. Convenience is all destination and no journey. This piece well predates the current AI boom, but “all destination and no journey” is a pretty good explanation for why using AI to create art is mainly compelling to people who think about creativity in terms of producing content and generating intellectual property. They just want the thing they can market and sell for money or clout; they don’t care how they got there. I know you’re sick of talking about AI. I am too. This is only a little bit about AI, I promise. Like all my writing about technology, it’s mostly about people.

101 Things I Learned in Engineering School

In this unique primer, an experienced civil engineer and instructor presents the physics and fundamentals that underlie the many fields of engineering. Far from a dry, nuts-and-bolts exposition, however, 101 THINGS I LEARNED® IN ENGINEERING SCHOOL probes real-world examples to show how the engine...

A Season of Learning

Credit: Unsplash / Patty Spahr There’s a concept in computer science called explore vs. exploit. Exploitation means using what you know to get reliable returns; exploration means trying new t…

We should be more tired than the model

Adding deliberate friction back into development

A quote from Andrew Quinn

One could say in the first quarter-century of my life, that while I was always fascinated by programming, I could never overcome the guilt of not really knowing whether the …

Social learning preserves both useful and useless theories by canalizing learners’ exploration

In many domains, learning from others is crucial for leveraging cumulative cultural knowledge, which encapsulates the efforts of successive generations of innovators. However, anecdotal and experimental evidence suggests that reliance on social information can reduce the exploration of the problem space. Here, we experimentally investigate the extent to which cultural transmission fosters the persistence of arbitrary solutions in a context where participants are incentivized to improve a physical system across multiple trials. Participants were exposed to various theories about the system, ranging from accurate to misleading. Our findings indicate that even under conditions conducive to exploration, the transmission of cultural knowledge canalizes learners’ focus, limiting their consideration of alternative solutions. This effect was observed in both the theories produced and the solutions attempted by participants, irrespective of the accuracy of the provided theories. These results challenge the notion that arbitrary solutions persist only when they are efficient or intuitive and underscore the significant role of cultural transmission in shaping human knowledge and technologies.

I Started Programming When I Was 7. I'm 50 Now, and the Thing I Loved Has Changed | James Randall

I still love developing but the shifts that AI have brought are tectonic and are forcing me to re-evaluate my own relationship to building things

Up and Down the Ladder of Abstraction

"In science, if you know what you are doing, you should not be doing it. In engineering, if you do not know what you are doing, you should not be doing it. Of course, you seldom, if ever, see either pure state."

The case for friction in AI-mediated information seeking and learning

Introduction. This paper challenges the assumption that frictionless AI-mediated information seeking represents progress. We argue that AI systems eliminating productive friction, such as uncertainty, exploration, and reflective processes, undermine intellectual virtues essential for critical thinking in an AI-saturated information environment. Method. This conceptual article employs a theory synthesis approach, drawing on library information science (LIS) theories of information behaviour, experience, and literacy, virtue epistemology, and human–computer interaction (HCI) friction design literature. Analysis. We map intellectual virtues such as curiosity, thoroughness, and intellectual humility onto the Association of College and Research Libraries (ACRL) Framework for Information Literacy for Higher Education, building on and extending previous analyses. We connect dimensions of virtuous search to AI system design principles and align friction types with specific intellectual virtues. Results. We propose three design principles for human-centred AI systems (representation, affordance, and facilitation) and develop a typology that maps friction interventions to intellectual virtues, providing concrete examples for each. Conclusion. Productive friction should be understood as a feature supporting intellectual development, not a barrier to efficiency. The design choices made today will determine whether AI serves as intellectual scaffolding or cognitive crutch.

Why Some Students Learn Faster

A hypothesis about teaching and learning

Why Some Students Learn Faster

A hypothesis about teaching and learning