Beyond AI Cheating: Rethinking Learning for an AI-Integrated World
Teachers can create learning environments that prioritize process, critical thinking, and authentic engagement (4 full unit plans included)
Thanks for joining our growing community of educators exploring AI in learning! With 200-300 new subscribers each week, you're now part of an important conversation about how AI is transforming education at all levels.
Everything we share remains freely available, though paid subscriptions help support our work. Questions about anything I cover? Just DM me - always happy to chat!
Nick
This week, The Wall Street Journal published yet another article about AI and cheating in schools. Nearly three years into the widespread use of generative AI, the conversation remains largely unchanged: students are using AI to complete their assignments, and educators are left to figure out how to stop them.
I appreciate Stefan Bauschard’s response to this latest round of discourse: Let’s give students a break. They didn’t ask for this technology. None of us did. AI arrived fully formed, embedding itself into nearly every aspect of how we write, research, and problem-solve. It’s here, it’s not going away, and it’s time to move beyond the AI cheating narrative.
The impulse to crack down is understandable. There’s a lot at stake—academic integrity, critical thinking, the ability to engage deeply with ideas. But what if we stepped back for a moment and reimagined the problem? Instead of asking how to keep students from using AI, we might ask:
What happens if we mix things up a bit?
What happens if we take proactive steps to activate new learning pathways?
What happens if we shift the focus away from policing AI and toward rethinking what learning should look like in an AI-integrated world?
The goal of this piece is not to propose simple solutions or AI-proof assessments. Instead, it’s to explore a shift in pedagogy—one that moves away from static assessments of final products and toward a process-oriented approach that captures the evolution of student thinking.
The following sections outline what this shift could look like. First, I introduce a process-driven pedagogy, designed to make learning more iterative, reflective, and resistant to AI shortcuts. Then, I provide unit overviews across disciplines that demonstrate how these ideas might play out in real classrooms.
This is not about stopping AI use—it’s about building learning experiences where AI is a tool, not a shortcut. It’s about designing education in a way that prioritizes process, iteration, and deep engagement over polished final products.
Let’s take a closer look.
Process-Oriented Pedagogy: Rethinking Learning in an AI World
For decades, education has been built on the assumption that final products—essays, lab reports, problem sets—are the best measure of learning. AI challenges this model because it accelerates production but doesn’t necessarily replace the deeper cognitive work that goes into structuring ideas, testing hypotheses, or refining understanding.
Rather than seeing AI as a shortcut that students might misuse, we can reframe assessment to emphasize how knowledge is formed and applied. This means prioritizing:
Process over final product—capturing how students arrive at ideas, not just the end result.
Iteration over single submissions—allowing students to refine and revise work through structured feedback.
Real-time engagement—assessing learning through discussion, reasoning, and in-the-moment articulation.
Multimodal expression—expanding beyond text-based assignments to oral, visual, and collaborative demonstrations of understanding.
This following was created in collaboration with AI tools (GPT o3), reflecting my commitment to understanding both the potential and limitations of AI in writing instruction.
Key Components of a Process-Oriented Approach
Instead of replacing traditional assessments with AI-proof tasks, this methodology shifts the focus of learning toward how students engage with material, develop insights, and communicate ideas. Below are the four key components of this approach:
1️⃣ Process Documentation: Capturing Thinking in Motion
Rather than evaluating only the final output, students document their evolving understanding over time. This makes their thought process visible, tangible, and assessable.
Journals & Reflection Logs: Students write short daily or weekly reflections on their evolving understanding, noting moments of insight, confusion, or revision.
Think-Alouds & Video Reflections: Students verbally articulate their reasoning as they analyze texts, solve problems, or interpret data. These recordings provide a raw look at their in-the-moment thinking.
Iterative Drafts & Annotations: Instead of submitting a single final essay or report, students revise work based on feedback, keeping track of how their thinking evolves.
Peer Feedback & Self-Assessment: Students engage in structured peer reviews and self-reflection, explaining what changed between drafts and why.
📝 Example: Instead of a single history paper, students annotate primary sources, document how their perspectives shift across discussions, and submit a final synthesis alongside notes on how their thinking changed.
2️⃣ Live, In-Person Engagement: Thinking in Real Time
AI can generate responses, but it cannot replicate spontaneous out-of-box reasoning and interactive discussion. In a process-oriented classroom, real-time engagement becomes a critical part of assessment.
Oral Defenses & Socratic Seminars: Students present ideas and respond to live questioning, requiring them to demonstrate adaptability and depth of thought.
Debates & Role-Playing Exercises: Rather than writing static argumentative essays, students defend positions in a live setting, engaging in direct discourse.
Collaborative Problem-Solving: Students work in groups to tackle real-world challenges, explaining their reasoning and refining solutions through discussion.
🗣️ Example: In a math class, students develop a model for real-world data, then present their reasoning and answer live questions about their approach, forcing them to engage beyond pre-written responses.
3️⃣ Iterative Feedback & Revision Cycles: Learning as Refinement
Instead of treating assignments as one-time performances, this approach builds structured revision into the learning process.
Multiple Submission Points: Students turn in initial drafts, receive feedback, and refine their work before final submission.
Teacher & Peer Reviews: Feedback is integrated into the learning process, emphasizing revision as a skill, not just a requirement.
Metacognitive Reflections: Students explain why they made specific revisions and how their understanding evolved.
🔁 Example: Instead of a single science lab report, students conduct an experiment, document findings, present preliminary results for peer feedback, refine their approach, and submit a final report with an explanation of what changed and why.
4️⃣ Multimodal Expression: Expanding How We Show Understanding
AI makes it easier to produce text, but not all understanding needs to be communicated in written form. Expanding assessment formats ensures that students engage with content in authentic, dynamic ways.
Oral Presentations & Podcasts: Students articulate insights verbally, explaining key concepts to an audience.
Visual & Digital Storytelling: Infographics, video essays, or interactive digital timelines allow students to synthesize ideas in creative formats.
Applied Demonstrations & Simulations: Hands-on, experiential learning activities help reinforce concepts through real-world application.
🎨 Example: Instead of a written essay on The Great Gatsby, students create a digital storyboard tracing thematic development, supported by oral analysis.
Process-Driven Unit Overviews
Each of the following units is structured to prioritize process-based learning and real-time engagement, moving beyond assessments that can be easily automated or AI-generated. These units ensure that students are actively constructing knowledge, refining ideas, and demonstrating their learning in multiple ways over time.
📖 Literary Analysis: Meaning-Making Beyond the Essay
Click on title for full unit!!!
📌 Traditional Approach: Write a final analytical essay on The Great Gatsby.
📌 Process-Oriented Approach:
Phase 1: Deep Reading & Annotation
Students annotate passages with an evolving focus—first on imagery, then on character motivations, and later on thematic development.
Using color-coded marginal notes, they track changes in their interpretation across multiple readings.
Phase 2: Think-Aloud & Verbal Reasoning
Students record a 5-minute think-aloud responding to an analytical prompt (e.g., How does Fitzgerald use setting to reinforce themes of decay and illusion?).
They compare their first response with a later one, reflecting on what changed and why.
Phase 3: Synthesis & Argument Building
Students develop multiple thesis iterations over time, testing different angles of analysis.
Instead of committing to one argument early, they engage in structured peer critiques that push them to refine their position.
Phase 4: Creative Interpretation & Live Defense
Rather than writing a single essay, students choose to:
Construct a visual thematic map that traces the evolution of a motif across the novel.
Develop an alternate scene that maintains Fitzgerald’s tone but shifts the outcome for a key character.
Perform a live oral defense, where they present and justify their argument in response to real-time questioning.
📌 Assessment:
✅ Digital portfolio including annotated texts, recorded think-alouds, thesis iterations, and a final creative or oral defense component.
📜 History: Investigating the Complexity of the Past
Click on title for full unit!!!
📌 Traditional Approach: Write a research paper on the causes of the American Revolution.
📌 Process-Oriented Approach:
Phase 1: Primary Source Deep Dive
Students analyze firsthand accounts, political pamphlets, and personal letters from figures on all sides of the conflict (Patriots, Loyalists, Indigenous leaders, enslaved people, and British officials).
They develop source annotation journals, highlighting bias, perspective, and historical context.
Phase 2: Research & Perspective Mapping
Instead of a single "argument," students create a mind map of competing perspectives—mapping how different groups justified or resisted revolution.
Small teams take on different perspectives and present a “mock historical roundtable”, where they debate key turning points using only primary sources to support claims.
Phase 3: Interactive Data Storytelling
Students create a “Day in the Life” interactive narrative, following a real historical figure through a key event (e.g., the Boston Tea Party, the drafting of the Declaration of Independence).
This forces them to think about historical context beyond broad generalizations—how decisions were shaped by specific material conditions, fears, and alliances.
Phase 4: The Final Inquiry & Defense
Instead of writing a static research paper, students:
Build a dynamic timeline that presents key events through multiple perspectives.
Develop an annotated historical brief, justifying their stance on a historical counterfactual (e.g., If Britain had offered greater colonial representation, would revolution have been avoided?).
Defend their findings in a live Q&A session, where classmates challenge their conclusions using counter-evidence.
📌 Assessment:
✅ Digital portfolio including annotated primary sources, perspective mapping, research logs, an interactive narrative, and a live oral defense.
🔬 Science: Experimental Thinking as a Continuous Process
Click on title for full unit!!!
📌 Traditional Approach: Conduct an experiment and submit a one-time lab report.
📌 Process-Oriented Approach:
Phase 1: Hypothesis Development & Justification
Students do think-aloud recordings explaining how they arrived at their hypothesis.
They annotate past research to show how existing findings influenced their design.
Phase 2: Experimental Design with Iterative Testing
Students conduct an initial test run and analyze any flaws in their approach.
Instead of a single experiment, they revise methodology based on early results, documenting adjustments in an experimental journal.
Phase 3: Collaborative Data Analysis & Refinement
Groups compare initial findings with peers, looking for trends, errors, and sources of bias.
They develop data visualization narratives, using graphs and models to make sense of the results.
Phase 4: Public Presentation & Peer Review
Instead of submitting a static lab report, students:
Present their findings in a scientific poster session where peers challenge their conclusions.
Write a short “reflections on process” statement, explaining how their understanding evolved from hypothesis to final results.
Defend their results in a live Q&A format, responding to critiques from classmates and instructors.
📌 Assessment:
✅ Digital portfolio including hypothesis think-alouds, iterative experiment logs, peer-reviewed data visualizations, and a final oral defense.
🧮 Mathematics: Modeling & Real-World Application
Click on title for full unit!!!
📌 Traditional Approach: Solve textbook equations and submit a problem set.
📌 Process-Oriented Approach:
Phase 1: Real-World Problem Identification
Students identify a real-world phenomenon they want to model—such as population growth, climate trends, or economic shifts.
They brainstorm variables and discuss different types of functions (linear, quadratic, exponential, etc.) that might apply.
Phase 2: Data Collection & Model Building
Students collect or analyze real datasets.
They build initial models, testing which function best fits the data and justifying why.
Phase 3: Iterative Refinement & Peer Review
Groups swap projects and critique each other’s models, identifying flaws in assumptions, outliers, or alternative function fits.
Students refine their models and record think-aloud explanations of changes.
Phase 4: Application & Communication
Instead of submitting equations, students:
Create an infographic or data story, visually communicating the implications of their model.
Present findings in a live mathematical defense, responding to critiques about the validity of their approach.
📌 Assessment:
✅ Digital portfolio including raw data analysis, function modeling logs, a visual presentation of findings, and a recorded live defense.
Final Thoughts: A Measured Shift Toward Meaningful Learning
None of these approaches “AI-proof” learning—but that’s not the goal. Instead, they shift the focus away from easily automatable outputs and toward dynamic, iterative, and multimodal engagement.
By emphasizing process over product, we can:
✅ Move away from static, easily replicable assignments.
✅ Create richer, more engaging learning experiences.
✅ Ensure that students grapple with complexity rather than seeking shortcuts.
The challenge isn’t how to stop students from using AI—it’s how to design learning experiences where using AI isn’t a substitute for real engagement. This shift allows students to own their learning process, fostering habits of critical thinking, iteration, and authentic inquiry. 🚀
Nick Potkalitsky, Ph.D.
Check out some of our favorite Substacks:
Mike Kentz’s AI EduPathways: Insights from one of our most insightful, creative, and eloquent AI educators in the business!!!
Terry Underwood’s Learning to Read, Reading to Learn: The most penetrating investigation of the intersections between compositional theory, literacy studies, and AI on the internet!!!
Suzi’s When Life Gives You AI: An cutting-edge exploration of the intersection among computer science, neuroscience, and philosophy
Alejandro Piad Morffis’s Mostly Harmless Ideas: Unmatched investigations into coding, machine learning, computational theory, and practical AI applications
Michael Woudenberg’s Polymathic Being: Polymathic wisdom brought to you every Sunday morning with your first cup of coffee
Rob Nelson’s AI Log: Incredibly deep and insightful essay about AI’s impact on higher ed, society, and culture.
Michael Spencer’s AI Supremacy: The most comprehensive and current analysis of AI news and trends, featuring numerous intriguing guest posts
Daniel Bashir’s The Gradient Podcast: The top interviews with leading AI experts, researchers, developers, and linguists.
Daniel Nest’s Why Try AI?: The most amazing updates on AI tools and techniques
Riccardo Vocca’s The Intelligent Friend: An intriguing examination of the diverse ways AI is transforming our lives and the world around us.
Jason Gulya’s The AI Edventure: An important exploration of cutting edge innovations in AI-responsive curriculum and pedagogy.
Thanks Alasdair. Your support is very encouraging!!!
This is absolutely brilliant as always from you. Your encapsulation of how AI can challenge the way of learning fills me with optimism. “For decades, education has been built on the assumption that final products—essays, lab reports, problem sets—are the best measure of learning. AI challenges this model because it accelerates production but doesn’t necessarily replace the deeper cognitive work that goes into structuring ideas, testing hypotheses, or refining understanding.” Thank you.