Featured Speakers
- Becca Busselle, Associate Director, Digital Fluency Team, WestEd
- Patrick Moyle, Project Director, Digital Fluency Team, WestEd
Host
- Danny Torres, Associate Director of Events and Digital Media, WestEd
Danny Torres:
Hello everyone, and welcome to the 22nd session of our Leading Together series. In these 30 minute learning webinars, WestEd experts are sharing research and evidence-based practices that help bridge opportunity gaps, support positive outcomes for children and adults, and help build thriving communities. Today’s topic, the Friction by Design Framework, Centering Learning in the Age of AI.
Our featured speakers today are Becca Busselle, associate director for our digital fluency team at WestEd, and Patrick Moyle, project director for our digital fluency team. Thank you all very much for joining us. My name is Danny Torres. I’m associate director of events and digital media for WestEd. I’ll be your host.
Now, before we move into the contents of today’s webinar, I’d like to take a brief moment to introduce WestEd. As a nonpartisan research, development, and service agency, WestEd works to promote excellence, improve learning, and increase opportunity for children, youth, and adults. Our staff partner with state, district, and local leaders providing a broad range of tailored services, including research and evaluation, professional learning, technical assistance, and policy guidance. We work to generate knowledge and apply evidence and expertise to improve policies, systems, and practices.
Now I’d like to pass the mic over to Patrick. Patrick, take it away.
Patrick Moyle:
All right, hello everybody, and welcome to this 30 minute session focused on Friction by Design. We’re super excited to share this framework with you that we’ve been developing for quite some time. Before we jump in, I’ll share just a little bit of background about who we are.
So the Digital Fluency Project at WestEd was born in late 2019. So if you go back in your memory, what was happening in late 2019? Everything was changing. One of the biggest disruptions to education across the world was occurring at the time with COVID related school closures. And during this time, we were quickly pivoting to supporting our existing partners in solving some of the problems or some of the challenges that we were all facing during that time related to techniques for remote learning, the different tools that we were all using all of a sudden. And you can imagine that there were lots of tools.
In fact, if we think back then, there was teams that were using Zoom for the first time, teams that were using Microsoft teams or Google Meet, collaborative documents, learning management systems, tons of tools that were immediately being used with very little training. And that’s where we came in. We were supporting our partners in thinking not just how to use those tools, but to use them in ways that were pedagogically sound. Not to use all the bells and whistles, but to use the features of each of these tools that actually supported learning.
And so here we are today with a new disruption. So the last two years has been quite the exploratory time in education with gen AI ending up in classrooms, ending up in students’ hands, teachers’ hands. And so in this moment, we go back to these sort of three truths. And these three truths are very similar to the truths that we had back in during COVID-19.
Number one, AI is shifting the instructional landscape. So things are changing. Number two, AI presents new opportunities. There are a lot of different things that generative AI can do, and so there’s lots of exploration to do around how those might be opportunities for learning. And number three, and this is a big one, AI has introduced new risks to learning.
And so what we’re gonna do is we’re going to share a framework that we’ve been developing that gets at each of these three truths. And so in just a moment, I’ll hand the mic over to Dr. Busselle, who is our associate director and lead developer of the framework. And she’s gonna take us through the framework as well as some of the conceptual and research underpinnings. And then we’ll come back, and I’ll talk a little bit about the practical applications of the framework and how it shows up in some of our work with partners across the United States.
And so with that, I would like to introduce Dr. Busselle and hand the mic over.
Becca Busselle:
Great, thanks Patrick. I am really excited to be here, to talk about this framework with you. And to get us started, I wanna share a quote that I love. This is from an education researcher Dan Schwartz. He’s the dean of the Graduate School of Education at Stanford. And he was being interviewed about artificial intelligence, and he said, “Technology is a game changer for education. It offers the prospect of universal access to high quality learning experiences, and it creates fundamentally new ways of teaching. But there are a lot of ways we teach that aren’t great. And a big fear with AI in particular is that we just get more efficient at teaching badly. This is a moment to pay attention, to do things differently.”
And what I love about this quote is that it reminds me that while the tools may change, how humans learn doesn’t. We have decades of research into good teaching and learning. And so the answers to how to design teaching and learning in the age of AI probably lie in what we already know about good instructional design.
And one of the core challenges at the heart of designing good learning is this tension between efficiency and challenge. And this goes back to some of our biology. We know that humans’ brains development, or the development of the human brain, it continues intensively after birth. And it is shaped by our experiences and our interactions. We have this strong innate drive that pushes us towards exploration, novelty, and play. And these drives are powerful engines for learning, but our brains are also highly efficient. And when a task feels unmotivating, confusing, or overwhelming, we naturally want to conserve effort and we look for easier roots.
The particularly unique challenge that generative AI tools introduce into this space is that they are designed to make things easier. And so what we risk is amplifying the wrong kinds of efficiency. And so before we try to answer the question of how to balance efficiency and challenge, how to address this core tension, I wanna step back and think about productive struggle.
So this takes us to the early 20th century with Lev Vygotsky, who was an early education researcher and theorist, and he posited this idea that we can think of learning as a set of expanding rings. And at the center of these rings, we have tasks that learners can do without assistance. This is either tasks they’ve already mastered or tasks that they innately are able to do.
Outside of that ring, we have tasks that learners can do with assistance. And Vygotsky called this, the Zone of Proximal Development. Outside of that ring are tasks that learners cannot do even with assistance. And it’s in that Zone of Proximal Development that the most developmentally rich learning happens.
Vygotsky was very interested in the role of the teacher in the Zone of Proximal Development. He believed that learning doesn’t happen in isolation, but that it happens through guided interaction with what he called a more knowledgeable other. This is someone who guides, models, questions, and supports the learner in this space where they are most capable of learning and growing.
And one of the unique things about AI tools is they can do all of those things. Generative AI tools can guide, model, question, and support a learner. They can serve as that more knowledgeable other. But they can also imitate the products of learning. They can generate the artifacts that we use to evaluate and discern the bounds of a learner’s Zone of Proximal Development, and they’re very accessible. Everybody has access to these tools.
And so if we’re designing learning tasks where the effort doesn’t feel worth doing, that urge to conserve effort or energy might push them towards tools that can mimic learning by producing, we all know this, very polished products that are outside the student’s Zone of Proximal Development. And so if we go back to that quote from Dan Schwartz, the one that we started with, this is a moment to pay attention. It’s a moment to do things differently, to be intentional about how we design learning in this new age of AI because these tools aren’t going away.
So we can think about generative AI tools as serving as the more knowledgeable other as scaffolding learning, or we can think of them as tools that are capable of producing artifacts outside the Zone of Proximal Learning, which would really be short cutting learning. And the way that we think about it is that the difference is not about the tools, but in the way that we design learning around the tools.
And so this leads us to our framework, which we call Friction by Design. So why do we talk about friction? Well, friction isn’t a new term, and we certainly aren’t the first ones to apply it to thinking about learning and generative AI. But we like this term because most people understand friction is something that makes a task a little or a lot more challenging. And we think of friction as the cognitive and social struggles that impact learning. And we think about generative AI tools which are designed to make things easier. These tools can remove friction in the learning space.
And our goal in thinking about designing good learning is not to preserve all friction. But what we do want to do, is preserve friction that is meaningful. We wanna preserve the kinds of friction that drive learning. We can think of these as productive friction. And we want to reduce or eliminate friction that really is just obstacles that are getting in the way of learning.
And so our framework centers around five lenses for thinking about friction in designing learning. And we differentiate between the productive friction, the kinds of friction that drives learning. And those are the ones that you see a little touch of green in these here and these are elevated. We want to amplify these kinds of friction in learning experiences, and we want to minimize the kinds of friction that are getting in the way, the kinds that we might think of as barriers.
And backing up a slide here, if we think about learning, if we think about our learning goals as a place that we want our learner to get to, what we’re trying to do is create experiences where there’s just enough friction that they are working, that they are challenged, but that it feels possible, that they are capable of engaging with this friction. What we want to avoid or eliminate is introducing or maintaining the kinds of friction that are really just a barrier, that are getting in the way of the learner making progress from where they are to where that learning goal is.
And so I’d like to spend the next little while really digging into these five lenses that we use for thinking about friction in learning. And these are really anchored in decades of research in the learning sciences and education research and adjacent fields that have taught us a lot about both how human brains learn, and also what it means to support learners in this social setting that we call school.
So our first lens, our first productive friction is cognitive ownership. And this lens orients us to who is doing the intellectual heavy lifting. We know that the deepest and most durable learning happens when learners are doing the sense making themselves, when they are retrieving information and making connections themselves, and when they are being asked to justify and revise their reasoning themselves. This is friction that we wanna make sure stays with the learner. We don’t wanna offload this kind of effort to any kind of tool, but especially not generative AI tools.
The second lens, our second productive friction is productive struggle. And this orients us to the cognitive effort that is desirable but not overwhelming. This goes back to the heart of Vygotsky’s Zone of Proximal Development. And we know struggle is productive when it involves trying strategies, receiving feedback, revising. We know that iteration deepens conceptual growth. These are tasks that we want to put in front of students so that they are experiencing all of that growth that happens within their Zone of Proximal Development. At the same time, we wanna make sure that we are not asking students to engage in struggles that are outside their Zone of Proximal Development, because that’s when we’re just giving them tasks that are frustrating.
The third lens of thinking about productive friction is social sensemaking. We’re social creatures, and we know that we construct knowledge through interacting with others. We know that discourse in community reveals misconceptions and helps us refine our understanding. We know that engaging in argumentation builds conceptual understanding. And so with when learners are asked to articulate claims, to give reasons, to weigh evidence, and to respond to counter arguments, they deepen their understanding of context or content.
We also know that argumentation builds epistemic agency. That is students begin to see themselves as people who can generate, evaluate, and improve knowledge. They’re not just passive receivers of knowledge. And this is also true when we ask students to critique and discuss ideas. This shifts them from passive learning into interactive learning modes. And these are associated with deeper learning.
And one of the things that I love most about this, this lens for thinking about learning is that being in community with others, being asked to engage in grappling with ideas with others is hard. There’s a reason why we think of it as a friction, but it is one of those things that we risk losing if we think of interacting just with a tool rather than learning as a social and communal effort.
Our third lens, and this is a lens for thinking about unproductive frictions. These are the frictions that we want to try to minimize in learning. So this, this is activation energy. We know that most avoidance with students isn’t about ability. It’s about not knowing how to start, or how to persist with tasks. Even small initiation barriers, can determine whether students engage at all with a task. Clear purpose around tasks and reduced ambiguity increase persistent, especially with challenging tasks, students are more likely to stick with them if they understand the why and the how. And we know that students are more motivated to stick with tasks when they feel competent, when they feel autonomous, and when they feel connected to the learning.
And that leads us to our final lens for unproductive friction, and that is access barriers. Unproductive friction often falls hardest on students who already face barriers to access. These are barriers unrelated to the core learning goals, and they suppress performance, but not a student’s potential. So some of these access barriers include language and formatting, which can obstruct meaning. And we know that this can cause the cognitive load of a task to increase for the learner.
And we know that learning is easier when the context of a task resonates with students’ identities and experiences. It supports both cognitive access and their sense of belonging. If students can’t access tasks, we might as educators interpret that as a lack of understanding when reality, it may just be a design issue with the task itself.
And so the friction by design framework doesn’t argue against using AI. What it argues for is using it well. The goal isn’t to have frictionless learning. The goal is to design learning with friction that’s worth the effort.
Patrick Moyle:
Thanks, Becca. So that is probably a lot to digest. We’re going to shift into some Q&A. So as you pause for a moment and think about the questions that are bubbling up, I’ll start with one of the most common questions that we get when we introduce the Friction by Design Framework. And that is how do we use it?
And at the end of the session, you are gonna get a link to the physical document or the electronic version of the physical document so you can explore how you might bring that into your own context as we’re seeing in the chat a few times. But I wanna share some of the ways that we’ve been using the framework in our own work. And so ultimately the goal is that teachers and educators are using this framework as they think about designing learning experiences for their students.
And so when you read the framework, you’ll notice that it is written with young learners, with students in mind. But we’ve also been using it a lot when we think about adult learning. And so you can imagine over the course of the last year and a half, we’ve been doing a lot of professional learning in different contexts, supporting experienced teachers in experimenting with AI, trying to figure out where it can be useful for them, working with novice teachers in some of the same things, but also making sure that we’re preserving their learning about teaching and learning.
And so one of the ways that we use this framework is when we’re designing learning activities, we’re really being thoughtful about our learning objective and where we need to push that cognitive ownership, where we need to create friction in the learning activities so that our participants are really grappling with their own thinking, how to design for social sense making, really thinking about each of those lenses in an adult learning context.
And then when we’re in those sessions and we’ve engaged teachers or educators in grappling with AI tools, we will pause, we’ll talk about the framework, and we’ll ask them to be metacognitive about how that friction showed up in the way that we designed their learning experience. And so in this way, a teacher, whether they are experienced or novice or anywhere in between, is really grappling with thinking about their own learning and then making connections to how they might replicate those sorts of same kind of design choices, design choice thinking in their own design for their own students.
And so that’s one of the ways that we are using the Friction by Design Framework. Another way that we use the Friction by Design Framework is not as a follow-up, a metacognitive follow-up, but as a planning tool for adults, thinking about their own learning in advance. So for example, we might tee up a task for a set of educators and ask them to think about what aspects of that task they need to remain hard, that they need to grapple with and work with, and what aspects of that task they don’t. And they actually could use the AI to make things a bit easier or if there’s any barriers, how they could use the AI to minimize those barriers.
And so in this way, teachers are doing sort of the opposite of the first model where they’re thinking about friction by design in advance. And so those are two ways that we’ve been talking about using Friction by Design with educators. We imagine there’s going to be many other ways that evolve over time as we get this framework out into the world as more people are grappling with it. And we love to hear the different ways that people are using it.
And so I think with that, we’re just a tiny bit early. I don’t see any questions in the Q&A box, so I’ll give it just a minute more for people to drop a question in and it looks, oh, it looks like we got a question in the chat. Becca, do you want to take that question? We’ll just give it a moment here. Yeah, I’ll go back to the different lenses.
Becca Busselle:
Yeah, so the question is how might this framework be adapted for a neurodiverse audience thinking beyond access barriers? I’m thinking about how much this framing in practice can support my ADHD/ASD1 teenager and how friction manifests differently sometimes, particularly in level of intensity. That’s a really great question.
And I would say that this is a question that I would want to put in the hands of teachers who are working specifically with these populations. And I don’t wanna claim an expertise that I don’t have here, but the idea with the framework is whoever your population is, as an educator, you should be thinking about where the friction needs to lie. And I would say if I was a teacher unable to identify the friction for that particular population in my classroom, I would wanna talk to an expert. I would wanna talk to someone who understands special education and can help me figure out what’s the friction that’s getting in the way of this student with their particular brain that they are bringing to the classroom, and what are the frictions that they need in order to meet this, this learning goal? And I would say this is work that we very much wanna continue to build on is working with experts, particularly in special education and working with neurodiverse learners in thinking about how to operationalize the framework for their audiences.
Patrick Moyle:
All right, thanks, Becca. There are a number of questions in the Q&A box. Two of them are about tools, and we try to stay away from conversations about tools and specifics. I’m gonna reach out to each of you that ask those questions sort of independently via email, but in this moment, I’m gonna hand it to Danny to wrap us up so we can hit our target.
Danny Torres:
All right, well, thank you Becca and Patrick for a great session today. And thank you to all our participants for joining us. We really, really appreciate you being here.
I’ve dropped a link into the chat for you all to access the Friction by Design Framework. You can also scan the QR code on the screen, and it’s available online at wested.org/friction-by-design. For those of you who had questions in the chat or the Q&A that we didn’t get to, we’ll follow up with you via email after the webinar.
And please do feel free to reach out to Becca and Patrick via email directly. If you have questions about the work we discussed today, you can reach them both at [email protected]. And to learn more about our Digital Fluency work at WestEd. Feel free to visit us online at digitalfluency.wested.org.
If you’re interested in learning more about how WestEd is helping educators navigate the promise of AI, visit us online at wested.org/ai. There’s a form on the webpage you can use to get in touch with us. Also, be sure to check out our AI at WestEd LinkedIn showcase. To connect with us there, just search for AI at WestEd in the search field on the LinkedIn website, and our page will pop right up.
You can check out recordings of our past leading together webinars online. We’ve covered a range of topics including literacy, assessments, special education, and mathematics. and we’ll continue our focus on math education and generative AI in January, 2026. To access our Leading Together Webinar Series recordings or to register for our upcoming webinars, visit us online at wested.org/leading-together.
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With that, thank you all very, very much. We’ll see you next time.