Email hl-ai-help-list@nd.edu to ask a team of Hesburgh Libraries professionals your questions about using GenAI tools in your research and scholarly work.
View all campus generative AI policies, approved tool information, and other resources at ai.nd.edu.
This guide covers important considerations for using generative AI tools in your research and scholarly work.
Information provided in this guide is not an endorsement, and we advise you to consult with the appropriate party (instructor, supervisor, campus office, publisher, etc.) for the most relevant and up-to-date generative AI policy information applicable in your context.
Artificial intelligence (AI) refers to the ability of computer software to perform advanced reasoning and problem-solving tasks. Generative AI refers to software tools that can create new content such as text, images, audio, and video.
"Text generators are trained on large amounts of text from books, articles, and websites which is analyzed to find patterns and relationships and create new texts by predicting the word or sentence most likely to follow another in a sequence. Text generators can be used to produce a wide variety of content including essays, memos, brochures, poems, songs, and screenplays.
Examples: ChatGPT | Gemini (formerly know as Bard)"
"Image generators learn by analyzing sets of images with captions or text descriptions. Once they learn which images are associated with which concepts, they can combine them to create new images in a range of styles from photorealistic to abstract.
Examples: Dall-E 2 | Midjourney | Stable Diffusion"
"Music generators analyze music tracks and metadata (artist, album, genre, release date, etc.) to identify patterns and features in particular music genres and generate similar sounding compositions.
Examples: Aviva | Soundful | Boomy"
"Video generators learn by analyzing large sets of annotated video and generating new video in response to a text prompt. Alternatively, users can upload existing videos and edit them using text prompts or by applying canned filters and effects.
Examples: Synthesia | Pictory | Kapwing"
"Code generators use algorithms trained on existing source code—typically produced by open source projects for public use—and generate new code based on those examples. Some tools can also analyze and debug existing code or offer suggestions for improvement.
Examples: CodePal | Tabnine | GitHub Copilot"
Recommended Resource: The ITHAKA S&R Generative AI Product Tracker is a living document that provides an overview of products marketed at or utilized by higher education audiences.
For AI systems to be trustworthy, they often need to be responsive to a multiplicity of criteria that are of value to interested parties. Approaches which enhance AI trustworthiness can reduce negative AI risks. This Framework articulates the following characteristics of trustworthy AI and offers guidance for addressing them. Characteristics of trustworthy AI systems include: valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair with harmful bias managed. Creating trustworthy AI requires balancing each of these characteristics based on the AI system's context of use. While all characteristics are socio-technical system attributes, accountability and transparency also relate to the processes and activities internal to an AI system and its external setting. Neglecting these characteristics can increase the probability and magnitude of negative consequences.