Generative AI and Open Scholarship

Generative AI (GenAI) is a type of artificial intelligence (AI) that is able to create new content, such as text, images, music, or entire datasets, based on patterns and information it has learned from existing data. While traditional AI systems are mainly used to analyze existing data and make predictions, GenAI takes this one step further by creating new data similar to the data it accesses. When an AI technology is creating something by itself, this is called “generative AI” or “GenAI”.^[1]

GenAI is rapidly transforming how we conduct research, scholarship, and teaching and learning. While we engage in this work we will need to grapple with what it means to include GenAI as organizations and institutions develop policies, best practices, and limitations to GenAI use. Each part of this text will address the emergence of GenAI and it’s impacts on the area of open access, open research, and open scholarship. As this is an emerging area, much of what will be contained in the parts will be debates, discussions, and the potential benefits and risks of of GenAI in these areas. POSE will not offer training in GenAI use or application in scholarship and education but will provide specific information related to open scholarly practices.  The following outlines how GenAI is covered in each unit:

Open Access

The Open Access chapters will cover issues related to GenAI and publishing, with a particular focus on its impact on open access. The following is covered in the unit:

• Academic publishing and GenAI – Including the opportunities and challenges of GenAI and publishing practices
• Authorship and GenAI – Including discussion on ethics of authorship, originality and GenAI, and the complexities of copyright

Open Research

The Open Research chapters  will cover issues related to GenAI and research, with a particular focus on its impact on open scholarship. The following is covered in the unit:

• GenAI Tools to Create Readable Code – opportunities and challenges in using an LLM as a coding assistant.
• GenAI for data analysis – Including ways to improve the long-term reproducibility of your analysis.

Open Education

The Open Education chapters will cover issues related to GenAI and teaching and learning, with a particular focus on its impact on education. The following is covered in the unit:

• GenAI as a Tool for OER – Including the potential for creation, adaption, and remixing using GenAI tools, the tensions and ethical considerations of using GenAI, and recommendations on using the tools for generative OER
• GenAI and Open Licenses for OER – Including ways to mitigate the GenAI use if openly licensed content for training
• Ethics and Tensions in using GenAI in Open Education

Dig Deeper If you are interested in learning about GenAI and the current landscape for teaching, learning, and scholarship, the following resources will give you a good introduction and may support your learning when completing the POSE Units: UBC Suggested Tools and Training for GenAI AI, Machine Learning, Deep Learning and Generative AI Explained by IBM Technology Glossary of GenAI Terminology Commonly Used GenAI Tools GenAI and Teaching and Learning