Regional Innovation Network

Co-designing Teaching for a GenAI World, Together

An initiative of the Learning Sciences + Emergent Technologies (LSET) Hub at the University of Pittsburgh, with regional partners

What is the Regional Innovation Network?

In early 2023, when GenAI tools became widely available, routine teaching and practices were no longer reliable methods for supporting and measuring student learning. This exacerbated three structural gaps in the education ecosystem: K-12 and higher education responding separately even though many students experience them as a pathway, research moves slower than practice and educators have to make pedagogical decisions now, and we tend to connect with national role-alike communities rather than with the local education ecosystem.

The Regional Innovation Network (RIN) brings high school educators, college faculty, technologists, and learning scientists together to co-design, test, and share teaching practices that respond to[LY1.1] challenges exacerbated by generative AI (GenAI). It is organized around shared challenges rather than around sector, discipline, or role. It is also rooted in place: the schools, colleges, and students involved are connected through the same southwestern Pennsylvania educational ecosystem.

The RIN takes a clear stance. It focuses on teaching and how learning sciences can help us solve GenAI challenges, not on every way GenAI is shaping the world. It is neither “for” nor “against” GenAI in teaching and learning but recognizes that we must adapt practices since GenAI is so widely available. And it is co-designed from the start. The RIN leadership brings research evidence, design processes, and organizing capacity, but the solutions come from a diverse group of participants who bring practitioner knowledge, leadership experience, and research expertise across disciplines. The result is a region that has the capacity to continuously adapt to emergent technologies in ways that benefit student learning and do so collectively, as a connected ecosystem.

How does the work happen?

The RIN works in iterative design and testing cycles with a focus on addressing shared pedagogical challenges around GenAI. If a cycle proves valuable and resources allow, the cycle begins again with new problems and wider participation.

Identify problems
Literature review and data collection surface shared problems of practice
Co-design
Diverse, cross-sector teams design approaches grounded in learning sciences
Test and learn
Educators try an approach in real classrooms and gather structured practical data
Share and spread
Educators and researchers together make sense of results, revise approaches, and share across the region

Designs are grounded in five core learning-science principles that help participants draw on what we already know about human learning to navigate the GenAI challenge. Each co-design team started from a brief evidence summary on its problem, so that what gets tested draws on research as well as classroom experience.

Teaching Approach	Need it addresses	What it looks like in practice
Making Learning Visible with AI	Helping students see when GenAI helps or harms their learning	Students explain a topic on their own, rehearse with a peer, then teach an AI “protégé” named Cora, who asks questions and checks understanding. The educator sets 3–5 learning objectives for Cora. A closing reflection covers what students know and the role the AI played.
Socratic AI Tutor	Assessing students’ process of learning	For out-of-class work, an AI tutor draws on educator-provided material and guides students one question at a time without giving answers. Students submit a summary of their reasoning, graded for completion. A design toolkit and examples library support educators.
Oral Defense	Using or not using GenAI in writing to build targeted skills	Students explain their written work aloud and describe their writing process, starting with modeled questions and moving to questions of their own, with reflections before and after. The defense itself involves no AI.
No Forklifts in the Gym	Shared understanding of what assignments are for	Educators and students discuss each assignment’s purpose using the TILT (Transparency in Learning and Teaching) framework. Students reflect: What is this teaching me? What feels valuable? What would I change?

Teaching Approach

Need it addresses

What it looks like in practice

Making Learning Visible with AI

Helping students see when GenAI helps or harms their learning

Students explain a topic on their own, rehearse with a peer, then teach an AI “protégé” named Cora, who asks questions and checks understanding. The educator sets 3–5 learning objectives for Cora. A closing reflection covers what students know and the role the AI played.

Socratic AI Tutor

Assessing students’ process of learning

For out-of-class work, an AI tutor draws on educator-provided material and guides students one question at a time without giving answers. Students submit a summary of their reasoning, graded for completion. A design toolkit and examples library support educators.

Oral Defense

Using or not using GenAI in writing to build targeted skills

Students explain their written work aloud and describe their writing process, starting with modeled questions and moving to questions of their own, with reflections before and after. The defense itself involves no AI.

No Forklifts in the Gym

Shared understanding of what assignments are for

Educators and students discuss each assignment’s purpose using the TILT (Transparency in Learning and Teaching) framework. Students reflect: What is this teaching me? What feels valuable? What would I change?

How you can get involved

Share your contact information to learn more. If you are interested in learning more, share your contact information through the QR code below and we will reach out to find a time to explore your interests and find ways to collaborate.

Help shape the next cycle. The network plans to come together in January 2027 to share what field-testing revealed and to launch a new round of co-design. We will also engage educators at the Pittsburgh Technology Council’s 2027 Three Rivers Educational Technology Conference in naming current problems and generating ideas and invite those who want deeper involvement into more intensive co-design with researchers and college educators.

Join a co-design or testing team. Teams are cross-sector by design, and high school educators, college faculty, learning scientists, and technologists all make valuable contributions.

Bring what you know. Educators, researchers, technologists, and regional organizations can contribute evidence, tools, teaching approaches and connections, and help examine what emerges from classroom testing.

Connect with the RIN. Fill out our contact form. We will follow up to explore how the RIN could connect with your needs and that of your district or institution.

The RIN is led by the LSET Hub, a cross-unit collaboration of the School of Computing and Information, the Broadening Engagement in STEM Center, the Learning Research and Development Center, the University Center for Teaching and Learning, the Department of English, Health Sciences IT, and the Institute for Learning. It was launched with a Pitt Momentum Fund Teaming Award and is expanding in partnership with the Consortium for Public Education, the Pittsburgh Technology Council, and the Allegheny Intermediate Unit.

RIN Core Implementation Team

Jennifer Iriti (LRDC)

Yiwen Lin (LRDC)

Alison Slinskey Legg (BE STEM)

Disan Davis (BE STEM)

Olivia Phillips (BE STEM)

Elizabeth Votruba-Drzal (LRDC)

Margarita Zabelina (Pitt Economics Dept)

Gayle Rogers (Pitt English Dept)

JD Wright (UCTL)

Lisa Votodian (UCTL)

Lindsay Onufer (UCTL)

Maria Hahn (UCTL)

Lindsay Forman (TCFPE)

What is the Regional Innovation Network?

How does the work happen?

How you can get involved

External Collaborators for the RIN

RIN Core Implementation Team

Additional LRDC members who have contributed and/or participated

External advisors