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What is Practical Theory?
Practical Theory is the personal blog of a school-based educator, published under the tagline "A View From the Schoolhouse." It is a running commentary on schools as they actually operate day to day, written from inside a school rather than from a policy office or a research department. The current front page is dominated by a long essay on AI in schools, which sets the tone: practical problems, acknowledged trade-offs, and skepticism toward easy answers from either direction.
What it covers, and for whom
The blog's value is in the perspective rather than in tools or reviews. The featured piece argues that AI cheating is rising and that schools cannot "lesson plan" their way out of it by making every assignment irresistible to every student — an argument aimed at administrators and teachers who are being pushed toward either enthusiastic adoption or a return to blue-book exams. Alongside that sit posts on student safety and on understanding history when responding to current pressures.
That makes it most useful to:
- Teachers and school leaders weighing how to respond to AI use in their own buildings.
- Administrators who want a practitioner's counterweight to vendor-driven or think-tank-driven claims.
- Parents and school-community members trying to understand what the debate looks like from inside a school.
It is less useful if you want product comparisons, purchasing guidance or step-by-step classroom templates; the writing is essayistic and argument-driven.
How to read it
Treat individual posts as one educator's reasoned position, not as a consensus or a research summary — the strength is the lived detail, the limitation is the single viewpoint. A sensible next step: start with the AI essay and read it against a specific decision you face, such as your department's assessment policy, and note where its claims match or clash with what you see in your own classes. For contrast, Greg Toppo writes on education and technology, and the U.S. Department of Education publishes official guidance on AI in schools.
How does Practical Theory address AI cheating in schools?
Practical Theory treats AI cheating as a school-wide problem that cannot be lesson-planned away. In the post "AI and Showing Our Work," the author describes a clear rise in AI cheating reported by administrators and observed at SLA, and argues that schools must "own" how easy AI makes cheating rather than assume engaging lessons alone will prevent it. The post rejects two extremes: outsiders who claim AI will revolutionize school, and those who want to return to blue book exams to avoid judging authorship. It also notes that with 30 students in a class and compulsory subjects, no curriculum will appeal to every kid at every moment, so relying on motivation alone is unrealistic.
What the site actually argues
- Acknowledge the incentive: Generative AI is framed as the latest "easy button" in a world of immediate gratification, while good schools ask students to do hard, contemplative work.
- Don't blame teachers alone: Expecting teachers to out-design cheating for every student at every moment is called unrealistic.
- Find a third way: The post points to organizations and reports seeking a middle ground between revolution and blue book exams, while admitting that middle ground is difficult.
Practical next step for a school leader
Use the post as a discussion prompt with faculty: list where AI could substitute for student thinking in current assignments, then decide which tasks need in-class drafting, oral defense, or process evidence. Pair that with a clear disclosure policy so students know what "showing our work" means. For related reading, Greg Toppo is cited in the post as illustrating the split camps.
What is the middle-ground approach to AI in education that Practical Theory advocates?
Practical Theory argues for a third way between two extremes: tech-optimists who claim AI will "revolutionize" school, and traditionalists who want to retreat to blue-book exams because they don't want to bother figuring out whether a student wrote something. The blog's author, writing from inside a school (SLA), calls both camps unrealistic and unhelpful. The middle ground it advocates is honest rather than technocratic: admit plainly that AI makes cheating easy, accept that better lesson planning alone won't solve it, and still keep asking students to do hard, thoughtful work even when an "easy button" is always within reach.
What the middle ground looks like in practice
The argument rejects the idea that teachers can "lesson plan their way out" of AI cheating by making every assignment so engaging that no student would ever be tempted. With 30 students in a class and compulsory subjects, some kids will always like some assignments less than others. So the realistic approach combines several things:
- Name the problem openly instead of pretending AI cheating isn't happening or that it's purely a teaching failure.
- Design work that still demands contemplation and effort, while accepting that engagement reduces but never eliminates cheating.
- Stop expecting a single fix. The post points to organizations and reports trying to find this third way, and notes that the middle ground here is genuinely difficult to locate.
How this plays out for different readers
| Reader | What the middle-ground stance means for them |
|---|---|
| Teacher | Don't blame yourself for every AI-assisted submission, and don't abandon demanding work either; build in ways to see students' process. |
| Administrator | Acknowledge rising AI cheating honestly with staff and families rather than adopting either hype or a punitive back-to-basics posture. |
| Parent | Expect a school that takes AI misuse seriously without treating every student as a suspect. |
| Ed-tech advocate | Recognize that promised transformation doesn't cancel the threat, and schools need to discuss both. |
The post's own framing—headlined by "AI and Showing Our Work"—suggests the practical hinge is making students' process visible, so assessment rests on work shown rather than only on the final product. That is my reading of the emphasis, not a detailed program the post spells out.
A useful next step
If you're deciding how to apply this, start with one assignment and ask: could I tell how this was made? Redesigning a single task so the process is visible—drafts, notes, in-class thinking—tests the middle-ground idea without demanding a whole-school overhaul. For related perspectives on schooling and technology, see Greg Toppo and Science Leadership Academy.
How can teachers reduce AI cheating without designing perfect lessons for every student?
Teachers can reduce AI cheating by shifting assessment design and classroom culture rather than chasing a single perfect lesson. The Practical Theory essay argues that expecting teachers to craft lessons so engaging that no student ever wants to cheat is unrealistic: with 30 students and compulsory subjects, some assignments will always appeal more than others. The realistic path is to make cheating harder, make genuine work more visible, and address the incentives directly.
Practical moves
- Assess the process, not just the product. Require outlines, drafts, notes, or short in-class check-ins so you see how work develops.
- Use in-class writing for key skills. Short handwritten or offline responses reduce the "easy button" problem for core competencies.
- Ask for reflection tied to the student's own work. Students explain a choice, defend a claim, or connect an idea to class discussion; generic AI output often cannot do this convincingly.
- Change tasks to be specific to your class. Reference local texts, shared discussions, or a student's earlier draft so outside answers fit poorly.
- Talk about AI openly. The essay stresses that ignoring the threat creates harm; naming the temptation and the school's expectations makes the norm visible.
- Keep lessons interesting where you can, but don't treat engagement as the only defense. Interest helps; it is not a complete safeguard.
A concrete scenario
A history teacher assigning an essay can collect a thesis and two annotated sources in class, then have students write one paragraph under supervision and revise it at home. The final essay still allows deep work, but the teacher has evidence of the student's own thinking and a checkpoint where AI shortcuts are harder to hide.
Decision criterion
Choose the approach based on what you need to verify: if the skill is recall or core writing, assess it offline; if the skill is research or revision, require visible process artifacts. For broader context on school responses to AI, Practical Theory discusses the divide between "AI will transform everything" and "return to blue-book exams," and argues for a middle path that admits how easy cheating has become.
Why does Practical Theory argue that knowing education history matters when responding to AI?
Practical Theory argues that knowing education history matters because it keeps schools from treating AI as an unprecedented crisis with only two extreme responses. The post "Meeting the Moment by Knowing Our History" sits alongside the site's AI discussion, and the AI post itself frames the debate as split between people who expect AI to revolutionize school and people who want to retreat to the blue-book exam. The author, a school-based educator writing at Practical Theory, rejects both camps as unrealistic. History is the third path: it shows that schools have absorbed major technological and social shifts before, and that the recurring question is what work is worth doing and how we verify that students did it.
The practical use is for administrators and teachers deciding AI policy now. Rather than starting from the tool, start from what the school has always valued—thoughtful, contemplative work that asks students to do hard things—and ask which of those values AI threatens and which it could support. A reader facing a cheating surge could use the history lens to avoid overcorrecting: a return to handwritten exams solves verification but abandons the interesting curriculum the school built. The trade-off is speed versus depth; historical reflection slows a decision that feels urgent, but it prevents policy that swings between panic and hype.
A useful next step: before drafting an AI policy, write one page on how your school handled a previous disruptive technology—calculators, the internet, Wikipedia—and what it learned about assessment and trust. That becomes the baseline for judging AI proposals.
What practical steps does Practical Theory suggest for keeping students safe in the AI era?
Practical Theory's safety advice centers on awareness and conversation rather than a single technical fix. In "Keep the Kids Safe," the site treats AI safety as something schools must address openly: naming the risks of AI use, talking with students about them, and keeping the conversation going rather than leaving it to a one-off assembly or a policy document. The post's placement alongside "AI and Showing Our Work" reinforces that theme—students need to be honest about when and how they use AI, and adults need to be honest about the harms they are seeing.
What the site emphasizes in practice
- Name the problem directly. The writing is blunt that AI cheating is rising and that schools should not pretend otherwise. Acknowledging the problem is presented as the first step toward addressing it.
- Talk with students, not just about them. The safety framing is conversational: the goal is to keep an ongoing dialogue about AI's risks and uses, so students understand why boundaries exist rather than only encountering rules after a violation.
- Avoid the two extremes. The site rejects both "AI will revolutionize everything" enthusiasm and a retreat to blue-book exams. Neither camp, in its view, helps schools keep students safe or learning.
- Don't rely on lesson design alone. The argument is that no teacher can out-engage AI for every student in a class of 30 at every moment, so safety and integrity need to be built into how a school operates, not just into individual assignments.
A useful next step
If you are applying this in a school, treat the site's approach as a starting point for a staff discussion rather than a checklist. A concrete move: hold a short faculty meeting where teachers share what AI misuse looks like in their classrooms, then agree on two or three consistent practices—such as how to raise concerns with students and how to structure assignments so the thinking process is visible. Pair that with a student-facing conversation about the risks, which is the emphasis the site returns to. For broader context on school AI policy, U.S. Department of Education publishes guidance worth reviewing alongside this more practitioner-level perspective.
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