Guiding Students on Appropriate AI Use: Collaborative Approaches
Anna Mills, College of Marin
A presentation for the University of Nebraska, Kearney English Department
August 19, 2025
Licensed CC BY NC 4.0
Welcome! What to expect: One writing teacher’s approaches to addressing AI
Housekeeping
Yes to writing:
Framing the value of writing in an age of AI
Emphasize the purpose of each writing assignment. Why is writing in college important? Not for the product. For the thinking process.
“A fundamental tenet of Writing Across the Curriculum is that writing is a mode of learning. Students develop understanding and insights through the act of writing.”
Presentation by Anna Mills, licensed CC BY NC 4.0.
I start the semester by discussing the purpose of learning to write in college. We read the introduction to my (free OER) textbook How Arguments Work.
“What makes writing so valuable?”
“I would argue that academia and the professions need writing because it is our best tool for sharpening our thinking. It helps us slow down and clarify our ideas.”
Introduction, How Arguments Work: A Guide to Writing and Analyzing Texts in College
Writing is collaborative human communication
“We can think of academia as a conversation of many voices that speak to each other across time and place, through the medium of writing…We need each other’s help and input.”
Introduction, How Arguments Work: A Guide to Writing and Analyzing Texts in College
I put the learning purpose at the top of each writing assignment
A sample purpose section from a Summary and Response Essay Overview:
Purpose
Why we need writing practice more than ever in an era when AI can produce text
What does it take to use AI effectively and “add value”?
Yes to writing:
Designing assignments for intrinsic motivation and supporting writing practice
Let’s double down on what we know: research-based best practices for teaching writing have become yet more crucial
For one overview of principles in writing instruction, see the National Council of Teachers of English 2016 position statement Professional Knowledge for the Teaching of Writing.
Find ways students can make a writing assignment feel meaningful to them
Build relationships and community as the context for reading and writing�
Presentation by Anna Mills, licensed CC BY NC 4.0.
Teach and support writing processes
Collaborative annotation/social annotation of class readings (try Hypothes.is or Perusall)
Many of these strategies take time. Here are a few strategies for reducing the labor involved
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Three Mentimeter questions on writing pedagogy: what do you do, what might you add, what comments do you want to share?
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No to AI offloading:
Why we need guardrails for accountability
I focus on the positive, preventative, supportive practices just described. But I don’t find them to be adequate.
“Trusting students not to cheat isn’t fair to the students who don’t cheat no matter what….For all other human behaviors, we understand that people need help sticking to their goals or keeping their promises.” –Dr. Tricia Bertram Gallant, “Crafting Your GenAI & AI Policy Guide”
Let’s acknowledge some reservations around focusing on accountability
But even with the most motivating pedagogy in the world, students will sometimes be tempted to take shortcuts
This recent article “Everyone is Cheating Their Way Through College” in New York Magazine paints a dramatic picture. Even if it’s exaggerated, we’ve got to wonder, how much learning are students missing? How might the perception of widespread cheating will affect the value of course credits and grades?
We need ways to reduce potential learning loss and unfairness.
If we don’t know what is AI and what is the student’s, we can’t tell if the student is learning
Ignoring the impact of GenAI Tools on your Course Learning Outcomes undermines:
�This slide is from “Crafting Your GenAI & AI Policy:
A Guide for Instructors” by academic integrity expert Dr. Tricia Bertram-Gallant, shared under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license.
As you’re probably aware, there is no clear perfect way to ensure accountability and prevent AI misuse
Rundle et al argue that multiple imperfect means can be quite effective at reducing incidence of cheating
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I also find the metaphor of guardrails appealing because it reminds us of danger
What kinds of guardrails/swiss cheese layers might we set up? In the next sections, we’ll look at the following options:
AI Policies
Process observation
AI Detection?
AI Policy Guardrails
“You should be able to expect clear guidance from your instructor… guidelines should make clear which specific systems or tools are appropriate for any given assignment.”
--Kathryn Conrad in A Blueprint for an AI Bill of Rights for Education, Critical AI (Duke University Press)
AI is a lot to process for students as well as for us…
AI policy can offer
We don’t have to have the final answers on AI to make a policy: seek help and keep iterating
Sample policies
–From Crafting Your GenAI & AI Policy: A Guide for Instructors by Tricia Bertram Gallant of UC San Diego
A large collection: Classroom Policies for AI Generative Tools, curated by Lance Eaton
Templates and worksheets
Consider a “Tools and Rules” section for each assignment
The idea and phrase come from ESL instructor Julie Carey of Cañada College. Here’s some sample language I use:
Activity: discuss Joss Fong’s Vox.com video interviews with teachers and students
“AI can do your homework. Now what?
Students and teachers grapple with the rise of the chatbots.”
It’s hard to get specific enough about all possible uses of AI. That will always be a work in progress.
Consider: There are many possible uses beyond auto-generating the whole assignment (i.e. AI for brainstorming, AI for feedback, AI for help with organization, grammar, or genre conventions)
How can we make a policy that accounts for that variety and the variety of ways such uses could impact learning for different assignments?
The AI Assessment Scale developed by Leon Furze and colleagues is helpful; I’d like to share my adapted version.
An AI Assessment Scale (adapted)
Note: This is Anna Mills’ short version. See also the version that explains each one. This scale is adapted by Anna Mills from the Perkins, Furze, Roe, and McVaugh (2024). The AI Assessment Scale. CC BY NC SA. I have edited it and incorporated aspects of the earlier version by the same authors.
Further guidance on developing AI policies:
“Reinforcing Academic Integrity in the Age of AI: A Guide for Instructors” by Tricia Bertram Gallant of University of California, San Diego
Are you aware of any college, program, or department policies or guidance on AI use? Describe where to find any and/or what they say.
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Process Observation Guardrails
Observing students’ process as they write can take various forms. Which, if any, are intrusive?
Asking students to write in class at times can help but it’s not ideal to do all writing that way
We would have to give up too much class time to other essential learning activities.
Students wouldn’t have enough time and flexibility to experience an extended slow thinking writing process.
Handwritten timed work may not be a good sole measure for high stakes assessment.
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Still, some in-class writing, including digital writing, could be helpful
In-class writing doesn’t have to be the sole mode of assessment to help reduce AI misuse.
Used judiciously, it may allow us to get to know student voice so we inquire when there’s a discrepancy.
Of course, many students have anxiety about performance in class under time pressure or may find writing in class distracting.
Proctoring centers
Another option is to proctor student writing processes in writing centers or other proctoring centers.
I’ve heard one anecdote of a professor organizing this: Luke Fernandez at Weber State University in Utah arranged to require his students to write their essays at the writing center.
As we discussed, low stakes process assignments are good pedagogy.
Oral assessments can complement written ones
Caution: audio and video can be deepfaked pretty easily, for free
With platforms like HeyGen, anyone can upload a brief video of themself and create an avatar that will read AI scripts in AI-generated videos.
See this sample HeyGen X post where Wharton business school professor Ethan Mollick’s avatar speaks German (Mollick doesn’t).
Process tracking means ask students to share their document history
A user friendly version history can show how much time was spent, what was copied and pasted, what was edited, and more.
Note: process tracking is not foolproof. AI text can be retyped, either by hand or by free autotypers (alas).
One sample ad: “Need help typing your work? Duey.ai's Auto Typer for Google Docs helps you save time and effort by letting you create a Google Doc with a full Version History.”
Various apps and extensions allow replay and analysis of a document history, including cut/paste and time spent. Cost is $0 on up…
How much teacher labor does this involve? Some approaches involve some setup and then minimal labor.
If I ask students to share edit access to a Google Doc and enable a Chrome process tracking extension, I see a ribbon with process information across the top. No extra clicks or windows are needed.
I typically scan basic stats such as time spent typing in the document and large copy/paste events.
If something in those or in the style seems odd, I look at the full report, but this is more rare.
ProcessFeedback.org: nonprofit, educator created (Dr. Badri Adhikari, University of Missouri)
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ProcessFeedback.org Chrome extension offers a ribbon across the top of a Google Doc that tells the number of edits
If you click “Explore Process” you get a lengthy report that shows time spent, the history of what was copied and pasted, and much more (see this sample).
The level of detail may send the message to students that the value of the assignment lies in the process of working in the document.
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For a less adversarial setup, let the student generate and share/reflect on their process report
Grammarly Authorship
Screenshot from a Grammarly Authorship report
The user can decide if the authorship report includes a replay of the whole drafting process. I don’t require students to share this. I pay more attention to time spent.
Grammarly authorship reports attempt to label text by origin
Steps to try Grammarly Authorship
I ask students to reflect when they share a process report
Allow for alternative processes
My message to students: “Think of this as an online version of in-class writing that allows you more flexibility. It gives me a way to understand your writing process, both to encourage academic honesty and to encourage reflection on the writing process itself. If you have concerns or do not feel comfortable sharing your process in this way, please let me know and we can meet and work out another plan.”
The discussions of whether process tracking is surveillance are ongoing…
See “What is process tracking and how is it used to deter AI misuse?” a blog post from the MLA-CCCC Joint Task Force on Writing and AI. In it, I explain why I’m using process tracking, and the other task force members disagree with me.
AI detection guardrails
We all have the experience of reading text and trying to decide if we think it was AI generated.
How well can we really distinguish AI text from student writing?
Our intuition about what is AI text may help us initiate important conversations if we know the student’s writing well. But let’s be wary of our own unconscious bias and possible overconfidence. AI can imitate student writing styles.
Studies on the extent to which instructors can differentiate AI from student writing suggest we are quite unreliable (Darn!)
Could software be better than we are at identifying AI patterns in text? If so, is it ethical to use such software?
Is AI detection biased against English language learners? It’s unclear
Note: free software exists that rephrases AI text to “humanize” it and get around AI detectors
The accuracy of detectors is constantly changing as AI and detectors change. Right now, Turnitin seems to be ahead.
“Turnitin turned out to be the most accurate and consistent one, with a 100% AI score even with the adversarial techniques.”
It even did pretty well on AI text that was edited or paraphrased. Researchers used “three adversarial techniques (edited through Grammarly, paraphrased through Quillbot, and 10%-20% editing by a human expert).. The study found that the four AI-detection tools showed inconsistent AI scores - from very high by Turnitin (almost perfect) to very low by Writer AI. ”
–AI vs AI: How effective are Turnitin, ZeroGPT, GPTZero, and Writer AI in detecting text generated by ChatGPT, Perplexity, and Gemini? (Malik and Amjad 2025)
“For those who decide to use AI detectors, please consider the following questions”
From the MLA/CCCC Task Force on Writing and AI working paper on policy development. (The Modern Language Association and the Conference on College Composition and Communication are the professional associations for writing, language, and literature faculty in American higher ed.)
• What steps have you taken to substantiate a positive detection?
• What other kinds of engagement with the student’s writing affirm your decision to assign a failing grade outside the AI detector’s claim that the text was AI generated?”
I am gravitating toward non-punitive, cautious ways of using detection. For more discussion of this, see
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Three mentimeter questions on guardrails: what do you do, what will you add, what do you need to move forward?
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To sum up, here’s my approach, and I hope it stimulates your process as you find your own!
Questions or comments?�Thank you, and feel free to get in touch!
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This presentation is shared under a CC BY NC 4.0 license.