Open Educational Resources (OER)

How is the emergence of generative AI seen as a new frontier in the landscape of openness in education?

The emergence of generative AI is considered a significant turning point because it shifts the focus from the creation and sharing of organic (human-created) content to the potential of synthetic (AI-generated) content within the open education sphere. This new capacity to generate novel content through natural language processing and large language models raises questions about how these synthetically produced resources will align with and impact existing concepts of Open Educational Resources (OER) and Open Educational Practices (OEP).

"Generative OER" refers to open educational resources that are specifically designed to be used to create other OER. This concept differs from traditional OER, which are typically finished educational materials intended for direct use by students or faculty. Generative OER includes:

• Openly Licensed Prompts: Sophisticated prompts designed to elicit specific and high-quality outputs from generative AI models, shared under open licenses to allow for revision, remixing, and reuse.
• Openly Licensed Model Weights: The underlying parameters of generative AI models released under open licenses, enabling users to download, customize (fine-tune), and redistribute the models for specific educational purposes, potentially leading to more pedagogically aligned and context-aware AI.

• Automated Content Generation: AI can automatically create educational materials like textbooks, worksheets, and lessons based on curriculum guidelines or learning objectives, potentially expanding the availability of OER significantly.
• Resource Curation and Recommendation: AI can analyze learner needs and suggest relevant OERs based on their performance, learning style, and interests, making it easier for learners to find beneficial resources.
• Updating and Improving Existing Resources: AI can help keep OER current by generating new content based on recent developments and identifying and addressing gaps or inaccuracies in existing materials.

• Accessibility:  AI tools must meet ADA accessibility guidelines as should any content created.
• Amplified Inherent Bias: Bias is an issue with all learning materials but is worsened with AI due to the content used to train the AI models and the programming of AI's decision making algorithms.
• Avoidance: Ignoring or avoiding generative AI and its impact on education potentially risks leaving students underprepared for workplaces where AI literacy is increasingly expected. Avoidance also limits opportunities to explore how AI can support pedagogical innovation, such as personalized learning and efficiency in administrative tasks. Engaging with AI critically and thoughtfully ensures that faculty guide its responsible integration rather than react to its disruptive effects after the fact.
• Quality and Reliability of AI-Generated Content: Ensuring the accuracy, validity, and robustness of content created by AI is crucial. Quality assurance procedures and mechanisms for review and validation are needed.
• Data Privacy and Security: As AI systems are trained on vast amounts of data, concerns about the privacy and security of user data and the data used to train AI models must be addressed.
• Equitable Access to AI Technologies: Ensuring that all stakeholders, including educators and learners in different socio-economic and geographic contexts, have equitable access to AI tools and the skills to use them is vital to avoid exacerbating the digital divide. Be aware that access to AI tools might change from free to fee based.
• Ownership and Copyright: The non-human nature of AI-generated content raises complex questions about copyright and ownership. Current copyright law in many jurisdictions requires human authorship, leaving AI outputs potentially in the public domain immediately. Determining how permissions will be defined and attributed for such content is a key challenge.
• Ethical Considerations: Concerns about academic integrity, transparency, trust, and the potential for misuse of AI, including the spread of misinformation and difficulties in verifying the authenticity of student work, need careful consideration and the development of ethical guidelines and policies.

• Balance AI generated content with your own voice and expertise.
• Engage in critical evaluation of content.
• Faculty should model ethical AI use for students.
• Always clearly state how AI is used for a project.
• Stay informed and experiment with new tools.