What’s of interest? Expanding OER with GenAI | EDUCAUSE Review
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In some ways, GenAI and open education may appear to share complementary goals. Both can support more equitable access to knowledge, reduce barriers to learning, and enable greater customization of content to meet diverse learner needs. However, the alignment between GenAI and open education is not automatic, and in some cases, the two may be in direct tension. While OER emphasizes transparency, shared ownership, and user agency, many AI systems operate as closed platforms with opaque decision-making processes. Faculty often encounter tools selected by institutions without clear communication or shared governance about how they should be used, why they were selected, or why the companies behind these tools should be trusted.
Because transparency is a key value of the open education community, data ethics is another critical area of concern. GenAI systems frequently draw from user data to deliver “personalized” experiences, but personalization is often determined by algorithms trained on biased or incomplete datasets. These practices raise questions about who controls the system, how student data is used, and whether the resulting outputs truly serve learners or institutional efficiency goals.
The GenAI–OER Framework offers one such approach. This framework builds on the OER Adopt–Adapt–Build model, which gives educators clear entry points for understanding OER by categorizing engagement into three primary approaches
Where is it?: https://er.educause.edu/articles/2026/5/expanding-oer-with-genai
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