As generative AI technologies reshape how colleges and universities approach teaching, learning, and policy, higher education leaders are navigating new questions about how these tools can be used in ways that support students, strengthen pedagogy, and align with the values of open education.
DOERS is exploring the intersection of generative artificial intelligence (Gen AI) and open educational resources (OER) through research, policy guidance, and examples from practice. Together, the resources below offer both a framework for making thoughtful decisions about Gen AI and OER and real-world examples of how educators, institutions, systems, and consortia are putting those ideas into practice.
Policy Priorities for Generative AI and Open Education
Policy Priorities for Generative AI and Open Education: A Report for the DOERS Community offers a practical framework for evaluating the adoption and implementation of generative AI policies through the lens of open education.
Developed through focus groups and workshops with educators, administrators, and librarians across North America, the report identifies four guiding principles:
Rather than proposing a single model policy, the report is designed to help states, systems, and institutions evaluate policy choices, consider how AI policies can complement open education policy, and create environments that can evolve alongside emerging technologies and practices.
DOERS is exploring the intersection of generative artificial intelligence (Gen AI) and open educational resources (OER) through research, policy guidance, and examples from practice. Together, the resources below offer both a framework for making thoughtful decisions about Gen AI and OER and real-world examples of how educators, institutions, systems, and consortia are putting those ideas into practice.
Policy Priorities for Generative AI and Open Education
Policy Priorities for Generative AI and Open Education: A Report for the DOERS Community offers a practical framework for evaluating the adoption and implementation of generative AI policies through the lens of open education.
Developed through focus groups and workshops with educators, administrators, and librarians across North America, the report identifies four guiding principles:
- Center Student Outcomes: Prioritize the immediate learning experiences and needs of students.
- Engage Critically with AI: Understand the capabilities and limitations of current AI tools, not just their potential futures.
- Let Pedagogy Drive Policy: Ensure that teaching and learning goals shape how and when AI tools are used.
- Support Underserved and Marginalized Learners: Design AI policies that promote equity, inclusion, and flexibility for students.
Rather than proposing a single model policy, the report is designed to help states, systems, and institutions evaluate policy choices, consider how AI policies can complement open education policy, and create environments that can evolve alongside emerging technologies and practices.
About the Report
This work was produced by the DOERS Innovation Workgroup, with support from the William and Flora Hewlett Foundation. It is intended to spark cross-sector conversations among systems, institutions, and classrooms about how to responsibly and equitably integrate AI in education.
This work was produced by the DOERS Innovation Workgroup, with support from the William and Flora Hewlett Foundation. It is intended to spark cross-sector conversations among systems, institutions, and classrooms about how to responsibly and equitably integrate AI in education.
Cover image for Policy Priorities for Generative AI and Open Education book
Policy Priorities for Generative AI and Open Education
Edited by Liza Long
Presented by Meredith Jacob and Will Cross on behalf of DOERS, this report focuses on how to approach the evaluation, adoption, and implementation of generative AI policies that are coherent with the DOERS mission. This report is not intended to be a model policy for AI to be adopted by states and institutions, but rather a way for states and institutions to evaluate policy choices with the DOERS principles in mind - to think about how the choices about AI policies can complement open education policy and align with the principles of innovative, student-focused, collaborative work.
Policy Priorities for Generative AI and Open Education: A Report for the DOERS Community © 2025 by Meredith Jacob and Will Cross is licensed under CC BY 4.0
Edited by Liza Long
Presented by Meredith Jacob and Will Cross on behalf of DOERS, this report focuses on how to approach the evaluation, adoption, and implementation of generative AI policies that are coherent with the DOERS mission. This report is not intended to be a model policy for AI to be adopted by states and institutions, but rather a way for states and institutions to evaluate policy choices with the DOERS principles in mind - to think about how the choices about AI policies can complement open education policy and align with the principles of innovative, student-focused, collaborative work.
Policy Priorities for Generative AI and Open Education: A Report for the DOERS Community © 2025 by Meredith Jacob and Will Cross is licensed under CC BY 4.0
AI + OER Case Studies: A DOERS Project
Building on DOERS’ exploration of Gen AI and open education policy, AI + OER Case Studies: A DOERS Project turns to practice.
This case study anthology brings together projects and experiences exploring the intersection of Gen AI and OER across higher education systems, consortia, and institutions. The collection provides real-world examples, emerging practices, and shared approaches that can help others navigate similar questions within their own contexts.
The case studies explore how open education communities are using and examining Gen AI to support student success and pedagogical innovation, while also addressing the ethical and practical tensions these technologies introduce. Contributions span classroom practices, institutional and system-level policies, faculty support and professional development, and cross-institutional collaborations.
Contributors include faculty, instructional designers, librarians, system-level administrators, education consultants, and other higher education leaders. Each chapter offers a different perspective on the design, implementation, or analysis of Gen AI and OER.
Organized around Shared Values, Shared Practice, and Shared Future, the collection illustrates both the possibilities and the unresolved questions emerging as the open education community engages with generative AI.
Building on DOERS’ exploration of Gen AI and open education policy, AI + OER Case Studies: A DOERS Project turns to practice.
This case study anthology brings together projects and experiences exploring the intersection of Gen AI and OER across higher education systems, consortia, and institutions. The collection provides real-world examples, emerging practices, and shared approaches that can help others navigate similar questions within their own contexts.
The case studies explore how open education communities are using and examining Gen AI to support student success and pedagogical innovation, while also addressing the ethical and practical tensions these technologies introduce. Contributions span classroom practices, institutional and system-level policies, faculty support and professional development, and cross-institutional collaborations.
Contributors include faculty, instructional designers, librarians, system-level administrators, education consultants, and other higher education leaders. Each chapter offers a different perspective on the design, implementation, or analysis of Gen AI and OER.
Organized around Shared Values, Shared Practice, and Shared Future, the collection illustrates both the possibilities and the unresolved questions emerging as the open education community engages with generative AI.
Cover image for AI + OER Case Studies: A DOERS Project
AI + OER Case Studies: A DOERS Project
Edited by Kathy Essmiller
This case study anthology describes projects and experiences exploring the intersection of generative artificial intelligence (Gen AI) and open educational resources (OER). The case studies highlight promising practices and emerging policies across higher education systems, consortia, and institutions.
AI+OER Case Studies © 2026 edited by Kathy Essmiller is licensed under CC BY 4.0
Edited by Kathy Essmiller
This case study anthology describes projects and experiences exploring the intersection of generative artificial intelligence (Gen AI) and open educational resources (OER). The case studies highlight promising practices and emerging policies across higher education systems, consortia, and institutions.
AI+OER Case Studies © 2026 edited by Kathy Essmiller is licensed under CC BY 4.0
From Principles to Practice
Taken together, these resources reflect DOERS’ commitment to approaching emerging technologies thoughtfully, critically, and collaboratively. The policy priorities provide a framework for asking the right questions; the case studies demonstrate the many ways those questions are being explored in practice.
As Gen AI continues to evolve, DOERS will continue to support the open education community in sharing what we are learning, examining both opportunities and challenges, and developing approaches grounded in student success, strong pedagogy, and the values of openness.
Taken together, these resources reflect DOERS’ commitment to approaching emerging technologies thoughtfully, critically, and collaboratively. The policy priorities provide a framework for asking the right questions; the case studies demonstrate the many ways those questions are being explored in practice.
As Gen AI continues to evolve, DOERS will continue to support the open education community in sharing what we are learning, examining both opportunities and challenges, and developing approaches grounded in student success, strong pedagogy, and the values of openness.
