Generative AI Annotated Bibliography
Included in our module of AI resources is this repository of articles, videos and podcasts that we've found relevant to the discussion surrounding ChatGPT and other Generative AI tools. Some of these resources might be relevant to you as a teaching professional as you navigate the disruption AI represents, and some of them may be relevant to your students as you decide how to address AI in your classroom. We'll continue to add to this bibliography over time as new issues and trends emerge.
The 4 Stages of AI
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Edward J. Maloney, Inside Higher Ed. (April 04, 2023)
The article discusses the impact of artificial intelligence on higher education. It acknowledges the range of opinions surrounding AI's influence, from concerns about cheating to its potential benefits. The author proposes a framework for approaching AI in education, consisting of four stages: Regulate, Adapt, Integrate, Reimagine. Consider looking at each of your assignments through this lens. Should some be adapted? Maybe integrating AI into others is an ideal approach, and we might want to take a step further in some cases: "We might need to reimagine what teaching and learning mean in this new context. We may find ourselves shifting our epistemological frame from production and creation to something like analysis and critique. Our educational models would need to change as well."
AI Bots Can Seem Sentient. Students Need Guardrails
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Susan D'Agostino, Inside Higher Ed. (Feb. 22, 2023)
This article describes a lot of the current landscape and conversation around these tools. It does a good job of reflecting on the effects of the predictive text generation and how we as policymakers now have to navigate (in most cases reactively) to the fast moving industry. I appreciate the connection to how we have a tendency to anthropomorphize these models (look no further than how loosely we throw around "intelligence" in the acronym AI) since I think that is a big concern in the early stages of our understanding. It can cloud the ability of our students and us as instructors to think critically and be reflective when it comes to the use of these tools in our practice. A great thought provoking article.
Computers just got a lot better at writing
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Vox Media. (Mar. 4, 2020)
This 7-minute YouTube video by Vox does a great job of visually explaining how predictive language models (like ChatGPT) generate coherent sentences. Created a couple of years before GPT-3 and ChatGPT, it effectively predicts both the future potential of predictive models, as well as the dangers of misuse.
On the Dangers of Stochastic Parrots: Can Language Models be Too Big?
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Bender, Gebru, McMillan-Major, & Shmitchell. (Mar. 2021)
This influential work takes a critical look at Large Language Models, focussing on the ethical, environmental, and economic dangers of LLMs. It also coined the term "Stochastic Parrots," to describe LLMs, which generate text without meaning or communicative intent, allowing human readers to imagine intent and meaning.
You Are Not a Parrot
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Elizabeth Weil, New York Magazine. (Mar. 2023)
This profile piece on Emily Bender is an entertaining, accessible companion piece to "Stochastic Parrots."
What is ChatGPT Doing... And Why Does it Work?
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Stephen Wolfram (Feb. 14, 2023)
This is a very deep dive into the technology behind ChatGPT and other Large Language Models, written with abundant scaffolding and images. Not essential reading for everyone, but a valuable resource if you really want to build a full conceptual understanding of how LLMs work.
Using AI to Implement Effective Teaching Strategies in Classrooms: Five Strategies, Including Prompts
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Ethan & Lilach Mollick, SSRN (Mar. 24, 2023)
This paper provides guidance for using AI to quickly and easily implement evidence-based teaching strategies that instructors can integrate into their teaching. The authors discuss five teaching strategies that are traditionally hard to implement due to time and effort constraints, but can be more manageable using AI tools.
Five Days in Class with ChatGPT
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Thomas Rid. (Jan. 22, 2023)
This narrative from a computer science instructor is a great case study of using AI in the classroom. This example is for a programming heavy cybersecurity but we can imagine a similar workflow for other lab based courses.
Why OpenAI's ChatGPT Is Such A Big Deal
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Magdalena Petrova, CNBC. (Feb. 2, 2023)
This 13-minute video gives a broad journalistic overview of ChatGPT's popularity, use cases, limitations, and future implications.
Machines Can Craft Essays. How Should Writing Be Taught Now?
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Susan D'Agostino, Inside Higher Ed. (Oct. 26, 2022)
This Inside Higher Ed article from late 2022 was among the first mainstream academic coverage of generative AI as a disruptor of college-level writing skills. The author explores instructor opinions on the policing/integration AI spectrum, and predicts some of the ongoing unanswered questions that we're all now dealing with.
OpenAI Used Kenyan Workers on Less Than $2 Per Hour to Make ChatGPT Less Toxic
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Billy Perrigo, Time Magazine. (Jan. 18, 2023)
ChatGPT partly functions by relying on a vast library of automated predictive text samples. It also functions because of the painstaking work of low-paid data laborers that have "trained" it with guardrails to avoid racist, misogynistic, terroristic, or otherwise harmful subjects. This article investigates the ethical concerns of using workers in low-wage countries to engage with mentally harmful content. Content warning: This article contains non-explicit descriptions of sexual abuse.
Classroom Policies for AI Generative Tools
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Lance Eaton. (Accessed Jan. 16, 2023)
This Google Document is being used to collect syllabus statements from instructors around the world that are engaging with generative AI tools in their course policies. Sample statements range from extremely permissive to extremely proscriptive, with many nuanced policies falling on the spectrum in-between.
I Tried Using AI. It Scared Me
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Tom Scott. (Feb. 13, 2023)
This 15-minute video by a popular YouTube content creator describes his experience using ChatGPT to write some code for a programming project. He compares the likely coming disruption of generative AI to the early internet's widespread disruption of many industries and social interactions.
The Bing Who Loved Me
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Kevin Roose & Casey Newton, New York Times, Hard Fork. (Feb. 17, 2023)
In this podcast episode, a tech journalist describes his unnerving experience having a conversation with Microsoft's new Bing AI chatbot integration. Roose and Newton talk about why Bing's chatbot tends to generate such strange conversations, and why our tendency to anthropomorphize generative AI will have unforeseen social consequences.
ChatGPT Cheating: What to Do When It Happens
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Alyson Klein, Education Week. (Feb. 21, 2023)
This short article provides a concise, practical, actionable framework for minimizing the potential for AI-enabled cheating in your courses, and for what to do when you suspect cheating has occurred. Of particular note: AI-writing detectors are not particularly accurate, often flag false positives, and cannot be relied on as evidence in an academic dishonesty report.
How to cite ChatGPT
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Timothy McAdoo, APA. (Apr. 7, 2023)
The American Psychological Association has released guidelines for citing the work of Generative AI in APA format.
How do I Cite Generative AI in MLA Style?
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MLA Style Center (Mar. 17, 2023)
The Modern Language Association has also released their Generative AI citation guidelines for MLA.
Science Journals: Editorial Policy
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American Association for the Advancement of Science. (accessed Apr. 11, 2023)
Contrast APA and MLA's stance with the editorial policy of the journal Science (found under "general policies," "image and text integrity") which states that no text, images, or graphics generated with AI tools cannot be published in Science journals without explicit permission from the editors.