AI with Integrity: Bringing Clarity to the Learning Process #AskTurnitin
Got questions about AI in the classroom? Watch this panel discussion — then ask what's on your mind.
We've gathered a panel of educators to come together for a panel discussion on a question many of us are navigating: how can we support authentic student learning in a world where AI is everywhere?
The recording is attached above. Take some time to watch, then share your questions in this thread. We’ll be checking in and responding right here on TEN through July 3.
Answering your questions:
Jason Friend has been an English teacher at Saratoga High School since 2003. He is a founding member and the current program coordinator of the Media Arts Program, an interdisciplinary academy dedicated to innovative education. Passionate about thinking and writing, Jason has had several articles published in Philosophy Now. He received the Goldin Award for Excellence in Education in 2016, and was named Teacher of the Year for the Los Gatos-Saratoga Union High School District in 2026.
Melissa Rofer has just completed her first year as an English teacher at Los Gatos High School. Previously, she taught English at Cupertino High School from 2004 to 2010. Between these roles, she spent 14 years as a parent volunteer and K-12 substitute teacher. She has her Master's in Education from U.C. Santa Cruz and is a graduate of Humboldt State University.
Audrey Campbell is a Manager of Educator Engagement at Turnitin, where she connects educators with practical, real-world strategies for teaching in a rapidly changing landscape. Before joining Turnitin, she was a classroom teacher for ten years and understands the everyday realities educators face. She’s passionate about helping educators make sense of feedback, learning integrity, and the evolving role of AI in ways that feel supportive and useful.
Karen Smith brings 34 years of experience as a public school ELA teacher and literacy coach to her role on Turnitin's Teaching and Learning Innovations team. Since 2021, she has designed instructional resources and professional learning content that help educators worldwide implement Turnitin products effectively. Her extensive background in writing instruction ensures all her work is deeply rooted in pedagogy and academic integrity.
Not sure what to ask? Start here:
"How do I write an AI policy my students will actually read?"
"Should AI use be allowed on some assignments but not others?"
"How do we rebuild a culture of original thinking in an AI-saturated world"
#AskTurnitin Guidelines:
1. Be respectful: Treat all participants with kindness and professionalism.
2. Stay on topic: Questions should relate to AI detection, teaching strategies, and classroom experiences.
3. No product support requests: Technical or account issues should be directed to Turnitin Support
4. Avoid sensitive personal info: Do not share personally identifiable information about yourself, your institution, or students.
5. Engage constructively: Share insights, ask thoughtful questions, and build on others’ contributions.
21 replies
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#AskTurnitin Conversation Starters: When supervising projects and dissertations, what sort of indicators help you distinguish between meaningful AI-supported learning and superficial use of AI-generated outputs?
Onboarding Consultant at Turnitin and former researcher and lecturer, shares:
That is really the million-dollar question right now, isn’t it? Because as educators, our instinct shouldn't be to turn our supervision sessions into a hostile surveillance state. My own research focuses on how people resist surveillance, and I can tell you that if students feel we are just trying to 'catch' them, they will simply find more sophisticated ways to hide it.
Instead of playing cat-and-mouse, which I sort of escaped my Forensic Investigative background to try and avoid, really! I look for a few distinct indicators that separate meaningful learning from superficial generation.
First, I look at the paper trail of the project. Meaningful learning leaves a messy, human footprint. A student who is using AI properly as a sounding board can show me their evolution such as their early mind maps, their draft progression, different versions of the drafts, and how their thesis shifted from week three to week six. Superficial use, on the other hand, usually results in a 'sudden breakthrough.' A perfectly polished, cohesive chapter suddenly appears out of nowhere, with zero logical evolution from the previous week's conversation, and often not taking into account most feedback or comments that I have provided.
Second, I look for the illusion of synthesis. AI is incredibly good at summarizing information smoothly, but it tends to 'flatline' intellectually. If a student's literature review lists five different theories perfectly, but fails to critically evaluate how those theories conflict in a real-world context, that's a red flag. Human learning is often non-linear or messy; it has 'spikes' of unique, perhaps less-polished, but highly original critique. AI outputs are often polished but hollow.
Finally, the ultimate indicator is the live defense. In a one-on-one supervision, I’ll ask them to expand on a specific, nuanced paragraph. A student who used AI as a tool will confidently debate the concept with me because they’ve internalized the knowledge. A student who simply copy-pasted a superficial output will struggle to explain the text using their own vocabulary.
Ultimately, if we start assessing the process of their research, for instance, asking them to critically reflect on why they accepted or rejected an AI’s suggestion instead of just grading the final output/report/prototype or whatever it is that they are supposed to deliver, superficial AI use becomes impossible to hide, and meaningful learning naturally comes to light.
Educator at Los Gatos High School, California, shares:
The Clarity tools of the writing history and the AI detector are helpful indicators, especially so when used in conjunction with the pre-writing strategies I mentioned previously.
What indicators do you look for when distinguishing meaningful AI-supported learning from superficial use? Share your thoughts below!
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Welcome to AI with Integrity: Bringing Clarity to the Learning Process #AskTurnitin!
We’ve brought together a panel of educators to explore a question many of us are navigating today. This discussion features secondary educators in California who have firsthand experience with how AI is showing up in the classroom.
We invite you to watch the video above and share your insights and experiences on AI use in your classroom. Our panel will be here to answer your questions.
This is an open space for thoughtful discussion and shared learning, so we encourage you to join in!
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Wow, what a great discussion!
So grateful to , , and for jumping into a great conversation on responsible AI use and the modern challenges of learning and teaching in a digital landscape. I'm eager for others to watch our recording and let us know what they think about our range of topics.
Check it out and please leave your comments below!
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Hi all! I'm super excited to have been a part of this panel and engaging with two fantastic educators who are facing the use of AI daily! This was a fun conversation and I was a bit disappointed when our time was over. Hope all you viewers have a similar experience!
I can't wait to continue the conversation over the next two weeks! -
#AskTurnitin Conversation Starters: How has AI changed when and how you check student work?
Educator at Saratoga High School District, California, shares:
"As I mentioned in the panel discussion, I’ve stopped thinking about AI detection as something that happens at the “end” of the writing process. Instead, I check for AI use on the earliest possible writing steps, and try to hold one-on-one conversations with every student whose writing has been flagged by Clarity as concerning. Students have been much more receptive and honest when the stakes are low, and this approach has had positive impacts on getting them to do their own work."
Educator at Los Gatos High School, California, shares:
- Laptops closed for notes unless previously arranged with a student due to an accommodation.
Minimal laptop use during class. Specific expectations communicated for when they are used.
Typed assignments written in Clarity and/or uploaded to Canvas with a check for AI.
Handwritten notes checked regularly.
An increased attention to penmanship and spelling.
Increased amount of printed articles, documents etc, so that laptops stay closed more and work is completed on the article or paper/whiteboard/posters/etc.
More in-class individual writing and student interaction in pairs, small groups, and whole class.
We’d love to know: Are you shifting AI checks earlier in the writing process? What has that looked like in your classroom so far?
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Thank you for sharing this discussion. It was helpful!
I'd love to get perspective on this question both from Turnitin employees and from educators. When using Turnitin Clarity, do you find that students ever have privacy concerns about their writing process being recorded?
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#AskTurnitin Conversation Starters: When did you first realize AI was changing student work? What moment made it real for you?
Educator at Los Gatos High School, California, shares with us this answer:
When suddenly the time normally spent to grade essays quadrupled as I tried to figure out what was AI vs student generated. As I mentioned in our discussion, I was quite confused by some of the patterns I was seeing in many of the students’ writing. The issues were not what I would expect from a developmental standpoint nor based on the coursework I knew they had completed in previous grades. When I saw what LLMs were producing and compared it to some student work it suddenly made MUCH more sense. -
#AskTurnitin Conversation Starters: How are you redesigning tasks to make student thinking visible?
Teachers are innately adaptable. We adapt courses, assignments, instructions, feedback, deadlines, and expectations. In many ways, we are professional tailors within the garments of education: adjusting the thread, fabric, buttons, and seams so the learning experience fits the students in front of us.
When I think about making student thinking visible, I don’t think it always means adding more steps or creating more work. Often, it means slightly adjusting what we already ask students to do so we can better see the reasoning, decisions, questions, and revisions happening along the way.
As a former educator, I usually started from the end. If I wanted students to produce a literary analysis of a short story or poem, I first thought about what I needed to see before the final essay: how they were reading, what they noticed, where they were confused, what questions they asked, and how their interpretation changed. That shaped the scaffolding around the assignment.
Sometimes that meant literature circles with specific roles. Sometimes it meant short reflections after discussion. Sometimes it meant brainstorming, thesis work, rough drafts, quick check-ins, or discussion posts. The point wasn’t to create busywork. The point was to make the learning process visible enough that I could understand how students were getting to the final product.
By the time I read the final essay, I usually knew the student’s work already. I knew who had struggled through an idea, who had changed direction, who needed more support, and who had skipped parts of the process entirely. That visibility helped me respond to the learning, not just evaluate the product.
I think this matters when we talk about AI and assignment design. If students are allowed to use AI, or allowed to opt out, the core question may not be “How do I redesign everything?” It may be “Where can I ask students to show the thinking that is already happening?”For example, maybe AI is allowed during brainstorming, especially for students who process ideas through conversation. In that case, students might explain what they asked, what the tool helped them consider, what they rejected, and what they decided to do next. Maybe AI is used to explore possible sources, alongside a lesson on research literacy and evaluating credibility. Students who choose not to use AI can still document their search process, source decisions, and moments of uncertainty.
In both cases, the learning outcome stays at the center. Students are still practicing analysis, research, writing, revision, and reflection. The difference is that we are asking them to make their choices visible.
To me, “visible thinking” is not about surveillance or adding another sleeve to a jacket that already fits. It is about changing the thread a bit so the work students are already doing becomes easier to see, discuss, support, and assess with integrity.
I'm wondering if others in the community might have their own perspective? How are you redesigning tasks to make student thinking visible? -
This is not a question but rather an appreciative comment. I really appreciated being able to hear the insights of current educators who are actively using Turnitin products like Clarity. They bring a helpful, on-the-ground perspective about the benefits and challenges of handling AI usage. I'd love to see more discussions of this nature.
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#AskTurnitin Conversation Starters: Is AI supporting or shortcutting student learning in your experience so far?
Educator at Saratoga High School District, California, shares:Shortcutting. In many ways, the current battle against illicit AI use reminds me of the early years of Spark Notes. The more students relied on Spark Notes, the more they lost confidence in their own powers of literary analysis, and the more dependent they became on it. What Spark Notes did to reading, LLMs are doing to student writing.
Educator at Los Gatos High School, California, shares:
I think it is shortcutting student learning in ELA. I cannot speak to other subjects. Students are most often using it as a tool to replace thinking, reading, and writing. I am a firm believer in reading the actual text rather than a summary and in grappling with the writing process to find one’s own voice. I want students to think deeply about concepts and to talk and ask questions about the work amongst themselves and to make meaning. I even think that having it revise student work or ask them questions about it isn’t ideal, because I want the student to learn the skills themselves and discuss it with their peers.
Onboarding Consultant at Turnitin and former researcher and lecturer, shares:
With my background in AI and my experience in teaching, research and supervision, I see it as neither inherently supportive nor a shortcut, it depends on how it is integrated into learning and how students are approaching those tools. In my experience so far, AI can enhance critical thinking, feedback, and accessibility when used intentionally, but it can also encourage surface level engagement if students rely on it to replace rather than support their own thinking. The focus should be less on restricting AI and more on developing AI literacy and assessment approaches that promote meaningful learning.
For example, in my experience teaching MSc students across Big Data, Computer Science, Machine Learning, and AI and supervising their dissertation work, I have seen AI act as both a support mechanism and, at times, a shortcut depending on how students engage with it. When used appropriately, using the right codes (safely and with due diligence taking into account privacy concerns in oversharing information in GenAI prompts etc…), it can help them explore concepts faster, debug code, structure ideas, and receive formative feedback, which can deepen learning and improve confidence, especially with complex technical topics. This also reduces the workload that was traditionally on the soldiers of the instructor and/or supervisor.
However, many times, especially when supervising projects and marking theses, I have also observed that overreliance on AI can sometimes lead to weaker conceptual understanding, weaker critical arguments especially in literature review, or when addressing the research questions, particularly when students accept outputs without critically evaluating them or understanding the underlying methods. Maybe even resorting to outdated, irrelevant and even non-existing sources. This has reinforced for me that the challenge is not AI itself, but ensuring students develop critical AI literacy, reflective practice, and the ability to justify and evaluate the decisions behind their work. At MSc level especially, the emphasis should remain on higher order thinking rather than content generation alone.
We’d love to know: What evidence are you seeing that supports your view? Are there specific moments or assignments that shaped your thinking?