by Ryan Cecil
3 April 2026
The Learning Sciences & Emergent Technology Hub (LSET) is a University of Pittsburgh organization devoted to developing and testing emergent technologies in teaching and scholarship. GAINS recently had the opportunity to interview Jennifer Iriti, co-chair of the LSET Hub and the Associate Director for Applied Practice and Partnerships at the Learning Research and Development Center, about the hub’s upcoming event: “Co-Designing Teaching for a Gen AI World, Together”.
The goal of the event is to bring together educators from both the high school and college levels to collaboratively address problems of practice arising from the widespread availability of generative AI tools. Participants will engage in a full day of structured collaboration that launches a community of practice focused on developing and evaluating evidence-informed approaches to teaching and learning in the context of AI.
If interested in learning more about the event itself, please see the event’s registration page or this additional interview with Jennifer Iriti conducted by the University Times. Below, you will find paraphrased responses from a recent GAINS interview with Jennifer Iriti, addressing further questions about the event.
How many people have registered for the event?
We have about 115 people registered.
How will teams be formed during the event?
We have identified five problems of practice that we'll be focusing on, and individuals who are participating will be able to opt in to the problem of practice they're most interested in working on. We will have our LSET Hub leaders and others supporting participants to help figure out where people want to work, which groups they want to work in. We will most likely be trying to keep STEM folks together, humanities folks together, and so forth.
Each of the small groups will have a structure and process to follow, along with tools that will help them engage with one another as a group. There will also be facilitators from the LSET Hub to help groups do their work, as well as resources supporting each group. For example, a group that's working on a problem of practice around students' metacognitive awareness of how generative AI use is shaping their learning will also have a resource packet that includes a short summary of the learning sciences literature about that topic. Then we'll have some use cases and examples such as ideas of ways to try to address this within classroom practice. Groups don't have to adopt one of those examples; they're more to help get neurons firing and get folks thinking creatively about what they could do to address that problem. The groups will then work together and decide whether they all want to try a similar thing or each try something slightly different, but they'll work in their groups to support each other, help each other flesh out their ideas, query the approaches they're developing, and get themselves in the best place to try the intervention in the fall.
Are there any perks to participating?
The idea here is that this shouldn't feel like additional work or like you're taking on a whole other project. It should feel like this is a problem you're facing in your practice, and so it will be helpful to be working in a supported group with other educators to try to solve that challenge. What we're hoping is that it's a natural incentive, because it's something you would probably want to be working on and trying to solve anyway, but instead of doing it alone and in isolation, you'll be working with supportive colleagues, with resources to support your efforts, and a place to process what you're learning along the way.
One of the things that often happens is we're all so busy that when we try to solve something, we build out a solution, put it into place, and then move on to the next thing, whether that be solving the next problem or attending to the next issue, and we don't keep our attention on it or gather data to really see if the change we implemented is actually an improvement. Sometimes we make changes and keep them in place that really aren't doing what we hoped they would do, because we've already moved on. The goal of having a six-month cycle like this is so that we can see it through and actually spend time thoughtfully designing and planning, implementing it, gathering some practical measures data so that we understand what it's actually doing in our practice spaces, and then coming back together to make sense of what we observe and share that with one another. That way, when we find something that works, we can spread that knowledge quickly across a larger set of people rather than just keeping it within our own personal network or department.
Are PhD students able to contribute?
If PhD students have teaching responsibilities, that's a natural fit for sure. PhD students who are interested in teaching and want to be part of thinking intentionally about instructional design in relation to AI could make sense as well. What we are looking for is folks who are going to be positioned to go try things out in their instructional practice. If someone comes and they don't actually have a role in an instructional context where they're teaching, they will be limited in what they can do and how they can contribute to the groups. If others had interest in coming who didn't have an instructional role, we would love for them to reach out to the LSET Hub so that we can help figure out how to plug them in, in a productive way.
Are there any differences in how high school and college are treating generative AI so far?
There are certainly big differences between high school and college on a lot of dimensions. However, it's also true that the students being taught in high school are inevitably the ones that show up at our doors, and so having some sense of connection and awareness across those two contexts is really important for students to experience some level of alignment and continuity. The big idea behind this was to create a space where there could be more of that kind of conversation. When folks form groups, there's no expectation that they're doing exactly the same thing — that's the beauty of this, as it allows individuals in the group to still test something that's context appropriate for them. A high school science teacher and a college biology teacher may be able to implement interventions that have a similar DNA to them but are adapted and designed a bit differently to fit their students developmentally, to fit the context, and to fit the curriculum. The goal is to create spaces where that high school science teacher and the college biology teacher can hear and see what's happening in the other's space.
In terms of responses to generative AI, there isn't one college response and one K–12 response. It is highly variable in both contexts. It's variable by teacher, by district, by instructor, by department, and by institution. One thing we all have in common is that there's just so much variation and heterogeneity in how responses are being built and handled, and that variation is creating a lot of stress on everybody. The hope is to start trying to get our arms around where there's friction and where we feel real problems, and to implement some solutions that let us feel like we're managing those tensions. These solutions aren't necessarily tech solutions; there might be no tech solutions to these problems that have come about because of the wide availability of generative AI. This is also a big experiment. We haven't pulled folks together this way before, and so we're going to be learning a lot from how we structure and organize it so that we can improve over time. What we're hoping to get underway is a rich space for inquiry, testing, and community building.
Is there any way teachers can stay up to date with any information or events being offered by the LSET Hub?
Right now, the LSET Hub has a website that teachers can check for information and updates. We're also working on building out a newsletter, but that newsletter is not live yet. If teachers go to the LSET website for now, soon there will be an opportunity to sign up for a newsletter as well.