The rise of GenAI presents a transformative opportunity for OER. While OER is often promoted for being free for students and instructors, the act of creating or adapting OER is often heavily time and resource intensive. To address these barriers, genAI can be used as a tool to support the rapid creation and adaptation of OER. By lowering the technical and time-related barriers to content creation, genAI has the potential to help to democratize the OER landscape, making high-quality educational resources more accessible to a wider audience. Additionally, genAI tools can allow for quicker updates and responsiveness to new information, making OER more dynamic and relevant—especially in fast-evolving disciplines.
Let’s first explore some of the ways genAI is being used as a tool for OER and then we’ll dive into to some of the ethical issues and tensions in doing so.
Creation
One of the most recognized applications of genAI tools in education is content creation. Educators are increasingly leveraging these tools to streamline the development of a wide variety of open educational resources (OER), including open textbooks, lesson plans, course modules, quiz banks, interactive handouts, instructional images, and multimedia content such as videos and animations. By automating and accelerating the drafting process, GenAI significantly reduces the time and effort traditionally required to produce high-quality, openly licensed learning materials.
Moreover, the integration of genAI with open technologies—such as H5P—enhances the creation of interactive and engaging OER. These platforms often provide “recipes” or templates that include suggestive prompts and auto-formatting capabilities, helping educators generate complex learning objects more efficiently. Some systems even offer direct integration with GenAI tools, enabling real-time content generation within the authoring environment.
In addition to being a powerful tool for creation, genAI can serve as a valuable “sounding board” for educators during the OER development process. It can review drafts for clarity, tone, grammar, accessibility, and inclusiveness, while also identifying potential content gaps or suggesting improvements to better align materials with learning objectives. This multifaceted support enables educators—especially those working in open and collaborative contexts—to iteratively refine their resources and ensure they meet both pedagogical and equity-driven goals.
Adaption
GenAI also excels at transforming text, making it an especially powerful tool for adapting OER to meet the unique needs of instructors, curricula, learning outcomes, and student populations. Its adaptability allows educators to customize existing OER to fit specific contexts—whether aligning with a local curriculum, addressing diverse learner profiles, or supporting culturally responsive pedagogy.
For example, genAI can function as a virtual learning designer, adjusting the complexity, tone, and depth of content to suit learners at different educational levels. An open textbook originally created for undergraduate students can be revised to support Grade 12 learners, ensuring the content remains pedagogically sound while matching their cognitive and developmental stages. This kind of level-specific adaptation empowers educators to maintain the pedagogical integrity of OER while increasing their relevance and usability.
GenAI is also highly effective at restructuring content for clarity and usability. It can reformat dense paragraphs into tables, bullet points, or infographics to improve readability and information retention. Furthermore, it has the potential to improve accessibility by helping to generate alt text for images, ensuring that visual components of OER are meaningful and navigable for learners with visual impairments.
Through these transformation capabilities, GenAI significantly contributes to the creation of diverse, flexible, and inclusive OER. It enables educators not only to repurpose and localize existing resources but also to extend their reach, ensuring all learners—regardless of background, ability, or location—have access to high-quality educational materials.
Scenario – What is the consequence of not using an accessible format?
Let’s consider this scenario: Dr. Chen, a university environmental science instructor, finds an open educational resource on climate change that was originally developed for a general education course. Wanting to tailor it for her upper-level majors, she uses a generative AI tool to increase the scientific depth, integrate current research, and align the material with her course objectives.
As she reviews the AI-assisted adaptation, Dr. Chen pauses to reflect: How does this technology challenge her disciplinary expertise—and where is her own scholarly judgment essential to maintaining the rigor and integrity of the content?
Remixing
Gen AI can also enable educators to combine diverse texts, formats, and perspectives into cohesive, context-specific learning materials. This function supports one of the core principles of OER—adaptability—by making it easier than ever to curate and blend content from multiple sources into a unified resource that is pedagogically aligned and learner-centered.
With GenAI, educators can efficiently integrate OER materials that span different authors, disciplines, and media formats—such as combining excerpts from open textbooks, multimedia elements, datasets, and scholarly articles. The tool can assist in harmonizing language, tone, and structure, ensuring that the resulting resource maintains clarity, coherence, and a logical flow. This remixing process can also be used to restructure existing OER for different instructional formats, such as shifting from traditional lecture-based content to flipped classroom models, project-based learning, or asynchronous online modules. Educators may also use GenAI to adapt and combine content across disciplines—for instance, weaving together history and literature texts to support interdisciplinary inquiry—or to scaffold content for learners with varying levels of prior knowledge. The result is a highly flexible and customizable learning experience that reflects both the goals of the curriculum and the diverse needs of students.
From Text to Dialogue
“What if, in the future, educators didn’t write textbooks at all? What if, instead, we only wrote structured collections of highly crafted prompts” – David Wiley; https://opencontent.org/blog/archives/7238
Traditionally, OER has focused on static formats such as textbooks, slide decks, and handouts. GenAI tools are now reshaping this landscape by enabling dynamic, conversational learning experiences. These tools can simulate the responsiveness of human tutors, allowing students to engage with open content in real time—asking follow-up questions, exploring topics at their own pace, and receiving tailored explanations. This evolution transforms OER from passive to interactive, supporting more active, inquiry-based learning and giving students greater agency in how they explore and internalize complex ideas.
According to David Wiley, genAI tools bring us closer to solving “Bloom’s two sigma problem“—a concept introduced by educational researcher Benjamin Bloom, who found that students who received one-on-one tutoring performed better than 98% of those in traditional classroom settings. Tutoring, Bloom argued, is one of the most effective instructional methods, but until now, it has been difficult and costly to scale. GenAI tools, Wiley suggests, have the potential to make personalized tutoring widely accessible at a reasonable cost. Instead of simply reading about a topic, learners can have meaningful conversations with AI-powered resources that mimic human dialogue. This opens up opportunities for Socratic questioning, adaptive feedback, and scenario-based learning.
With GenAI, educators can design OER not just as content, but as guided learning conversations that adjust to each learner’s pace, curiosity, and context. As a result, OER becomes more inclusive, accessible, and learner-centered—supporting multilingual interaction, voice-based learning, and immersive simulations. GenAI thus has the potential to redefine OER as a flexible, engaging conversation engine that invites exploration and empowers learners to direct their own educational journey.
Should GenAI Be Used for OER?
There are many tensions and ethical considerations around the use of genAI, especially in open education, that must be taken into consideration when using these tools. We’ll explore them in the next section.
