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AI Literacy in the Age of ChatGPT

This guide was created to assist faculty in the understanding and use of AI and ChatGPT.

AI Literacy

AI Literacy

Liberally-educated students need to be more than consumers of AI - Ted Underwood, Professor of Information Sciences and English at the University of Illinois, Urbana-Champaign.

Understanding the technology

Understanding the technology

Elements of AI - University of Helsinki
Free course for learning about AI in general.

ChatGPT is everywhere. Here’s where it came from - Will Douglas Heaven, MIT Technology Review
Useful history.

The Acceleration of Artificial Intelligence - Anna-Sofia Lesiv
An interesting summary of the development of today’s generative AI. It’s useful for understanding the 2017 breakthrough (transformer architecture) that led to so much that’s happening now.

A jargon-free explanation of how AI large language models work - Timothy B. Lee and Sean Trott
Learn about word vectors, transformers, and more.

Generative AI exists because of the transformer - Visual Storytelling Team and Madhumita Murgia, Financial Times
Useful explanation with helpful visuals.

Why does chatGPT make up fake academic papers? - Twitter thread by David Smerdon, Univ. of Queensland
Very good explanation.

Large language models from scratch and Large Language Models: Part 2 (videos) - Graphics in 5 Minutes on YouTube

What is Reinforcement Learning From Human Feedback (RLHF) - Alex McFarland
Useful explanation.

It’s worth reading these policies and FAQs from OpenAI:

Using language models effectively

Using language models effectively

Prompt engineering for ChatGPT - Coursera course by Dr. Jules White, Vanderbilt University

Three ways to leverage ChatGPT and other generative AI in research - Times Higher Education
Ideas for using ChatGPT for (1) to determine a hypothesis or question; (2) research method: follow an accepted research method or invent a new method or algorithm to conduct an investigation that resolves or answers the question; and (3) research output: formulate, evaluate, and document the solution to enable further research.

Teaching resources

Teaching resources

Future of Writing in the Disciplines and Professions
White paper from Carnegie Mellon

Four Singularities for Research: The rise of AI is creating both crisis and opportunity - Ethan Mollick
How the rise of AI is affecting writing, publishing, and research.

AI & Accessibility - Center for Teaching Innovation, Cornell University

Using AI to Help Students Teach in Order to Learn - Inside Higher Ed
"By changing ChatGPT’s system prompt, we can create content misunderstandings that students can correct, write Joel Nishimura and Anna Cunningham."

New BYU computer science study shows four ways students are actually using ChatGPT - Brigham Young University News
A survey finds 4 categories of use.

Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions - Beth Buyserie, Ph.D., & Travis N. Thurston, Ph.D.
Open access ebook on Pressbooks.

Instructors as Innovators: a Future-focused Approach to New AI Learning Opportunities, With Prompts - Ethan Mollick & Lilach Mollick
A paper that explores how instructors can leverage generative AI to create personalized learning experiences for students that transform teaching and learning. 

AI Detection in Education is a Dead End - Leon Furze
How AI detection tools work and why they don't work.

Incorporating Generative AI in Teaching and Learning: Faculty Examples Across Disciplines - Columbia University Center for Teaching and Learning
Faculty across disciplines provide a glimpse into their approaches as they experiment with AI in their classrooms.

Handout: AI and the Future of Teaching and Learning - U.S. Dept of Education, Office of Educational Technology (PDF)
Two-page list of key insights and recommendations from the U.S. Dept. of Education.

Understanding AI Writing Tools and their Uses for Teaching and Learning at UC Berkeley.  Berkeley Center for Teaching and Learning

Critical AI, new interdisciplinary journal (Rutgers and Duke)
Follow the new journal at the link provided, or check out the blog feed site including, “research, reviews, and commentary by interdisciplinary scholars in a wide range of AI-adjacent fields, as well as posts by faculty and students affiliated with the Critical AI @ Rutgers initiative.”

Special issue: Critical AI a Field in Formation, American Literature (Duke) “This special issue provides an overview of the emerging interdisciplinary field of Critical AI, which seeks to demystify artificial intelligence; counter its mythologizing as a marvelous and impenetrable black box; and translate, interpret, and critique its operations, from data collection and model architecture to decision making. Artists and researchers are developing new methods, practices, and concepts for this critical project, which is both historicist and attentive to the institutional, technological, and epistemic transformations still underway.”

Ethical issues

Ethical issues

Copyright issues
Generative AI Meets Copyright - Pamela Samuelson, Professor of Law, UC Berkeley (YouTube)
Recording of a talk she gave, summarizing the outstanding cases. "It will take years for this to be resolved in the courts."

Prof. Matthew Sag Testimony on Copyright and AI (PDF), Testimony before the U.S. Senate Committee on the Judiciary, Subcommittee on Intellectual Property, July 12, 2023.

Letter to the U.S. Copyright Office from the Library Copyright Alliance (The American Library Association and The Association of Research Libraries). Oct, 31, 2023. (download the PDF) Supports the idea that training data for generative AI should be considered fair use.

Bias
AI expert Meredith Broussard: ‘Racism, sexism and ableism are systemic problems’ - The Guardian
Racism, sexism and ableism are systemic problems that are baked into our technological systems because they’re baked into society. It would be great if the fix were more data. But more data won’t fix our technological systems if the underlying problem is society.

Language models might be able to self-correct biases—if you ask them - MIT Technology Review
A study from AI lab Anthropic shows how simple natural-language instructions can steer large language models to produce less toxic content.

Mitigating AI bias with prompt engineering — putting GPT to the test - VentureBeat
Designing ethically-Informed prompts.

A Radical Plan to Make AI Good, Not Evil
OpenAI competitor Anthropic, on using AI to train AI (instead of humans). They call it “constitutional AI.”

Identifying AI-created content

Content Credentials verification tool
Upload an image, find out whether is has metadata from AI image generation tools from OpenAI, Microsoft, and Adobe. Not foolproof because other image generators don't include this metadata.

Is badging a solution? (badging AI vs badging human created content)

Generative AI is forcing people to rethink what it means to be authentic - The Conversation

Postplagiarism

Eaton, S.E. Postplagiarism: transdisciplinary ethics and integrity in the age of artificial intelligence and neurotechnology. Int J Educ Integr 19, 23 (2023). https://doi.org/10.1007/s40979-023-00144-1