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.
40 replies
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#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?
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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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#AskTurnitin Conversation Starters: What does “responsible AI use” actually look like in your classroom or lecture hall?
In my nearly two decades of teaching secondary literacy, the persistent push to “use more technology in the classroom” grew in urgency each year. Whatever the shape of the latest technological advance, the fears were the same: Will it become a crutch? Will students be able to learn – and demonstrate their learning – without it?
The concerns surrounding the responsible use of AI in an educational setting are not that different. With the lines between original and AI-assisted work becoming blurred, educators have valid concerns over how to determine (and prove) that a student’s work is authentic. The concept of responsible AI use doesn't have to be centered on policing shortcuts or banning bots. Like any new technology, teachers can model how to use these tools ethically to cultivate the mindset that AI is a thinking partner, not a thinking replacement.
In an inclusive literacy classroom, where gifted scholars share tables with students building foundational reading skills, responsible AI can act as a leveling force for equitable access.
- Advanced students can use LLMs to generate complex counter-arguments in order to push their thesis statements or refutations further, addressing their personalized goals for academic enrichment.
- Developing language learners can utilize AI to adjust text complexities when reading, or use voice-to-text AI in their first language to outline their thoughts, translate the framework, and suggest syntactical refinements in the developing language.
By shifting the cognitive load from the mechanics of decoding and transcription to the critical thinking required for comprehension and analysis, AI can remove barriers to entry, transforming differentiation from a teacher’s exhaustive logistical hurdle into a student-driven reality.
Is there still a need for reliable methods of determining work that was generated solely by AI tools? Absolutely.
Thankfully, some of the tools used by educators to detect AI use also come equipped with pedagogically-driven guidance that empowers students to refine their writing strategically and authentically (have you seen Turnitin Clarity?).
Treating AI as an equitable thinking partner rather than a shortcut can allow educators to bridge the gap between diverse learning needs. When supported by tools that encourage growth rather than just policing compliance, technology ceases to be a threat to academic integrity and instead becomes a powerful catalyst for student-driven, differentiated success.
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#AskTurnitin Conversation Starter: What is gained when students use AI well?
Have you seen moments where using AI helped students build skills they will need when they enter the workforce? What did that look like in your classroom?
When used intentionally, instead of replacing student thinking, AI can enhance learning and help students build the essential skills the modern workforce requires.
Integrating AI into the writing process encourages critical engagement. Instead of staring at a blank page, students can use AI to generate a starting point and then focus their energy on critiquing and refining those ideas. For multilingual learners, it’s a powerful scaffold that lets them prioritize deep concepts over grammar mechanics. Plus, learning to prompt and verify outputs builds the evaluative judgment needed in every professional role today.
Professional competence now means knowing how to delegate to automated systems while staying 100% accountable for the result. The real skill is the discernment to know when to trust AI and when to override it. Students who master this balance enter the job market with a massive competitive edge.
As educators, our shift is moving from monitoring for AI use to teaching students how to use it effectively. By creating transparent environments for AI use, we can see the trajectory of a student’s work and offer coaching where they need it most. This turns learning integrity from a "gotcha" moment into a teachable career skill.
Our goal is to prepare students who can collaborate with technology while never losing their own voice or judgment. That balance between algorithmic help and human oversight is the future of work.
How are you using AI to enhance career-readiness in your classroom?
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#AskTurnitin Conversation Starter: How are students responding to clearer boundaries around AI use?
Educator at Los Gatos High School, California, shares:
Students are grappling with the influx of AI in our society and I think are appreciative of candid conversations about it. Clearer boundaries have been beneficial in all cases, although some still do choose to push back and/or find workarounds.
We'd love to know: Do they push back, engage more thoughtfully, or find workarounds? Let's discuss!