GenAI in Education: An Overview

AI in Education

Artificial Intelligence (AI) has gathered significant attention since November 2022, when ChatGPT, a Generative Artificial Intelligence (GenAI) tool, was released to the broader public, and many other tools and platforms followed. That led to the rapid expansion and explosion of interest and engagement with AI, and in particular GenAI, including in educational settings. Since then, discussions and debates around the potential use of AI in education have become widespread. Amongst these are the creators, innovators and experimenters who forge ahead without seeking approval, while others await guidance and reassurance, engage in staff development initiatives, or simply wish to sideline AI, boxing it away in hopes that it will dissipate.^[1]

Before the rise of generative AI, various AI systems were already employed in higher education. These systems focused on tasks such as identifying at-risk students, recommending courses, boosting motivation, and predicting student performance. Your institution’s homepage might deploy a chat bot to answer questions, one of the most common uses of AI in higher education. The introduction of generative AI has further expanded these capabilities, offering adaptive and automated assessments, personalized learning experiences, tutoring, and feedback opportunities. In higher education, generative AI is utilized across disciplines for creating content, writing code, facilitating research, addressing accessibility concerns, and restructuring writing processes. The recent development of students using AI-powered chatbots to generate essays, code, and digital art has sparked discussions on academic integrity, the evolving nature of learning, and the role of technology in the classroom.^[2]

AI in Open Education

In the Open Education community, AI-related articles, conference presentations, and resources are increasingly dominating the conversation. At the annual Open Education conferences (perhaps the largest American gathering devoted to all thing Open Education), there were a total of five AI-related sessions at #OpenEd23. By the time #OpenEd24 rolled around, 20 of the 130 presentations—about 15%—offered Generative AI as their primary topic, along with one of the keynote speeches; the question of how AI would affect the future of Open dominated participants’ conversations, whether casual or formal. Prominent organizations and leaders in Open Education offered articles, guides, and bold predictions.^[3]. Consider, for example, the highly debated assertions from David Wiley, generally considered to be one of the founders of the Open Education movement:

The global Open Education community has likewise been abuzz with questions, concerns, and plans for how Generative AI tools might transform pedagogy, decrease workload, increase OER usage, and address major challenges that have plagued Open for years. Correspondingly, OEGlobal 2024 saw approximately 10% of its sessions focused on the intersections of Generative AI and OER. While practical applications were still a focus, the international conversation trended more toward questions of access and ethics: who would have access to Large Language Models (LLMs)? How would digital literacy and student privacy be ensured when Generative AI tools are incorporated into OER? How can practitioners ensure that the foundational principles of Open Educational Practices (OEP) remain a priority in the shift toward AI-enabled OER?

Potential Benefits, Concerns, and Considerations

To some, the emerging challenges the Open Education movement faces today—the need to maintain repositories that offer easily-found, relevant, current, high-quality materials, paired with the continual lack of funding, personnel, technical and pedagogical expertise, and structural consistency—finally have a potential solution on the horizon. For others, the foundational values of the Open Education movement—collaborative knowledge creation and sharing, amplifying voices and contributions that have historically been marginalized, broadening educational access and achievement through the democratization of course materials—seem threatened by handing over the creation of learning materials to machines. Both of these perspectives, and the spectrum of positions between them, are both valid and necessary if the Open Education movement is to navigate an ethical and effective path forward in its relationship with Generative AI. The considerations provided below will inevitably evolve, so as you consider the potential benefits, concerns, and other open questions we’ve noted, keep in mind that this resource was developed in 2025.

Potential Benefits

Chances are high that you, or at least someone you know, has already explored the benefits to using AI in education that are listed below. While by no means exhaustive, for most educators (and especially those committed to the use of OER) these areas currently represent an enormous proportion of the time they spend outside of the classroom to ensure the quality of student learning. It’s no surprise, then, that many educators have enthusiastically embraced the prospect of much more efficient work, especially when the end-product can often be of equal or even better quality than what they are able to produce without AI assistance.

Potential Concerns

Beyond plaigarism and cheating—the most dominant topics of conversation when it comes to AI and education—there are a number of potentially (or currently) problematic effects from the use of Generative AI in teaching and learning. Harm Considerations of Large Language Models (LLM)^[4] organizes these concerns in a way that may help us both become better informed about them and also find ways to mitigate them moving forward.

An accessible version of this interactive is available: Harm Considerations of Large Language Models (LLMs).^[5]

Consideration: Intellectual Property^[6]

One crucial consideration in the intersection of AI and OER is intellectual property. When creating or adapting OER it is important to consider copyright; this is also true when considering content generated by AI. The United States Copyright Office (USCO) has launched a copyright initiative on AI-generated materials. As of January 2025, they’ve issued two reports: Part 1 – Digital Replicas, Part 2 – Copyrightablity, and Copyright Registration Guidance for Works Containing AI-Generated Materials.

At the moment, USCO will not register any works under copyright that are entirely generated by machines due to US copyright law’s requirement of human authorship (see Copyright Registration Guidance for Works Containing AI-Generated Materials). Works including GenAI-generated images, such as a human-authored book containing GenAI-generated images, can still be registered, but the images are not protected under copyright.

Creative Commons Guidance^[7]

In the current context, AI technologies and practices are rapidly evolving. Governments are scrambling to regulate AI, and courts are hearing cases regarding the application of existing law. With this in mind, if you create works using generative AI in your OER, you can still apply a CC license to the work you create. The CC license will apply to the creative work that you contributed as a human being to the final product, even though the portion produced by the generative AI system is not copyrightable.

For works that do not involve a significant degree of human creativity, a CC0 license clarifies the intellectual property status of the work, and ensures the public domain grows and thrives.

Consideration: Professional Development^[11]

As we move towards a world of hybrid human/Al work, understanding how to use generative Al will be a critical digital information literacy skill. To be competitive in the workforce, students will need to know how to use generative Al to produce a variety of work products. This will mean that students will need to know how to craft prompts, evaluate responses, determine appropriate use of AI, and guide the Al into creating the appropriate output.

For Students

Today’s students likely already are interacting with numerous AI-embedded tools daily. AI’s ongoing development for teaching and learning promises to expand these tools to create more personalized, on-demand student success tools to provide:

• Tailored learning materials on customized paths based on their progress and strengths (e.g., Knewton Alta)
• Just-in-time help to explain, clarify, or recommend resources (e.g., IBM Watson or ChatGPT)
• Tutoring and coaching (e.g., Khan Academy’s Khanmigo)
• Improve writing and language skills (e.g., Grammarly and Google Translate)
• Academic Support (e.g., Starfish)
• Virtual reality (VR) and augmented reality (AR) applications, when integrated with AI, can create immersive and interactive active learning experiences (e.g., Meta Quest)

For Education Professionals

AI is rapidly changing course design and instructional processes in higher education, with numerous platforms integrating AI tools to help instructors auto-generate course content. For example, in April 2023, Coursera announced that, with their new AI-assisted course builder, with just “a few simple inputs from a human author, a new set of AI-powered features can auto-generate course content — such as overall course structure, readings, assignments, and glossaries — to help educators dramatically reduce the time and cost of producing high-quality content” (Goli, 2023).

AI is reshaping instructional design in higher education and can be used to create personalized learning experiences for students while optimizing course content, from personalized and adaptive learning content to intelligent tutoring systems, natural language processing, gamification, content creation, assessment, and feedback generation, and learning analytics resource allocation (Gibson, 2023). In a 20-minute webinar, Exploring AI In Instructional Design: 5 Essential Strategies, Lance Eaton (director of faculty development and innovation at College Unbound) offers five tips for using generative AI in instructional design. He provides one especially helpful tip to improve prompts for better outputs with generative AI: “The first question to ask should ALWAYS be to improve the question you want to ask. I usually start with ‘Improve this prompt to maximize the creativity and analytical abilities of a large language model’ and then I insert my prompt. The new prompt it provides is the prompt I use, and I always get better results.”