Last fact-check: May 25, 2026
Most advice on teaching with AI is given at the altitude of “consider rethinking your assignments.” This article goes the other direction. We’re going to follow one professor through the actual redesign of one specific course — every assignment reconsidered, every assessment rethought, every syllabus line debated. The professor and the course are composites, drawn from patterns the CalMatters and CSU reporting surface across the system. The decisions are real. So are the tradeoffs.
This is part of Tygart Media’s free AI Literacy curriculum. The pillar is here. The full library is at tygartmedia.com/category/ai-literacy, free to fork, adapt, or use in any classroom.
The professor and the course
Dr. Elena Marquez is a tenured associate professor of political science at a mid-sized regional state university. She’s been teaching PS 215: Introduction to American Government for eleven years. Three sections per term, around 35 students per section, mostly second-year undergraduates fulfilling a general education requirement. The course meets twice a week for 75 minutes.
The previous version of her course was straightforward. A textbook. Weekly reading quizzes. Three essays (4-6 pages each) on different aspects of American institutions. A midterm exam, take-home, essay-style. A final exam, in-class, mix of multiple choice and short answer. Class participation. The grade distribution had been stable for years.
In the fall of 2024, Elena noticed something change. The essays were getting better in a specific way — more polished, more confident in tone, more even in quality — and simultaneously emptier. Students who had struggled in class discussion produced clean papers. Students who had been writing rough but original work were now turning in something that read more professionally than their in-class contributions could justify. By the end of that semester, she was certain some students were using AI. By spring 2025, she was certain most of them were.
She’s now redesigning the course for the next academic year. Her university bought ChatGPT Edu. No required student training. No required faculty training. No model syllabus statement. Her department chair has said, in writing, that “individual faculty determine appropriate AI policies for their courses.” Elena is on her own.
This article walks through her redesign decisions in roughly the order she makes them.
Decision 1: What does the course actually teach?
Elena’s first instinct, after the rough fall semester, was to ban AI. She wrote a syllabus statement that did. Then she thought about it longer and rejected her own first draft.
The question she made herself answer first, before any policy decision, was: what is this course actually supposed to teach? Not what it covered. What the students were supposed to be able to do afterward that they couldn’t do before.
Her honest answer, after some thinking:
- Understand the structure of American government well enough to read news critically
- Be able to make and defend an argument about a political question using evidence
- Be able to evaluate sources for credibility and bias
- Be able to read a primary document and explain what it says
- Understand how political institutions actually work, not just how they’re described
The list of learning objectives in her current syllabus said something like that, but in more passive language. The reframed list, written as actual capabilities, changed how she thought about the rest of the redesign. Each assessment in the course either built one of these capabilities or it didn’t. If an assessment could be passed by submitting AI output, it wasn’t building any of them.
This was the move that mattered most. Not the AI policy. The clarity about what AI use would have to leave intact.
Decision 2: The reading quizzes
The weekly reading quizzes had been multiple-choice, taken online through the learning management system, with the questions drawn from a publisher’s test bank that came with the textbook. They were designed to enforce reading. They had worked for years.
They no longer worked. ChatGPT could answer any question from any major American government textbook reliably. The quizzes were no longer enforcing reading — they were enforcing students’ willingness to copy a question into a chatbot.
Elena’s options:
Option A: Drop the quizzes entirely. Honest about what they had become. Replaces nothing with nothing.
Option B: Move them to in-class, on paper. Restores the integrity of the assessment but consumes ten minutes of every class meeting, which is real time she’d have to take from somewhere.
Option C: Replace them with reading reflections. Short written responses to a question about the reading, due before class. Possible to AI-generate, but the question can be structured to make AI output obvious.
Option D: Replace them with annotation. Students annotate the reading itself using a tool like Hypothesis. Annotations are visible to the class. Hard to AI-generate convincingly because the value is in showing your specific reaction to specific passages.
Elena went with a hybrid. Drop the multiple-choice quizzes. Add Hypothesis annotation as a low-stakes graded activity — at least three substantive annotations per reading, due before class. Use the first five minutes of class to discuss a couple of annotations that came up, which both rewards the work and makes it part of the class fabric.
The trade: she gives up the easy automated grading the quizzes provided. She gets back actual evidence of student engagement with the reading, plus a class discussion starter she didn’t have before.
Decision 3: The essays
The essays were the assignments that broke first. Three essays, take-home, 4-6 pages each. Each one on a topic from a list she provided. Submitted as Word documents through the learning management system.
Elena considered keeping them and adding heavy disclosure requirements. She considered replacing them with in-class essays. She considered replacing them with oral presentations. She considered keeping the essays but making them collaborative and process-graded.
The option she settled on was the most labor-intensive but the one that protected the most of what the essays were originally for. Three essays, but each one structured as a multi-stage assignment:
Stage 1: Topic proposal. 150-200 words, submitted in class, on paper, written by hand. Each student writes which of the essay topics they’re choosing, what they think they’re going to argue, and what one piece of evidence they think they’ll use. This stage establishes that the student has personally chosen the direction.
Stage 2: Source list. Submitted electronically a week later. Each student lists 4-6 sources they intend to use, with one sentence on what each source contributes. Elena reviews the lists and flags any sources that don’t exist or don’t say what the student claims they say. This is the verification step. Students who pasted in AI-generated source lists get caught here and have to redo.
Stage 3: Outline. Submitted electronically two weeks later. Detailed enough to show the argument’s structure. Elena gives feedback on the outline, but the feedback is brief. The real grading happens in stage 4.
Stage 4: Draft. Full 4-6 pages, submitted electronically.
Stage 5: Defense. A 5-minute in-class oral conversation with Elena about the essay. Not a presentation. A conversation. What’s your argument? Why this evidence? What counter-argument did you consider? Why did you reject it? Students who wrote the essay themselves can answer easily. Students who didn’t, can’t.
The stage 5 defense was the move Elena agonized over the most. It’s a major time commitment — 35 students × 5 minutes × 3 essays = roughly 9 hours per essay round, per section, three sections, three essays. About 80 hours per term in oral defenses alone.
She accepted the cost. The reason: in her testing, the oral defense was the only thing that reliably distinguished student-written essays from AI-assisted essays from AI-written essays. It was also, she realized, where most of the actual learning verification was happening. The conversation revealed what the student understood. The essay was now mostly evidence that they did the work. The defense was where she found out if they learned anything.
The trade: she’s now spending 80 hours per term on oral defenses that didn’t exist before. She gets back something close to certainty that the essay grade reflects student work. She also gets back a much sharper sense of who’s actually learning the material — which is what her job is supposed to be.
Decision 4: The AI policy
With the assessments redesigned, the AI policy almost wrote itself. Elena was now in a position where AI use was not particularly threatening to most of her assessments — the multi-stage essay design made AI-only work hard, the oral defense caught what slipped through, the annotation assignment was AI-resistant by design.
Her syllabus statement, after several revisions:
AI Policy for PS 215
You may use AI tools (ChatGPT, Claude, Gemini, etc.) in this course for the following purposes:
- Explaining concepts you’re trying to understand
- Suggesting counter-arguments to a position you’re developing
- Improving grammar or sentence structure in writing you have drafted yourself
- Helping you find sources to read (which you must then locate, read, and evaluate yourself)
You may not use AI tools to:
- Draft any portion of text you submit
- Generate citations, quotes, or other specific factual claims
- Summarize readings in place of reading them
- Develop your thesis or central argument for any essay
- Complete annotations or reflection responses
Any AI use in a graded assignment must be disclosed in a brief note at the end of the assignment, stating which tools you used and what you used them for. Failure to disclose AI use that has occurred is a violation of academic integrity. Honest disclosure of AI use within the permitted boundaries is not.
If you are uncertain whether a particular use is permitted, ask before doing it. I would rather have the question than have to figure out the answer in an academic integrity hearing.
This policy may evolve as I learn more about how AI is being used. I will communicate any changes in writing.
The thing Elena debated longest was the closing line — the acknowledgment that the policy might change. She’d been told by colleagues to project certainty. She decided to project honesty instead. Students respond to teachers who admit they’re figuring this out. They lose respect for teachers who pretend they have it all worked out and obviously don’t.
Decision 5: The midterm
The take-home essay midterm was the assessment Elena was least attached to. It had been a convenience, not a pedagogical decision. The take-home format was a relic of when she had been overloaded with grading and wanted to spread out the work.
She replaced it with an in-class midterm. 75 minutes. Open-note, but closed-device. Students bring handwritten notes (typed notes from a laptop don’t count — too easy to have an AI-assistant on a second device). Three short essay questions, drawn from a list of seven possible questions she releases two weeks in advance.
The handwritten-notes requirement is the move that made this work. Students who actually studied have notes. Students who tried to game the system with AI-generated notes either don’t have them, or have notes too generic to actually help them under time pressure. The closed-device policy means no real-time AI assistance during the exam.
The trade: in-class exams take class time and are more stressful for students. Elena considered this seriously — she has students with documented anxiety, students with accessibility needs, students whose first language isn’t English. The accommodations she works out individually with those students are the same kinds of accommodations she’s always made. The general format is more demanding than the take-home was. That’s the point.
Decision 6: The final
The final exam was already in-class and was already working. She kept it largely as-is, with one change: she replaced the multiple-choice section with short-answer questions. The multiple-choice section had been a concession to grading speed. With AI now able to produce passable multiple-choice answers for any topic, the format no longer measured what it used to measure even when administered in person — students could memorize AI-summarized study guides without engaging with the material.
The short-answer section is harder to grade. Elena adds about three hours of grading per section. She thinks the assessment is now actually measuring something useful, which she had stopped being sure was true of the multiple-choice version.
Decision 7: Participation
The participation grade had always been the squishiest part of the course. Elena’s gut sense, basically, of whether students were engaging. She kept it, but added structure. Students who participate verbally during class get full participation credit. Students who don’t (for any reason) can earn equivalent credit through office hour visits, written reflections submitted outside class, or contributions to the class discussion forum.
The structure matters because of who’s silent in class. Some students are silent because they’re not doing the reading. Some are silent because of anxiety. Some are silent because their cultural background didn’t prepare them for American-style classroom participation. Some are silent because they’re non-native English speakers. A participation grade that just rewards talking penalizes the second, third, and fourth groups for problems that have nothing to do with engagement.
This is not strictly an AI-driven change, but it’s part of the redesign because Elena was rethinking the whole grading scheme. The new participation structure is also harder to fake with AI, since the alternative paths involve specific written work or actual conversation with her in office hours.
Decision 8: What to tell students on day one
The first class meeting is where the redesigned course either lands or doesn’t. Elena spends most of her preparation time on the first-day script.
What she says, in approximate terms:
“You probably use AI. I’m not going to pretend you don’t. I’m going to be honest about what this course is doing, and I’m going to ask you to be honest about how you use AI in it.
“This course is going to teach you to understand American government well enough to read news critically. To make and defend arguments using evidence. To read sources carefully and evaluate them. The work of the course is designed so that AI can help you with parts of it, but AI cannot do the central work for you and have you still get what you came here for.
“My job is to design the work so that you have to do the thinking. Your job is to actually do the thinking. The AI policy is on the syllabus and I’ll walk through it. I am not trying to catch you. I am trying to make sure that what you take with you from this course is real.
“If you have questions about any of this, ask. If you’re not sure whether a particular use of AI is okay, ask before you do it. I would rather answer a hundred questions at the start of the term than have a hundred academic integrity hearings at the end.”
Elena rehearses this. The reason: tone matters. The script delivered as a threat lands as a threat. The same words delivered with the assumption that students are going to take their education seriously land differently. She’s teaching to the median student. The median student wants to do the work but has been given no useful guidance about how to do it in the presence of AI. The script is the guidance.
What the redesign cost
Honest accounting. The redesign cost Elena approximately:
- 40 hours of design work over the summer
- 80 additional hours per term in oral defenses
- 15 additional hours per term in grading short-answer exam sections
- 10 additional hours per term in policy questions during office hours, at least in the first term
- An unmeasurable cost in cognitive load — every assignment requires more decisions now than it used to
Total: maybe 150 additional hours per academic year. About four weeks of full-time work, spread across the year.
Elena is tenured. She can absorb this. A junior faculty member without tenure protection, an adjunct teaching five sections to make rent, a graduate student instructor — none of them can absorb 150 hours per year of redesign work for free. The fact that Elena’s redesign is possible is partly a function of her institutional position. This is a real limitation of the approach. Institutions that want all their faculty to do this kind of work need to compensate for the time it takes, or only the most protected faculty will do it.
What the redesign gained
Honest accounting on the other side. The redesign produced:
- Assessments that actually measure what they claim to measure
- Confidence that grades reflect student learning, not AI capability
- A defensible AI policy that students can follow
- A clear understanding, by Elena, of each student’s actual progress, because the oral defenses are diagnostic in a way that essays weren’t
- An institutional artifact — the redesigned syllabus — that her department can use as a model for other courses
- Class discussions that are sharper because students have engaged with the reading rather than skimmed AI summaries
- Lower academic integrity caseload, because the work is designed so that cheating is harder than doing it
The last point matters most. Elena now spends almost no time on academic integrity disputes. Not because nobody tries to cheat. Because the design of the course makes cheating mostly unprofitable — the oral defense will catch a fake essay, the in-class midterm catches AI-assisted preparation, the annotation work catches students who didn’t read. By making cheating expensive, the redesign reduces the actual incidence.
What didn’t work and got changed mid-term
One honest note: the first time Elena ran the redesigned course, several things didn’t work as designed.
The Hypothesis annotation tool had a learning curve. Students struggled with it for the first three weeks. She added a short tutorial in week one and lowered the early-term annotation requirements.
The oral defenses ran longer than planned in the first round. Students were nervous, conversations stretched. By the third round, both Elena and the students were more efficient.
The “ask before you use” policy generated a high volume of office hour questions in the first weeks. She started compiling answers to common questions and posting them on the course site, which reduced the volume by midterm.
Some students used AI in ways the policy didn’t explicitly cover and that turned out to be fine. She updated the policy mid-semester to reflect the new examples. Students appreciated this. So did the chair, who used the updated policy as a model when other faculty asked for help.
The course evolved during the term. That’s the honest version. The redesigned syllabus is not a static document. It’s the starting position for an ongoing process.
The lesson, for any professor reading this
The redesign Elena ran is not the only possible response to AI in the classroom. It’s one defensible answer for one specific course taught by one specific professor with one specific tenure status. Your version will be different. The framework underneath, though, generalizes:
Start with what the course is actually supposed to teach. Be honest about which assessments still measure that. Redesign the ones that don’t. Build in stages, oral components, in-class work, or process grading wherever possible. Write a specific AI policy. Tell students on day one. Adjust as you learn.
The 200-policy syllabus repository at Cal Poly is full of professors doing some version of this work in isolation. They’re doing it well. They’re doing it without compensation, without institutional support, without time, and mostly without colleagues to talk it through with. The CSU faculty union testimony at the August 2025 hearing was right about this part: the work is real, it’s necessary, and the institutional structure is not yet supporting the people doing it.
This walkthrough exists in the open so that the next professor doesn’t have to reinvent it from scratch. If you take any of it, fork it, adapt it, or use it as a starting point for your own redesign, that’s exactly what it’s for.
About this knowledge node: This is a cluster article in Tygart Media’s AI Literacy content sprint. It’s licensed for use in any classroom, training program, custom GPT, or Claude Project as long as attribution is maintained. The pillar article that introduces the sprint is here.
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