Welcome to the University of Windsor’s guidance on Generative Artificial Intelligence! This site is meant to support the entire campus community - faculty, staff, and students - as we learn about and navigate this emerging technology. As Gen AI is rapidly evolving and improving, this site is a work in progress and will be updated frequently to provide the latest information and advice. We encourage you to check back here regularly.
Background and introduction
Artificial Intelligence (AI) is an umbrella term for a range of technologies that use large amounts of data and machine learning to support human activities. These have mostly been used in the past to analyse data and provide predictions based on that data. Generative Artificial Intelligence (such as ChatGPT, Microsoft Copilot, Claude, DALL-E, etc.) is a recent development in the field of machine learning that differs in its ability to create multimodal content in response to prompts, which can also be in multiple modes (e.g. text, images, videos). Generative AI can generate new content in multiple formats, including text, images, code, audio, video, and music, based on large amounts of data from the internet that these models are trained on. It is rapidly becoming embedded in everyday tools such as phones, computers, office software, social media platforms, and medical applications, such that it is almost unavoidable to engage with the technology at some point. There are many different statistical models and training approaches that underlie the tools we know as generative AI or ‘bots’, each with its own benefits, challenges, and limitations. The risks and benefits of using these tools must be carefully weighed before using them.
While generative AI can be fun and exciting to explore, and as a learning organisation we encourage thoughtful and creative experimentation with emerging technologies, it is important to do so with an understanding of ethical and responsible use. Being a responsible user of generative AI means being aware of the risks and the current limitations of the technology.
There remain many unanswered questions about generative AI and how it will continue to evolve and impact society and academic work. There are concerns that these tools: are impacting environmental sustainability; may perpetuate or amplify societal biases and systemic inequalities; have limited reliability, accuracy, and accountability; and presently operate within an uncertain legal framework. There are also transparency and explainability concerns about the data that is used, and how it leads to outputs. How best to use them, how to go beyond mitigating the current risks to prevent future misuses, and how to ensure that outcomes of generative AI use are positive for our community and society at large are all emerging questions that we must grapple with as a community.