Slide 1 of 34
I Learned It From Watching You!
- I Learned It From
- Watching
- You!
- Realistic Strategies for Handling Student AI Use
- Jon Fila
- Founder and AI Strategist, Inclusive AI Strategies™
Image: Inclusive AI Strategies logo
Animation: A paved walkway turns a corner, and a worn dirt path cuts across the grass. An adult’s footprints appear one at a time along the dirt path, left, right, left, then a child’s smaller yellow footprints follow, stepping inside the adult’s prints. The walk repeats every few seconds while the slide is up.
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Slide 2 of 34
You should just…
- If your solution to a problem relies on “If everyone would just…” then you do not have a solution.
- Everyone is not going to just.
- At no time in history has everyone just, and they’re not going to start now.
Image: decorative illustration of a head in silhouette with a lit light bulb and sketches of charts and gears around it.
Animation: Like live captions, the words of the first line appear one at a time when the slide opens; each follow-up line appears the same way on its click.
Slide 3 of 34
Desire path
- The worn dirt path people make when the planned route is not the one they want.
- Photo: “Desire path,” The Ohio State University, by Dan Keck, CC0 (public domain)
Image: A paved campus sidewalk curves away while a worn dirt path cuts straight across the grass between two walkways. A rental scooter stands at the edge of the pavement.
Animation: The photo eases from slightly zoomed in to full frame.
Slide 4 of 34
Two ways to respond to a desire path
- Rope it off
- Flagging and new turf over the path people made
- Pave it
- A desire path in Delft that the city accepted
- Photos: “Desired path in a park” by Maedelo, CC BY-SA 4.0; “Olifantenpad Delft 2023” by Vera de Kok, CC BY-SA 4.0 (Wikimedia Commons; cropped and resized).
Image: In a park, a desire path beside the official walkway has been roped off with orange flagging and replanted with new turf.
Image: On a street corner in Delft, a gravel path follows the curve people walked across the grass, edged like an official walkway.
Animation: The second photo, “Pave it,” rises in on the next step.
Slide 5 of 34
Detection will always be bypassed
- 2023: OpenAI pulled its own AI text classifier “due to its low rate of accuracy.”
- March 2026: 34% of GPT-5.4 text caught by Pangram when the model came out, until Pangram retrained.
- 2026: 24.1% caught after an automated rewriting prompt, down from 94.7%.
- 2026: 38–80% of honest, light AI edits to human writing flagged. After a humanizer, under 4% of AI rewrites were.
- Sources: OpenAI, “New AI classifier for indicating AI-written text,” 2023 (withdrawn July 20, 2023); Ren, Raghavan and Garg, “Hitting a Moving Target,” arXiv:2606.25152, June 2026 (preprint); Karr, Khvatskii, Hua and Chawla, “Why AI Detection Fails for Academic Integrity,” ACM AI Summit 2026.
Animation: The 2023 line appears, then the two Pangram cards rise in together, then the light-editing card.
Slide 6 of 34
In 2023, detectors flagged essays by non-native English writers
- Flagged as AI by at least one detector: 89
- Never flagged: 2
- 89 of 91 human-written TOEFL essays were flagged as AI by at least one of seven detectors.
- 61.22% average false positive rate. Essays by U.S. 8th graders were classified near-perfectly.
- One prompt, “Enhance the word choices to sound more like that of a native speaker,” cut it to 11.77%.
- 2025: When writers polished their own text with AI, GPTZero labeled it 100% AI for 25% of non-native authors and 11% of native authors.
- Sources: Liang, Yuksekgonul, Mao, Wu and Zou, Patterns 4(7), 2023 (arXiv:2304.02819): 91 TOEFL essays, 88 U.S. 8th-grade essays, seven 2023 detectors. Pratama, PeerJ Computer Science 11:e2953, 2025, Table 6 (journal abstracts).
Animation: Ninety-one hollow circles, one per essay. On the first step, 89 of them fill in one after another; two stay hollow. The text blocks then rise in one at a time, and the 2025 finding comes last.
Slide 7 of 34
We must teach about AI
Animation: No animation.
Slide 8 of 34
Sycophancy is a persistent trait of AI assistants
- 2023: “Matching a user’s views is one of the most predictive features of human preference judgments.”
- 2026, 11 AI models: AI affirmed users’ actions 49% more often than people did. People trusted and preferred the agreeable AI, even as it distorted their judgment.
- Sources: Sharma et al. (Anthropic), “Towards Understanding Sycophancy in Language Models,” arXiv:2310.13548, 2023; Cheng et al., “Sycophantic AI decreases prosocial intentions and promotes dependence,” Science 391, March 26, 2026.
Animation: The 2023 quote, then the 2026 finding, rise in.
Slide 9 of 34
What people rewarded when they rated AI answers
- Each dot: how often people preferred an answer with that feature, all else equal. Right of the line means preferred.
- “Matches user’s beliefs” ranked first. People rating AI answers tended to prefer the ones that agreed with them. Models trained on those ratings learn to agree.
- Redrawn from Sharma et al., 2023, Figure 5; positions approximate. Anthropic’s helpfulness preference data (hh-rlhf), arXiv:2310.13548.
Chart: 23 features of AI answers, ranked by how strongly each predicted that people preferred the answer, from most to least (Funny is the only one below 50%):
- Matches user’s beliefs
- Authoritative
- Empathetic
- Relevant to user’s query
- Truthful
- Engaging
- Persuasive
- Matches user’s style
- Higher reading age
- Well-written
- Grammatically sound
- Logically sound
- Informative
- Rigorous
- Well-structured
- Friendly
- Entertaining
- Better evidentially supported
- Motivating
- Polite
- Concise
- Optimistic
- Funny
Animation: The dots start on the 50% line and move to their places, top row first. On the next click, the top row, “Matches user’s beliefs,” turns yellow with a brown dot and a “Ranked first” label, and the explanation rises in.
Slide 10 of 34
Three sycophancy habits, still in current models
- 1. Back down when questioned. 7.1%. 2026, five current models, medical cases: dropped or softened a correct answer under pushback with no new information (2.4% to 15.3% by model).
- 2. Give predictably biased feedback. 2023 models: warmer when the user says they like the work, harsher when they say they dislike it.
- 3. Go along with a wrong answer. 37%. 2026, Claude Sonnet 4.6, multiple choice: switched to a wrong answer when told “a subject expert chose” it. GPT-5.4: 17%.
- Sources: Hazare et al., medRxiv, May 2026 (preprint); Ravikiran and Vohra, arXiv:2609.08934, September 2026 (preprint); Sharma et al., arXiv:2310.13548, 2023.
Animation: The three cards rise in one at a time.
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AI tells students what they want to hear
- It tends to agree with the user, even when the user is wrong.
- How a question is framed changes how firmly it corrects a false claim.
- Models differ, and each new release can change how they behave.
- “I’m not trying to catch you. I’m concerned this tool is reinforcing misconceptions rather than helping you learn.”
Animation: The three findings, then the quote, appear one at a time.
Slide 12 of 34
Name where AI fits in every assignment
- Some assignments
- AI welcome as a starting point
- Use an LLM for initial research, then check its claims against real sources.
- Some assignments
- AI for one named step
- “You may ask an AI tool to ask you questions about your hometown to help you remember details.”
- When I’m assessing your thinking
- Fully original work
- “For this assignment, I need to see your thinking, not a tool’s output.”
Animation: The three bands rise in one at a time.
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Appropriate use and “Show Your Receipts”
- You might ask students to use an LLM for initial research, but then:
- Identify claims that need verification
- Evaluate the confidence
- Compare AI responses with authoritative sources
- AI Policy for This Unit
- If you use AI tools for any purpose, complete the “Show Your Receipts” form: share the prompt you used, the output you received, and explain what you kept, changed, or rejected and why.
- Sample AI policy language for online courses: inclusiveaistrategies.com/ai_policy (opens in a new tab)
Animation: The three steps appear together, then the policy box slides in, then the link to sample policy language.
Slide 14 of 34
Hallmarks of LLM responses
- Flag 13, Stock transitions: “Moreover,” “Furthermore,” “In conclusion”
- Flag 14, Inflated vocabulary: “delve,” “tapestry,” “leverage” (this one will only work for so long)
- Flag 15, Generic intensifiers: “plays a crucial role,” “highly effective”
- Flag 16, Time fillers: “In today’s world,” “Throughout history”
- Flag 18, Uniform structure: every paragraph the same length and shape
- Flag 19, Closing uplift: an inspirational last line that could end any paper
- See what it says
- Enter your existing assignments into an LLM to review the response.
- You get a sense of the default answer, so it is easier to recognize when a submission looks AI-generated.
- Know the defaults to recognize the patterns.
- Flag numbers from the AI Writing Flags Rubric: inclusiveaistrategies.com/ai-writing-flags-rubric (opens in a new tab). Models may vary.
Animation: The six flags appear together, then the “See what it says” panel slides in.
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Three rows from the AI Writing Flags Rubric
- Flag | Clear (full value) | Occasional (half value) | Pattern (no credit)
- 4. Dramatic setups | Sentences and paragraphs open with substance. | One or two setups announce a point before making it. | Setups come before most key points.
- “Here’s the thing:” “Honestly?”
- 8. Aphoristic flourishes | Literal verbs; headings and topic sentences describe what follows. | One or two tagline phrases or objects given motion or intent. | Key points are written as slogans; the plain claim is hard to find.
- “Your data travels with you.”
- 20. Generic examples | Includes at least one detail only this student, in this class, could have written: a personal experience, a class discussion, a local place, a specific source passage. | Examples are accurate but could belong to any student. | No example ties the writing to the student, the course, or the community.
- inclusiveaistrategies.com/ai-writing-flags-rubric (opens in a new tab) (20 rows; pick the ones that fit)
Animation: The three rubric rows rise in one at a time.
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Using the rubric alongside your content criteria
- Each row scores the writing on the page. No row accuses anyone of using AI.
- Clear
- full value
- Occasional
- half value
- Pattern
- zero
- Some students were taught these patterns
- Formula essays, transition-word lists and vocabulary drills in English language instruction produce “Moreover” and “In conclusion.” Start with feedback and a revision before you deduct.
- Three or more Pattern rows
- Hold a short conference before grading. Ask the student to explain one flagged sentence in their own words.
Animation: The two cards rise in one at a time.
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Assign value to the process
- Drafts
- Give drafts their own point value.
- Revision history
- “Show me what changed and explain why.”
- Break down tasks
- Smaller steps, each one visible.
- Immediate, personal, local
- Prompts tied to this class, this place, this student.
- Video submissions
- Ask for summaries and questions tied to timestamps.
- Check sources
- Students verify claims against real sources.
- AI responses to critique
- Hand students an AI answer and have them find what it gets wrong or leaves out.
- Align with UDL
- Options for how students show what they know.
Animation: The cards appear in three groups.
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What “show me” looks like
- Writing-intensive courses
- Drafts and outlines submitted before the final version
- Revision history: “Show me what changed between drafts and explain why”
- Voice memos or short videos about their writing choices
- Annotated bibliographies that show how sources shaped the argument
- STEM courses
- Shown work with an explanation of each step
- “Record yourself explaining how you solved this”
- Follow-up questions that require application, not recall
- Problems where students explain why a method works
- Build “show me” into the course so it’s a standing expectation, not something you ask for during a confrontation.
Animation: The two columns, then the closing line, rise in.
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What teachers hear
- “I barely used it and it was this one time.”
- “As a machine, I cannot sufficiently answer that question.”
Animation: The two speech bubbles rise in one at a time.
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Why these conversations are harder online
- No body language or facial expressions to soften your message
- Written words feel more permanent and more accusatory than spoken ones
- Students have time to build anxiety (or defensiveness) between messages
- Tone is easily misread in text. What you mean as concern lands as judgment.
- You can’t course-correct in real time if a message lands wrong
- Everything we write carries more weight when the class is asynchronous.
Animation: The five challenges rise in one at a time.
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Catching vs. coaching
- The stance you choose determines the conversation you’ll have.
- Catching
- Detective mode
- Gather evidence, build a case, confront.
- “This doesn’t look like your writing.”
- “Did you use AI for this?”
- “I ran this through a detector and…”
- What happens next
- Student shuts down or gets defensive
- Denials escalate
- Even if you’re right, you’ve lost the learning moment
- Coaching
- Curiosity mode
- Notice, inquire, support, teach.
- “I noticed some differences in this piece…”
- “Can you walk me through your process?”
- “Let’s talk about what support would help.”
- What happens next
- Student feels safe enough to be honest
- You learn what’s actually going on
- The learning moment is preserved
Animation: The catching panel rises in, then the coaching panel; on the last step both “What happens next” sections appear and a yellow outline lights up around the coaching panel.
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“I noticed…”
- Two words for async messages.
- They observe without accusing. They open a door instead of slamming one shut.
Animation: A text cursor blinks after the quote; the last line rises in.
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The graduated response
- What to do first, second, and third when you suspect AI use.
- Pause
- Don’t message yet. Does this match their prior work? Could there be another explanation? Read the whole submission.
- Inquire privately
- “I noticed some things in your submission I’d like to understand better. Can you tell me about your process?”
- Listen first
- Let them respond before you share what you saw. Their answer tells you what kind of conversation this is.
- Respond with next steps
- Offer a path forward: a revision, a skills conversation, a policy discussion.
Animation: Each step rises in while a yellow line fills the track from one step to the next.
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Shift the burden of proof
- The old model
- Teacher must prove the student used AI
- Requires detection tools (unreliable)
- Adversarial dynamic
- Teacher carries all the weight
- The new model
- Student demonstrates their understanding
- Requires showing process, not just product
- Collaborative dynamic
- Student carries appropriate responsibility
- “I don’t need to prove you used AI. I need you to show me you understand the material.”
Animation: The old model, the new model, then the quote rise in.
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The coaching stance at scale
- You don’t need 48 individual conversations. You need systems.
- Build it into your course
- Drafts, revision history or “explain your process” on every major assignment. When it’s routine, it’s not accusatory.
- Use Pause as triage
- Many can be handled with a standard revision-with-process option. Save the one-on-one for the ones that need it.
- Address patterns
- A class-wide message: everyone may resubmit with process documentation.
Animation: A row of 48 small message icons drops in; then the three systems rise in one at a time.
Slide 26 of 34
Tell families before there’s a problem
- Send this at the start of a term or unit. Don’t wait for an incident.
- Sample family message
- Dear families, AI tools like ChatGPT are part of our students’ world, and they will encounter them in their futures. In our course, we focus on teaching students to think critically and use these tools responsibly, not just to produce answers.
- Our AI policy is [specific]. Some assignments welcome AI as a starting point. Others require fully original work. We’re transparent about which is which.
- If your student is struggling with an assignment, I’d rather they reach out to me than turn to AI as a shortcut. I’m always here to help.
Animation: The sample message rises in.
Slide 27 of 34
Writing to a family about a concern
- Do
- Lead with concern for learning
- Share specific observations, not conclusions
- Offer a path forward together
- Ask what they’re seeing at home
- Frame AI as a shared challenge
- Don’t
- Use the word “cheating” in the first message
- Send evidence screenshots as proof
- Cite detection tool results as certainty
- CC administrators on the first message
- Compare the student to classmates
Animation: The Do column, then the Don’t column, rise in.
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When a parent pushes back
- “My child would never use AI.” “You’re singling out my kid.” “Prove it.”
- Validate
- “I understand your concern, and I appreciate you advocating for [student]. That’s exactly what I’d want from a parent.”
- Redirect
- “This isn’t about punishment. It’s about making sure [student] is getting the learning they deserve from this course.”
- Focus on observable facts
- “Here’s what I noticed in the work: [specific observations]. I’m not making a conclusion. I’m asking questions.”
- Partner
- “What would be most helpful is if we work together to support [student]. Can we set up a quick call or exchange to figure out next steps?”
Animation: The four steps rise in one at a time.
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Connection before correction
- The AI conversation is easier when it’s not the first real conversation you’ve had with a student.
- Name + specifics in feedback
- “[Name], the way you connected [X] to [Y] was strong.” Not “Good job.”
- Non-academic check-ins
- “Hey [Name], how’s everything going this week?” Once a month per student goes a long way.
- Celebrate before you correct
- When you do need to raise an AI concern, the student already has positive interactions with you to draw on.
- Humanize yourself
- “I’ve been learning about AI too, and I don’t have all the answers.”
Animation: The four strategies rise in one at a time.
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Reframing the message
- Instead of
- “Did you use AI?”
- Try
- “How did you approach this assignment?”
- Instead of
- “This looks AI-generated.”
- Try
- “I noticed this reads differently from your usual voice.”
- Instead of
- “You can’t use AI for this.”
- Try
- “For this assignment, I need to see your thinking, not a tool’s output.”
- Instead of
- “AI is cheating.”
- Try
- “Over-relying on AI means you miss the learning. That’s what I care about.”
Animation: One click starts an automatic sequence, one row at a time: the original phrase appears in plain text, a line strikes through it, then the “Try” version slides in beside it, and the next row begins. With motion off, all four pairs appear at once.
Slide 31 of 34
Your communication toolkit
- Lead with “I noticed”
- not “You did”
- Ask before telling
- “Can you walk me through your process?”
- Separate the work from the person
- “This submission,” not “You”
- Offer a path forward
- Always. Every time. Even when it’s hard.
- Build connection before you need it
- So the hard conversation isn’t the first one.
- Use the research as care
- “AI may be hurting your learning” is different from “You cheated.”
Animation: The six principles appear in two groups of three.
Slide 32 of 34 (event-specific details)
Practice the conversation
- The AI Conversation Simulator lets you rehearse talking with a student about AI use before the real conversation.
- iais.us/aiconversationsim (opens in a new tab)
- Access key: dlac26, good through October 30
- Jon Fila, Inclusive AI Strategies
Image: QR code for the AI Conversation Simulator with this event’s access key.
Animation: The QR code card rises in and the yellow corner brackets snap into place around it; the text rises in.
Slide 33 of 34
Questions?
Animation: on arrival, the heading and a decorative pair of speech bubbles rise in.
Slide 34 of 34
Jon Fila
- Founder and AI Strategist, Inclusive AI Strategies
- jon@iais.us
- inclusiveaistrategies.com (opens in a new tab)
- linkedin.com/in/jon-fila (opens in a new tab)
- Sign up for my newsletter: inclusiveaistrategies.com/#newsletter (opens in a new tab)
- Inclusive AI Strategies™ (label beside the logo)
Image: Jon Fila in a navy blazer and white shirt against a gray background
Image: Inclusive AI Strategies logo
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Get these slides: iais.us/studentaidlac26 (opens in a new tab)
Image: QR code for these slides, in the large yellow frame on the left. The photo of Jon Fila is now a small circle beside his name.