English Dept AI Updates for 2025

Video Updates

Agentic AI

ChatGPT Pro, tasked with an online discussion board

ChatGPT Pro, tasked with an online quiz

AI for Content Development

Templating content from a Google doc

Templating OER pages

AI Detection

This remains a sticky wicket, generally unpopular. Here's more information and guidance on using AI detectors.

From Ethan Mollick, author of Co-Intelligence: "When researchers secretly added AI-created the papers to the exam pool: 'We found that 94% of our AI submissions were undetected. The grades awarded to our AI submissions were on average half a grade boundary higher than that achieved by real students.' And, of course, automatic AI detectors are not reliable."

Mollick's LinkedIn post: You can't detect AI writing.

Anna Mills' LinkedIn post

"I know so many colleagues I respect are highly critical of AI detection, seeing it as signaling antagonism toward students. Christopher Ostro clarifies that his purpose is not to punish students but to provide some accountability that, in the end, encourages learning and shows that we care. He says, 'I am not a cop teacher. I am not someone who likes catching cheaters. I’m not someone who wants that to be a big part of my job. Honestly, it’s the least fun part of teaching, but it’s also it is still a part of the job.'

Anna Mills' LinkedIn post re: AI detection strategies

Anna Mills post regarding AI detection

Process Tracking

What is process tracking and how is it used to deter AI misuse?

November 25, 2024 · by MLA-CCCC Joint Task Force on Writing and AI ·

We are probably all familiar with version history in Word or Google Docs and may have heard of students being asked to or choosing to share version history to provide evidence that they wrote something. By “process tracking” we are referring to a wide range of practices that involve gathering data about how a piece of writing was created. Depending on how process tracking is done, such data can include

How much time was spent actively working on the document
What text was copied and pasted
Which websites text was copied and pasted from
Which sections of the text were typed
How much editing and revision there was
How much text was edited using AI grammar and style suggestions
A replay of the writing process, including early versions and editing
What dates and times the document was worked on

We note that the last year has seen a proliferation of different software approaches to tracking the student writing process in order to prevent or detect AI misuse. In some cases, this functionality is offered alongside AI detection, and in some cases it is framed as an alternative to AI detection. Some schools or individual teachers purchase proprietary software that may or may not sync with an LMS. Another approach is for instructors ask students for edit access to documents and then use free browser extensions to view process history. In yet another model, students opt to track their own history and view a report that they may be required to share with their teacher.

Some considerations include

Is asking for data on the writing process history overly intrusive? Is it a form of surveillance or close observation more akin to in-class writing? Are there lines to be drawn in terms of what should be tracked and what should not?
Alternate writing processes and accessibility: Students may want to or need to write using different software (such as a phone app or a dictation app) than the process tracking system mandated by the instructor offers.
How effective is process tracking at deterring AI misuse? We note that students could still retype AI text, spend time in a document, and insert edits. To what extent would the effort involved in doing that reduce misuse? To what extent will software that effectively simulates a human writing process become widely available?

In initial task force discussions, we find that members have a variety of sometimes conflicting views on process tracking. Here we offer some preliminary perspectives.

Prompt: What are the possibilities, limits, and implications of process tracking programs for faculty in reading and writing-intensive disciplines ?

Kofi Adisa

I typed a rant, deleted it, and I started again. And again. If I were a student drafting my response to the prompt, I might feel as if my process is idiosyncratic, perhaps even a secret; however, if I knew as a student, I had to share my process or worse to see that it was being tracked and that information was somehow in the purview of my professor, I probably would be too self-conscious and worried that my process was judging my writing. As a professor, I never ask students to do something for the class that I couldn’t do if I were a student. The closest I ever got to requiring students to share their process was to have them share their notes or annotations on or about a particular reading. I cannot imagine having them walk me through their reading and writing process or even wanting to track their process with some kind of technology. Yet, I know there are faculty at my institution who welcome this kind of intrusiveness. (I see it as an intrusion; it’s like asking me what my diary entry was when I was a young boy or some other kind of personal matter that’s unnecessary for me to respond to a prompt.)

My colleagues have argued that tracking student writing helps students “see” their writing differently. They also argue that it gives them insight into how students think about a topic. That’s why they “love” having students use G-docs. And I will admit that seeing “version history” is enlightening, but is it something worth evaluating? Is having students share any moment of their process necessary for me, their professor? I don’t think so. However, as more and more students use AI tools, I believe some faculty may rely too much on the surveillance of writing than the actual teaching of it.

I may come back to these paragraphs and rewrite them. Again. The human part of writing is having an authentic voice, a sound. Not every student desires this part of the writing process, nor do they always value reading the voice of a text. But I try to draw students’ attention to it so they can see and hear it for themselves and in themselves.

Antonio Byrd

Writing Studies has been (justifiably) obsessed with process for decades, going back to the 1960s when writing became a subject of research. Foundational scholars such as Janet Emig, Peter Elbow, and Donald Murray advocated for a focus on teaching the writing process. They opposed the dominant product-focused pedagogy, which focused on learning the features of four modes of writing – narrative, expository, expository, and persuasive. Teaching for product meant asking students to follow formulas, adhere to correct grammar, punctuation, mechanics, and neatness. But the entire experience writing itself – from ideation to final product – was more complicated, as Emig wrote in her book The Composing Process of Twelfth Graders. Murray describes the writing process accordingly:

What is the process we should teach? It is the process of discovery through language. It is the process of exploration of what we know and what we feel about what we know through language. It is the process of using language to learn about our world, to evaluate what we learn about our world, to communicate what we learn about our world.

There is one caveat: Murray distiless writing process to three stages: prewriting, drafting, and revision. As Writing Studies developed different perspectives on what mattered to the writing process, from cognition to expressivism to sociocultural influence, teachers fell into the trap of making the writing process a series of stages. My graduate students and I reviewed the process movement and we all agreed that the different stages are helpful for structuring a class over 16 weeks but in reality the writing process is more complicated, is, dare I say Writing Studies’s favorite words, recursive and/or iterative. To remind ourselves and to teach our students this reality is to embrace post-process theory, which apparently never really took off but nevertheless echoes in our disciplinary language.

There’s more in the process movement! I recently assigned my graduate students a couple of chapters from Writing Workflows: Beyond Word Processing. Based on qualitative research, Tim Lockridge and Derek Van Ittersum argue for analyzing the writing technologies we use in our writing process. What tools do we load up on our computers or what notebooks and pens do we grab to do the work of composing? Workflow and the friction we encounter while working with the writing tools we’ve picked remind me that writing is networked, ecological.

And how do we account for the writing process? I loved portfolio-based assessment when I was a graduate teaching assistant for University of Wisconsin-Madison. I disliked rubrics and enjoyed writing in response to my students’ writing memos, which detailed how all the pieces in their portfolio connected to learning about writing, and the final product which did and did not matter to me.

Why have I used this space to draw a super rough sketch of the process movement? Because process-tracking software, I think, doesn’t align with our values in Writing Studies. We care about text, but we also care about context and subtext. We care about the narrative our students weave about their process, and we care about their takeaways, like what transfers and what students learn about themselves as writers. I guess if you use a version of labor-based contract grading that asks students to keep track of their time working on writing, process tracking fits in your pedagogy. But I understand the writing process as more than time on text or text copy and pasted, but, ideally speaking, a transaction of love between writer and reader. I accept the expansiveness of the writing process, with all its messiness, with all the anxiety students may feel, with all the triumph we both find at the end. I believe if you create a classroom where writing is zero stakes (we’re not trying to publish anything we write after all. Most of the time.), then we have less need to use genAI or at the least mitigate its presence, so we can only connect through the sharing of language.

Leonardo Flores

Process tracking software, such as Grammarly’s new service “Authorship,” is being presented as a tool that allows people to defend their writing by providing evidence of their process to others in case they are accused of using AI. This is troubling to me on two levels: the first being that it is built with mistrust as a baseline. And because there are power imbalances in many educational and writing situations, it is a simple matter for faculty, publishers, or institutions to require the use of this software as a form of surveillance against those who are doing the writing. So what is offered as an opt-in tool can very easily become what Jeffrey Moro called “cop shit,” which he defined as “any pedagogical technique or technology that presumes an adversarial relationship between students and teachers” (2020). This subverts what is a good thing in writing pedagogy– focusing on process– and turns it into a prescriptive and auditable form of surveillance.

My second concern is that it can and will be used by educators to avoid updating their pedagogical practices to account for the impact of generative AI. Like AI detection software, it is simply another weapon in an AI arms race. AI detection has been proven to be ineffective and problematic, it has still been widely adopted by faculty and continues to be developed and marketed by educational technology companies. Both AI detection and process tracking can be easily circumvented, but the harm they do to writing practices and pedagogical situations are lasting.

We cannot turn back the clock to a time when writing could only be done by humans and was therefore a convenient tool for evaluation of student work. Generative AI technologies are being increasingly integrated into writing tools and workflows and they will become ubiquitous and naturalized for current and future generations. We need to cultivate AI literacy and update our teaching and writing practices to prepare our students for a world inflected by these technologies.

Liz Losh

We should be mindful of potential stigma associated with certain kinds of writing practices, such as writing large parts of papers on cell phones or using speech-to-text software. If we force students to disclose information about how and when they write, it could undermine trust and egalitarianism. These kinds of writing practices should not raise any red flags for academic integrity, but students may feel shame about what could be seen as non-normative behavior. They might feel uncomfortable about not owning their own computer or having learning anomalies that require adaptive tools. In Jay Dolmage’s “Writing Against Normal” (2012), he observes that too many writing teachers “have not acknowledged that we have a body, bodies; we cannot admit that our prevailing metaphors and tropes should be read across the body, or that our work has material, corporeal bases, effects and affects.” Surveilling these embodied interactions can make many students feel self-conscious. We already saw this during online teaching at the start of the Covid pandemic, when students who attended class from their beds or their cars didn’t want their cameras on.

As I argue in The War on Learning (2014), it is often not really disrespect for academic integrity or academic honesty that aggravates educators. It is academic labor. Some students don’t invest their labor in the learning enterprise and faculty feel that they must take on more labor in the interest of fairness. Unfortunately, just like a timeclock, it is difficult to always establish who is on the job and when. And we as faculty might not want our own time-stamped feedback practices to be visible to students.

Anna Mills

As in so many other domains like parenting and work, supporting intrinsic motivation goes a long way, but some form of accountability is still needed. Whether or not we allow use of AI, teachers do need to know what a student wrote in order to meaningfully assess and respond. AI misuse is less likely if pedagogy supports student motivation, a sense of belonging, and support to weather the ups and downs of writing processes. However, even with the best pedagogy, some temptation to take shortcuts with AI will remain when students feel insecure or under pressure. We can’t prevent all such misuse, but we can significantly reduce it by making it more of a hassle.

I have been incorporating process tracking in a limited form into my writing classes for the last year and a half. According to a recent poll I did on X, educators are split on this topic, but it seems to me a much better approach than AI text detection, which is inaccurate, likely biased, easily circumvented, and not transparent. Accountability doesn’t have to mean an adversarial relationship; I try to approach it in a spirit of collaborative reflection. If a report shows a big copy/paste or only a few minutes of time spent, I meet with the student to understand what happened. If they tell me they used their phone to dictate something, we discuss the content a little, and then I generally take their word for it. If it seems they did misuse AI, I offer them a chance to rewrite. Going forward, I want to find a way students can comment in the margin of their process report so it helps them reflect on the strategies they used and how they worked with sources or AI.

We don’t consider in-class writing to be surveillance, and I hope we can establish norms for process tracking that are parallel to the kind of observation we do in class. In my class, students see their writing process report first, and I ask them to share only total time spent, which text was typed and which was copied and pasted, and how Grammarly was used–not early versions or timestamps. As with in-class writing, we need to provide accessible alternatives. I offer to work out another way of showing a student’s process, such as an extra conference with me, if the student is not comfortable with process tracking.

This semester, several students have shared with me that they have cheated with AI in the past, and they see other students cheating with it. Acknowledging that this is tempting and asking for some transparency is reasonable. I’m trying to send the message that their learning matters to me, and I want to know their words are theirs.

Copyright

In early 2023, the Copyright Office announced a broad initiative to explore the intersection of copyright and AI. Since then, the Copyright Office has issued registration guidance for works incorporating AI-generated content, hosted public listening sessions and webinars, met with experts and stakeholders, published a notice of inquiry seeking input from the public, and reviewed more than 10,000 responsive comments, which served to inform these conclusions.

The report is being released in three Parts.

Part 1 was published on July 31, 2024, and recommended federal legislation to respond to the unauthorized distribution of digital replicas that realistically but falsely depict an individual.
In January 2025, Part 2 of its report on the legal and policy issues related to copyright and artificial intelligence. This Part of the Report addresses the copyrightability of outputs created using generative AI.

The Copyright Office affirms that existing principles of copyright law are flexible enough to apply to this new technology, as they have applied to technological innovations in the past. It concludes that the outputs of generative AI can be protected by copyright only where a human author has determined sufficient expressive elements. This can include situations where a human-authored work is perceptible in an AI output, or a human makes creative arrangements or modifications of the output, but not the mere provision of prompts.
The Copyright Office confirms that the use of AI to assist in the process of creation or the inclusion of AI-generated material in a larger human-generated work does not bar copyrightability. It also finds that the case has not been made for changes to existing law to provide additional protection for AI-generated outputs.
“After considering the extensive public comments and the current state of technological development, our conclusions turn on the centrality of human creativity to copyright,” said Shira Perlmutter, Register of Copyrights and Director of the U.S. Copyright Office. “Where that creativity is expressed through the use of AI systems, it continues to enjoy protection. Extending protection to material whose expressive elements are determined by a machine, however, would undermine rather than further the constitutional goals of copyright.”

The final, forthcoming Part 3 will address the legal implications of training AI models on copyrighted works, including licensing considerations and the allocation of any potential liability.

Resources on Refusing, Rejecting, and Rethinking Generative AI
in Writing Studies and Higher Education

Click on the expanders below for more resources on resistance than you could possibly want.

Attribution: Resources on refusing, rejecting, and rethinking generative AI in writing studies and higher education.

Resources on Refusing, Rejecting, and Rethinking Generative AI in Writing Studies and Higher Education

Teachable Pieces on GenAI

If you’d like to join the refusal conversation, you can join the Discord server for the Alliance for Refusing GenAI (ARG AI).

Th following list of resources is a companion project to the podcast Everyone’s Writing with AI (Except Me!), hosted by Maggie and Megan. Suggested Citation: Fernandes, M., McIntyre, M. Gray, K., & Messina, C.M. (2024).

The pieces in this section are approachable and illuminate key conversations about the impact of GenAI.

Algorithmic Bias and Oppression

The pieces in this section illuminate how bias is embedded in algorithmic technologies and the implications of algorithmic oppression.

Race

Ruha Benjamin (2019), Race After Technology
DISCO Network (2025), Technoskepticism: Between Possibility and Refusal
Timnit Gebru and Émile P. Torres (2024), “The TESCREAL bundle: Eugenics and the promise of utopia through artificial general intelligence”
Safiya Noble (2018), Algorithms of Oppression: How Search Engines Reinforce Racism
Khari Johnson (2022), “How to Stop Robots from Becoming Racist”
James O’Donnell (2024), “LLMs Become More Covertly Racist with Human Intervention”
Jesutofunmi Omiye, Jenna C. Lester, Simon Spichak, Veronica Rotemberg & Roxana Daneshjou (2023), “Large Language Models Propagate Race-Based Medicine”
Hofmann, Kalluri, Jurafsky, King (2024), “Dialect prejudice predicts AI decisions about people's character, employability, and criminality”
Garance Burke and Hilke Schellman (2024), “Researchers say an AI-powered transcription tool used in hospitals invents things no one ever said”

Gender

DISCO Network (2025), Technoskepticism: Between Possibility and Refusal
Hadas Kotek, Rikker Dockum, and David Sun (2023), “Gender Bias and Stereotypes in Large Language Models”
Safiya Noble (2018), Algorithms of Oppression: How Search Engines Reinforce Racism
Jeffrey Dastin (2018), “Insight - Amazon scraps secret AI recruiting tool that showed bias against women”
Emmanuel Maiberg (2024), “Microsoft Provided Gender Detection AI On Accident”
Lisa Stiffler (2024), “AI Overwhelmingly Prefers White and Male Job Candidates in New Test of Resume-Screening Bias”
UNESCO (2024), Challenging systematic prejudices: an investigation into bias against women and girls in large language models

Sexuality

William Agnew, Kevin R. McKee, and Jackie Kay (2023), "Technologies of Resistance to AI."
Sophia Chen (2024), “The Lost Data: How AI Systems Censor LGBTQ+ Content in the Name of Safety”
Anaelia Ovalle, Palash Goyal, Jwala Dhamala, Zachary Jaggers, Kai-Wei Chang, Aram Galstyan, Richard Zemel, and Rahul Gupta (2023), “‘I’m Fully Who I Am’: Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation”
Reece Rogers (2024), “Here's How Generative AI Depicts Queer People”
Arjun Subramonian (2024), “AI and Queer Communities”
Eddie L. Ungless, Björn Ross, and Vaishak Belle (2023), “Potential Pitfalls with Automatic Sentiment Analysis: The Example of Queerphobic Bias”
Eddie L. Ungless, Björn Ross, and Anne Lauscher (2023), “Stereotypes and Smut: The (Mis)Representation of Non-Cisgender Identities by Text-to-Image Models”

Disability

Vinitha Gadiraju, Shaun Kane, Sunipa Dev, Alex Taylor, Ding Wang, Emily Denton, and Robin Brewer (2023), "I wouldn’t say offensive but...": Disability-Centered Perspectives on Large Language Models”
Ian Moura (2023), “Encoding normative ethics: On algorithmic bias and disability”

Art, Writing, Cognition and Linguistic Philosophy

The pieces in this section consider the impact of GenAI tools on art, creative writing, and linguistic philosophy.

Ted Chiang (2024), “Why A.I. Isn’t Going to Make Art”
Noam Chomsky (2023), “Noam Chomsky: The False Promise of ChatGPT”
Hao Pang Lee, Ian Drosos, Advait Sarkar, Lev Tankelevitch, Sean Rintel, Richard Banks, and Nicholas Wilson (2025), The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers.
Emanuel Maiberg (2025), “Microsoft Study Finds AI Makes Human Cognition “Atrophied and Unprepared””

Debunking GenAI Hype

The pieces in this section offer counternarratives to the hype surrounding GenAI. These readings range from its ineffectiveness in the outputs the technology promises (writing feedback and assessment, summarizing readings, etc.) to how the technology itself is not good for business.

Shazeda Ahmed (2023), “From Algorithmic Monoculture to Epistemic Monoculture?”
William Agnew, A. Stevie Bergman, Jennifer Chien, Mark Díaz, Seliem El-Sayed, Jaylen Pittman, Shakir Mohamed, and Kevin R. McKee (2024), “The Illusion of Artificial Inclusion”
Ali Alkhatib (2024), “Destroy AI”
Jackie Davalos and Leon Yin (2024), “AI Detectors Falsely Accuse Students of Cheating–With Big Consequences”
Artemis T. Douglas (2024), “Is AI supplanting the purpose of higher education?”
Henry Farrell (2024), “What OpenAI Shares with Scientology”
Henry Farrell (2024), “After Software Eats the World, What Comes Out the Other End?”
Mike Isaac and Erin Griffith (2024), “OpenAI Is Growing Fast and Burning Through Piles of Money”
Shirin Ghaffary (2023), “Universities Rethink Using AI Writing Detectors to Vet Student Work”
Yekyung Kim, Yapei Chang, Marzena Karpinska, Aparna Garimella, Varun Manjunatha, Kyle Lo, Tanya Goyal, Mohit Iyyer (2024), “FABLES: Evaluating faithfulness and content selection in book-length summarization”
Emanuel Maiberg (2024), “The Editors Protecting Wikipedia from AI Hoaxes”
Dan McQuillan (2023), “ChatGPT: The World’s Largest Bullshit Machine”
Inioluwa Deborah Raji, I. Elizabeth Kumar, Aaron Horowitz, Andrew D. Selbst (2022), “The Fallacy of AI Functionality.”
Kylie Robison (2024), “Agents Are the Future AI Companies Promise–And Desperately Need”
Patricia Taylor (2024), “The Imperfect Tutor: Grading, Feedback and AI”
Annette Vee (2024), “The Moral Hazards of Technical Debt in Large Language Models: Why Moving Fast and Breaking Things is Bad”
Anette Vee (2024), “AI and New Promises for Teaching at Scale”
John Warner (2024), “Not So Fast on Teaching AI ‘Skills’”
Cam Wilson (2024), “AI worse than humans in every way at summarising information, government trial finds”
Edward Zitron (2024), “OpenAI Is A Bad Business”

Educational Technology and Surveillance

The pieces in this section discuss how the rise of GenAI tools has led to a proliferation of educational technology tools that tend to increase classroom surveillance and may harm student privacy.

Collin Bjork (2024), “Clones in the classroom: why universities must be wary of embracing AI-driven teaching tools”
Maggie Fernandes and Megan McIntyre (2025), “Drafting defensively, documenting authorship: an analysis of Draftback and Grammarly Authorship.”
Antero Garcia, Charles Logan, and T. Phillip Nichols (2024), “Inspiration from the Luddites: On Brian Merchant’s ‘Blood in the Machine’”
Chris Gilliard and Pete Rorabaugh (2023), “You’re Not Going to Like How Colleges Respond to ChatGPT”
Shirin Ghaffary (2023), “Universities Rethink Using AI Writing Detectors to Vet Student Work”
Matthew Kirschenbaum and Rita Raley (2024), “AI May Ruin the University as We Know It”
Jason Koebler (2024), “Massive E-Learning Platform Udemy Gave Teachers a Gen AI 'Opt-Out Window'. It's Already Over.”
Safiya Noble and Sarah T. Roberts (2017), “Out of the Black Box”
Suzanne Smalley (2022), “Course Hero contends with student privacy concerns”
Annette Vee (2024), “Student Data is the New Oil”
S.L. Nelson and Annette Vee (2022), "The View from "Zoom University": Surveillance and Control in Higher Ed's Pandemic Pedagogy Pivot"
Audrey Watters (2024), “AI for Breakfast”

Links to an external site.

Environmental Damage and GenAI

The pieces in this section call attention to the widespread environmental harms of GenAI tools and the companies that own them.

Emily Bender, Timnit Gebru, Angelina McMillan-Major, and Schmargaret Schmitchell (2021), “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?”
Adrien Berthelot, Eddy Caron, Mathilde Jay, and Laurent Lefèvre (2024), “Estimating the environmental impact of Generative-AI services using an LCA-based methodology”
Brian Calvert (2024), “AI already uses as much energy as a small country. It’s only the beginning.”
Kate Crawford (2021), Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence
David Danieliski (2023), “AI Programs Consume Large Volumes of Scarce Water”
Dustin Edwards (2020), “Digital Rhetoric on a Damaged Planet: Storying Digital Damage as Inventive Response to the Anthropocene”
Karen Hao (2024), “Microsoft’s Hypocrisy on AI”
Mél Hogan (2024), “The Fumes of AI”
Mél Hogan and Théo Lepage-Richer (2024), “Extractive AI”
Dara Kerr (2024) “How Memphis became a battleground over Elon Musk’s xAI supercomputer”
Rebecca Solnit (2024), “Is it Worse to Have No Climate Solutions–Or to Have Them but Refuse to Use Them?”
Ana Valdivia (2024), “The Supply Chain Capitalism of AI: A Call to (Re)Think Algorithmic Harms and Resistance Through Environmental Lens”
Trent Wintermeier (2024), “Affective Footprints”
Jon Ippolito (2025), "AI's Impact on Energy and Water Usage"

Intellectual Property, Labor, and GenAI

The pieces in this section discuss the implication of GenAI tools for intellectual property, including the misuse of copyrighted training data, and call attention to the impacts of GenAI on workers and working conditions across multiple industries.

Johndan Johnson-Eilola (1996), “Relocating the Value of Work: Technical Communication in a Post-Industrial Age”
J. Eliza Chapman (2024), “Citation Ethics Revisited”
Devin Coldewey (2024), “Perplexity CEO offers AI Company’s Services to Replace Striking NYT Staff”
Molly Kinder (2024), “Hollywood writers went on strike to protect their livelihoods from generative AI. Their remarkable victory matters for all workers.”
Jason Koebler (2024), “Massive E-Learning Platform Udemy Gave Teachers a Gen AI 'Opt-Out Window'. It's Already Over.”
Jason Koebler (2025), “OpenAI Furious DeepSeek Might Have Stolen All the Data OpenAI Stole From Us”
Brian Merchant (2024), “AI really is smoke and mirrors”
Kathryn Palmer (2024), “Taylor & Francis AI Deal Sets ‘Worrying Precedent’ for Academic Publishing”
Billy Perrigo (2022), “OpenAI Used Kenyan Workers on Less than $2 Per Hour to Make ChatGPT Less Toxic”
Niamh Rowe (2023), “‘It’s destroyed me completely’: Kenyan moderators decry toll of training of AI models”
Tech Won’t Save Us (2023), The Human Side of the AI Underclass w/ Joanne McNeil
Annette Vee (2024), “Student Data is the New Oil”

Linguistic Justice and GenAI

The pieces in this section grapple with the impact of GenAI on world Englishes and non-dominant varieties of English and how genAI furthers linguistic injustices.

Emily Bender, Timnit Gebru, Angelina McMillan-Major, and Schmargaret Schmitchell (2021), “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?”
Jackie Davalos and Leon Yin (2024), “AI Detectors Falsely Accuse Students of Cheating–With Big Consequences”
Carmen Kynard (2023), “When the Robots Come Home to Roost: The Differing Fates of Black Language, Hyper-Standardization, and White Robotic School Writing (Yes, ChatGPT and His AI Cousins)”
Weixin Liang, Mert Yuksekgonul, Yining Mao, Eric Wu, and James Zou (2023), “GPT Detectors are Biased Against Non-Native English Writers”
Keoni Mahelona, Gianna Leoni, Suzanne Duncan, Miles Thompson (2023), “OpenAI's Whisper is another case study in Colonisation”
Alfred Owusu-Ansah (2023), “Defining Moments, Definitive Programs, and the Continued Erasure of Missing People”
Roberto Santiago de Roock (2024), "To Become an Object Among Objects: Generative Artificial “Intelligence,” Writing, and Linguistic White Supremacy"
John Timmer (2024), “LLMs have a strong bias against use of African American English”

Misinformation/Disinformation and GenAI

The pieces in this section grapple with the implications of generative AI for the proliferation of misinformation and disinformation.

Garance Burke and Hilke Schellman (2024), “Researchers say an AI-powered transcription tool used in hospitals invents things no one ever said”
Emilio Ferrar (2024), “GenAI against humanity: nefarious applications of generative artificial intelligence and large language models”
Jason S. Lucas, Barani Maung Maung, Maryam Tabar, Keegan McBride, and Dongwon Lee (2024), “The Longtail Impact of Generative AI on Disinformation: Harmonizing Dichotomous Perspectives”
Tate Ryan-Mosley (2023), “How generative AI is boosting the spread of disinformation and propaganda”
Emanuel Maiberg (2024), “The Editors Protecting Wikipedia from AI Hoaxes”

Settler Colonialism and GenAI

The pieces in this section examine the relationship of generative AI and settler colonialism.

Collin Bjork (2024), “Clones in the classroom: why universities must be wary of embracing AI-driven teaching tools”
Kate Crawford (2021), Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence
Karen Hao (2022), “Artificial intelligence is creating a new colonial world order”
Chris Lindgren, Erin Yunes, and Cana Uluak Itchuaqiyaq (2025), "Technical Communication's Fight Against Extractive Large Language Modeling by Applying FAIR and CARE Principles of Data."
Keoni Mahelona, Gianna Leoni, Suzanne Duncan, Miles Thompson (2023), “OpenAI's Whisper is another case study in Colonisation”
Jasmina Tacheva and Srividya Ramasubramanian (2023), "AI Empire: Unraveling the interlocking systems of oppression in generative AI's global order"

Surveillance, Data Privacy, and GenAI

The pieces in this section examine generative AI and its relationship to data privacy and surveillance capitalism.

Kate Crawford (2021), Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence
Shirin Ghaffary (2023), “Universities Rethink Using AI Writing Detectors to Vet Student Work”
Todd Feathers (2024), “This AI Tool Helped Convict People of Murder. Then Someone Took a Closer Look”
Barbara Prainsack and Seliem El-Sayed (2022), “Big Tech Makes Political Choices About Our Data: We Should Do the Same”
Bruce Schneier (2023), “The Internet Enabled Mass Surveillance. A.I. Will Enable Mass Spying”
Jasmina Tacheva and Srividya Ramasubramanian (2023), "AI Empire: Unraveling the interlocking systems of oppression in generative AI's global order"
Nicholas Vincent, Hanlin Li, Nicole Tilly, Stevie Chancellor, and Brent Hecht (2021), “Data Leverage: A Framework for Empowering the Public in its Relationship with Technology Companies”
Privacy International, (2023) “Generative AI won't take over the world, surveillance capitalism already has | Privacy International”

Techno-Oligarchy and Techno-Fascism

Gebru and Émile P. Torres (2024), “ The TESCREAL bundle: Eugenics and the promise of utopia through artificial general intelligence ”
In Bed with the Right (2025) podcast, “ Episode 62: AI Slop and the New Fascist Aesthetic with Roland Meyer ”
Adrienne LeFrance (2024), “ The Rise of Techno-authoritarianism .”
Erin McElroy (2024), Silicon Valley Imperialism: Techno Fantasies and Frictions in Postsocialist Times.
Daniel McQullen (2022), Resisting AI: An Anti-fascist Approach to Artificial Intelligence