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Artificial Intelligence and Teaching


This guide is intended to be a starting point for faculty on information about artificial intelligence and large language modeling (ChatGPT). In particular, this guide explores AI in higher education, providing definitions, tools, syllabus statements, recommended reading, thoughts and ideas. This field is changing constantly and quickly. We invite you to check back with this guide often as we try to keep you updated and informed.        

Helpful Blogs

Below are blogs that focus on AI and teaching in higher education.

One Useful Thing - Trying to understand the implications of AI for work, education, and life by Prof. Ethan Mollick.

More Useful Things - A companion site to One Useful Thing containing  a library of free AI prompts and other resources mentioned in the newsletter.

Nicole Hennig - Keeping Current with Emerging Technologies for Librarians and Educators

The Year of Teaching Dangerously - Adventures in Course Design with Upgrading, AI, Enhancement and More  by Prof. Cynthia Alby

Glossary of Terms

AGI: Artificial general intelligence has not yet been realized and would be when an AI system can learn, understand, and solve any problem that a human can.

AI: The simulation of human intelligence in machines.

ChatGPT: An AI program developed by OpenAI that generates human-like text responses based on given prompts, utilizing the GPT model.

Generative Pre-Training Transformer (GPT): An autoregressive language prediction model using deep learning to produce human-like text, serving as the basis for ChatGPT.

Large Language Models: Apply deep neural networks to text data and generate output from prompts.

Machine learning: A sub field of AI focused on the problems of designing recursive algorithms capable of learning.

Natural Language Processing: Refers to a branch of artificial intelligence concerned with giving computers the ability to understand text and spoken word in the same way humans can.

Supervised learning: A machine learning technique where the authors of the model tell the machine learning algorithm how to handle the training data in order to generate the desired output.

Training data: The information that is digested by a machine learning algorithm.

Unsupervised learning: A machine learning technique where the machine learning algorithm creates its own labels for variables within the training data.