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Generative artificial intelligence: Understanding AI

About this Guide

Miytôtâkewin. Tawaw. Bienvenue! Welcome to USask's Library Guide on Generative Artificial Intelligence.

Generative AI (GenAI) has taken the world by storm, and in order to use it effectively and responsibly, it’s important to understand what it is, how it works, and the ethical considerations surrounding its use. While there are many questions about the impact it will have on society, there are three essential questions we can ask about its use in an academic setting:

  1. How can AI be used to enhance research, teaching, and learning across various disciplines?
  2. What are the potential challenges and limitations of integrating AI in academic practices?
  3. How do we ensure the ethical and responsible application of AI within the academic community?

This guide explores Generative AI and its academic applications, offering insights on utilizing AI for enhanced research, teaching, and learning. It examines challenges and limitations of AI integration while emphasizing the importance of using AI ethically and responsibly.

What is Artificial Intelligence?
Ask ChatGPT what AI is, and it provides a coherent response:

AI stands for Artificial Intelligence. It refers to the simulation of human intelligence in machines that are programmed to think and learn like humans and mimic their actions. AI involves the development of algorithms and systems that can perform tasks that would typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.

The Importance of AI Literacy

Engaging responsibly with Generative AI requires both human oversight and AI literacy. This balanced approach—combining knowledge of AI capabilities and limitations with ethical supervision and regulation—ensures AI technologies are used responsibly and effectively. It allows us (society) to harness the potential of generative AI, mitigate associated risks, and promote responsible and sustainable integration across various aspects of our personal, academic, and professional lives.

AI literacy focuses on understanding and interacting with artificial intelligence (AI) technologies and can typically be grouped into two main categories:

Basic AI Literacy Critical AI Literacy
Basic AI Literacy emphasizes the practical skills and knowledge needed to interact with AI technologies in an informed and responsible manner. This includes understanding fundamental AI concepts, recognizing the potential benefits and risks associated with AI, and evaluating its impact on society. Furthermore, it involves effectively prompting AI for desired outcomes and assessing the quality and relevance of AI outputs. With these skills, individuals can confidently engage with AI technologies, ensuring their safe, ethical, and effective use.

Critical AI Literacy involves a deeper examination or critical analysis of the ethical, cultural, and societal implications of AI. This includes understanding issues related to bias, privacy, accountability, and the potential impacts of AI on social structures, equity, and individual rights. Through critical AI literacy, individuals can thoughtfully navigate complex AI-related challenges, participate in informed discussions, and contribute to the development and implementation of AI policies and regulations that promote fairness, transparency, and social good.

To learn more, read From Data Literacy to AI Literacy

Understanding Generative AI

ChatGPT is an example of a large language model (LLM) within the field of Generative Artificial Intelligence (GenAI). Unlike predictive or analytic AI models, generative AI models produce or generate content by analyzing patterns and relationships in input data. In a process called machine learning, these models use complex algorithms to recognize patterns in vast amounts of training data (datasets), which allows them to create coherent outputs similar to those produced by humans. These outputs allow for a wide range of applications, from writing articles and composing music to generating unique artwork and even producing realistic human images.

Despite these capabilities, Generative AI does have limitations. It relies heavily on the quality and quantity of the data it is trained on, which may contain inherent biases and inaccuracies. Furthermore, using generative AI without human oversight raises ethical concerns, including the potential for plagiarism, the creation of deepfake content, and the erosion of trust in digital media.

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