Instructional Designer, CTLE
Online Learning Librarian, UHD Library
This guide is a comprehensive resource exclusively curated for UHD students to provide them with the knowledge and tools to responsibly explore the field of Generative Artificial Intelligence (GAI). Our guide includes background information, key terms, issues, guidance for academic and professional use and a variety of resources to expand your understanding. Due to ongoing developments in this field of Generative AI, this resource will be updated quarterly. Contact vacatoledof@uhd.edu for questions about this resource.
In recent years, there has been a surge of interest in the fascinating field of generative AI. This interest has been fueled by impressive advancements in technology and the widespread availability of user-friendly tools. Despite misconceptions about its novelty, generative AI has a deep-rooted history marked by significant milestones. However, it was not until 2014, with the introduction of generative adversarial networks, or GANs -- a type of machine learning algorithm -- that generative AI could create convincingly authentic images, videos, and audio of real people.
GenAI, an Artificial Intelligence (AI) technology, autonomously generates content, including texts, software code, images, videos, and music, in response to written prompts. Trained with data from webpages and social media, GenAI employs statistical analysis to identify and replicate common patterns, such as word or pixel distribution.
Artificial Intelligence (AI): A field of study within computer science, focused on the development of computer systems that can accomplish tasks typically associated with human intelligence. These tasks include speech recognition, route mapping, decision making, etc.
Bias: The training data of an AI model can skew the output, leading it to generate inaccurate or offensive material.
Chatbot: A program designed to communicate with humans in a natural manner, sometimes to facilitate providing information or completing tasks.
Chat Generative Pre-trained Transformer (ChatGPT): A chatbot developed by OpenAI. ChatGPT is a transformer type of AI that is designed to mimic conversations using natural language processing, through which users can write prompts to generate text-based responses.
Generative AI: A model of artificial intelligence that can generate new content such as text, images, video, etc., through pattern recognition, by examining large amounts of training data and creating material that contains similar characteristics to identified patterns in the dataset. Examples include ChatGPT, Claude, Midjourney or DALL-E.
Hallucinations: Instances where a generative AI model generates output that contains inaccurate or irrelevant information, especially when it may look correct. For example, when asking ChatGPT (or any text-based generative AI) to generate a list of citations for a topic, the citations it provides may look accurate but the source material associated with the citation may not actually exist when searching for it.
Large Language Model (LLM): An AI model that receives large amounts of training data that establishes the capacity for it to respond to conversational queries. AI such as ChatGPT, Bard, or Claude use LLM.
Natural Language Processing (NLP): The programmed capacity to understand conversations and respond in kind.
Prompt: A structured text-based query that asks a generative AI to generate new content in the form of text, image, video, etc. Prompt Engineering: The process of refining prompts to elicit more desirable results from generative AI.
Training Data: The development of a generative AI model involves the input of specific types of data, often in large amounts. This process is referred to as “training” and it determines the content output of the specific model. For example, if developing an AI that reviews artwork specifically, the AI model will be trained only on data containing artwork.
Adapted from Brown University's Ultimate AI Generative Glossary.
Hallucinations-Due to its tendency to generate content based on patterns, Generative AI systems can sometimes produce false content. It makes sense in the pattern of data but not in reality.
Inaccurate sources-If the data that it's trained on contains inacuracies, the bot will produce inaccuracies. This can lead to more misinformation.
Outdated information-Generative AI does not have access to the most up-to-date information. It might generate information that is obsolete leading to misinformation.
Biased information-Generative AI will have data that is biased, reflecting stereotypes, prejudices, discriminatory views. Generating this type of data causes harm and perpetuates inequality.
Copyright and Intellectual Policy Issues-There is a risk in generating content that is very similar to coopyrighted works without proper authorization. Legal challenges are taking place and will certainly influence copyright laws and institutional policies.
Data Privacy and Securtiy-There is a risk that sensitive and personal information could be exposed.
Enhanced Creativity-GAI can help you automate mundane tasks and free you to engage with different ideas. It can be used as a collaborator stimulating creativity in your process.
Personalized Learning-By generating prompts to learn skills that are specific to your interest and level of understanding can enhance your learning for courses and workplace.
Research Assistance-specific generative AI tools can help reduce the time of the initial search curating articles and data (Elicit, Consensus and Inciteful) and reviewing the content more quickly (TLDRthis and AskYourPDF).
Tutoring Assistance-Generative AI tools can provide personalized and adaptive learning experiences. Many educational technology software and apps already include Generative AI and are widely using in K-12 schools. It may analyze students' strengths and weaknesses, tailor educational content acordingly, and offer real-time feedback. These systems can also generate interactive exercises, quizzes, and simulations to reinforce learning. University students can benefit from this personalized approach, receiving targeted support that caters to their individual needs and learning styles.
Language Learning and Translation-Generative AI helps with language learning and translation. it uses advanced models to create natural-sournding text. Students can use AI-powered platforms to practice speaking, writing, and understanding different languages. Also, AI translation tools offer accurate and quick translations, making it easier to access educational materials in different languages. These toolsnot only improve language skills but also encourage cultural exchange among students from different backgrounds.
Accessibility-Generative AI can offer a variety of support materials and interaction methods tailored for students with disabilities, neurodiversity, multilingual backgrounds, and other challenges they may encounter. Tools with natural language processing and speech recognition can help suppport students with visual and auditory needs. The involvement of individuals with disabilities must be in the forefront of the development of these tools to ensure accessibility and inclusivity.
