Last fact-check: May 25, 2026
The previous article in this curriculum walked through Dr. Elena Marquez redesigning her political science course for the AI era. The redesign worked. It also cost her about 150 additional hours a year of uncompensated work, and the piece flagged honestly that she could only absorb this because she has tenure.
This article is for everyone for whom that’s not true. Adjuncts teaching five sections at three institutions to make rent. Graduate student instructors working at the bottom of the academic labor market. Lecturers without tenure protection or institutional voice. Visiting professors with one-year contracts who’ll be somewhere else next fall. For these instructors, the redesign Elena ran isn’t an option, because the time it takes isn’t available.
This article walks through what redesign looks like at scale for an adjunct. The answers are partial. Some of the tradeoffs are bad. There are real things this approach cannot achieve that Elena’s approach could. I’m going to name those clearly, because pretending otherwise would do exactly what the institutional rollouts have done — present a solution that only works for the most protected workers and act as though it generalizes.
This article is part of Tygart Media’s free AI Literacy curriculum, which is at tygartmedia.com/category/ai-literacy. The pillar is here. The companion piece on Elena’s redesign is here.
The instructor and the situation
Marcus Chen teaches Introduction to Sociology at a community college and at a regional state university branch campus. He teaches five sections per term across both institutions. About 150 students per term, total. He has no tenure, no protected research time, no benefits at one of the two institutions, and his contracts are renewed semester by semester. He drives between two campuses three days a week. He earns roughly $32,000 a year and has student loans from his graduate program.
Marcus is also a thoughtful teacher. He cares about his students. He has noticed the same AI-shaped degradation in their work that Elena noticed in hers. He has read the same advice about course redesign and has had the same response: the people writing that advice do not understand the conditions under which he actually works.
The honest version of his situation: he has approximately fifteen extra hours per term to invest in redesign work, not 150. He cannot run individual oral defenses on 150 students’ essays. He cannot offer the office-hour density that Elena offers. He cannot revise his courses semester to semester because each semester he is teaching a slightly different course mix at slightly different institutions with slightly different LMS systems and slightly different academic calendars.
What can he actually do?
What he can’t do, named first
The piece on Elena’s redesign described a multi-stage essay process with five stages including individual oral defenses. That process, applied to 150 students, would take Marcus roughly 60 hours per essay round in oral defenses alone, before any of the rest of the work. He cannot do this. Anyone telling him to is not engaging with his actual situation.
The redesigned syllabus Elena spent 40 hours building over the summer was a piece of writing she had the time to think about deeply. Marcus does not have 40 contiguous hours of thinking time over a summer when he is teaching summer sessions at one or both of his institutions.
The “ask before you use” AI policy generated office-hour questions Elena could absorb because she had office hours. Marcus has the legally minimum office hours at each institution, often by appointment only, and his students at both schools are working students who can rarely make them.
Any solution that requires significantly more of Marcus’s time is not a solution he can implement. Saying so out loud is the first move.
The strategy underneath what he can do
Given that the labor-intensive approach isn’t available, Marcus has to think differently about what he’s trying to achieve. The honest re-framing: he’s not going to be able to verify, with high confidence, what AI use happens in his classes. He’s going to have to design the course such that AI use is mostly contained by the structure rather than by his attention.
This is a different goal than Elena’s. Elena was trying to ensure that grades reflect student learning, primarily through verification. Marcus is going to try to ensure that students do at least some learning regardless of what they do with AI, primarily through what gets built into the course itself.
The difference matters. Elena’s design assumes she’ll catch most AI-substituted work. Marcus’s design has to assume she won’t, and work around that. He has to lower his ambitions about verification and raise his ambitions about what the course does in the time the student is physically in front of him, where AI isn’t an option.
The reading question
The reading quiz problem is the same for Marcus as for Elena: multiple-choice quizzes are no longer enforcing reading because students can paste them into ChatGPT.
Elena’s solution was Hypothesis annotation. This works but it requires Marcus to read 150 students’ annotations weekly, which he doesn’t have time for. He needs an annotation-style solution that doesn’t require him to read everything students produce.
The version that’s available to him:
Annotation requirement, spot-checked rather than fully graded. Students annotate the reading using Hypothesis. They get full credit for completing three annotations per reading. Marcus skims a random sample each week — maybe 10-15 students out of 150 — and flags any that are clearly AI-generated or copy-pasted. Spot checking catches enough to deter the worst behavior without requiring him to read everything.
This is worse than Elena’s version. He’ll miss some students who annotate badly. Some students will figure out the spot-check pattern and stop trying. The verification is weaker. The redesign accepts these costs because the alternative — full grading of 150 weekly annotations — isn’t possible.
An alternative he could consider, depending on his preference: in-class reading reflection. Devote five minutes at the start of each class to a written response to a question about the reading. On paper. The questions are quick to grade because they’re short. They establish that the student showed up having engaged with the material. They cost Marcus class time, which is its own real cost — he loses ten minutes of lecture/discussion per week per section to this.
Neither option is what Elena’s option is. Both are honest about what’s available.
The essay question
Marcus typically assigns two essays per term in each section. Two essays × five sections × 150 total students = 300 essays per term to grade. The five-stage multi-stage process Elena ran is impossible at this scale.
What he can do, and the honest tradeoffs:
Option A: Drop the essays entirely. Replace with shorter, in-class writing exercises. This is the cleanest solution to the verification problem and the cheapest in grading time. It also significantly reduces the kind of sustained argumentative writing the course was supposed to teach. Students who graduate from his course will have done less of the work that develops writing-as-thinking. This is a real loss.
Option B: Keep one essay, kill the other. Replace the other essay with multiple shorter, in-class writing exercises. Run the remaining essay with a process-graded structure (topic proposal, source list, draft, final) without the oral defense Elena added. Catch some AI use through the staged process. Accept that some will slip through.
Option C: Keep both essays but use in-class drafting time. Devote one class meeting per essay to in-class drafting. Students draft a substantial portion of the essay during class time, on paper or on a closed-device basis. The portion drafted in class becomes verifiable; the portion completed at home is less so. The final essay incorporates both, and grading rewards the in-class portion proportionally.
The version Marcus actually chose, in this walkthrough, is Option B. He kept one essay, designed it as a process-graded sequence, and replaced the second essay with three short in-class writing exercises that happen during normal class time. This balances skill-building against grading time.
What he gave up: the second essay used to be his summative assessment of an argumentative writing skill. The three in-class exercises don’t fully replace that. Students completing his redesigned course will have written one full essay and three shorter pieces, instead of two full essays. They will have done less argumentative writing development. He accepts this as the cost of the redesign being possible at all.
The exam question
Marcus’s existing exams were a take-home midterm (essay format) and an in-class final (multiple-choice). Both are now compromised by AI in obvious ways.
His redesign here is closer to Elena’s: convert the take-home midterm to in-class, switch the final from multiple-choice to short answer. The total grading load goes up because short-answer grading is slower than multiple-choice grading.
The tradeoff he’s making: he’s adding maybe 10 hours of grading per term but recovering exam integrity. This is the redesign cost he can absorb. He couldn’t absorb adding 60 hours of oral defenses. He can absorb adding 10 hours of short-answer grading.
The AI policy
Marcus’s policy is shorter than Elena’s. Not because shorter is better — Elena’s policy is good — but because he doesn’t have time to maintain a longer one and doesn’t have office hour density to handle the questions a longer policy generates.
His policy:
AI Use Policy for This Course
You may use AI to help you understand readings, brainstorm ideas, and improve grammar in writing you have drafted yourself. You may not use AI to write any portion of submitted text, to generate citations or quotes, or to summarize readings instead of doing them.
If you used AI in any way on a graded assignment, briefly say so at the end of the assignment. Honest disclosure within the policy is not a problem. Undisclosed use is a violation of academic integrity.
If you’re unsure whether something is allowed, default to not doing it.
This policy may be revised during the term.
Notice what’s missing: the invitation to ask questions before doing something uncertain. Marcus doesn’t have office hour capacity to handle a volume of pre-clearance questions across 150 students. So instead of “ask before you do it,” his policy says “default to not doing it.” This is a worse policy than Elena’s in terms of helping students make good decisions. It’s the policy that fits his available time.
This is also the kind of compromise that needs to be named for what it is. Students at Marcus’s institutions get a less responsive AI environment than students at institutions where their professors have time to engage with their AI questions. The students didn’t choose this; Marcus didn’t choose this. It’s a consequence of how the institutions structure his labor.
What this redesign costs
Approximately 20 hours of design work over the summer. About 12-15 additional hours per term in grading short answers, in-class writing, and the staged essay. Some loss of class time to in-class writing exercises and reading reflections.
This is at the edge of what Marcus can afford. He’s accepting it because the alternative — pretending the previous course design still works — is worse for the students who do show up wanting to learn.
What this redesign achieves, and doesn’t
What it achieves:
- Exam integrity (no more take-home midterm)
- Some real writing development through the staged essay
- Some verifiable evidence of reading engagement
- An AI policy that students can follow
- Reduced grading time per student through the multiple-choice → short-answer tradeoff being offset by the dropped second essay
What it doesn’t:
- Confidence that any given student’s essay grade reflects their own work — the staged process catches some AI use but not all
- The level of individual student engagement Elena gets through office hours and oral defenses
- The class discussion quality that comes from professors who can read every annotation
- The same depth of writing skill development Elena’s students get
Marcus’s students will, on average, learn less than Elena’s students. This is not because Marcus is a worse teacher. It’s because he has been allocated less time, paid less money, and given less institutional support to do the same work. The redesign minimizes the gap. It cannot close it.
What this article cannot solve
I’m going to be direct about the limits of what’s above, because the prior article in this curriculum probably wasn’t direct enough about its own limits.
This article cannot solve the structural underfunding of adjunct labor. The most thoughtful possible course redesign by the most thoughtful possible adjunct still results in lower-quality teaching than well-supported tenured teaching, because teaching is a labor-intensive activity and adjuncts have less labor available to give it. No amount of clever design at the individual-course level fixes that. What fixes it is paying adjuncts more, giving them benefits, lengthening their contracts, reducing their teaching loads, and treating them as professionals rather than as units of contingent labor. That’s a labor and policy question this article cannot address from inside a single course.
This article cannot solve the dual-institution problem. Marcus teaching at two institutions with different LMS systems, different academic calendars, different policies, and different student populations is doing two different jobs that the redesign approach treats as one. In reality, he might need slightly different redesigns at each institution. The article has flattened this for clarity. The reader who’s actually in Marcus’s situation will have to do their own work to adapt.
This article cannot give you the institutional support Marcus doesn’t have. If your department has resources for adjunct course development, use them. If your institution offers compensation for course redesign, ask for it. If a faculty union represents adjuncts at your campus, that representation matters. None of this is in your individual power, but the article would be dishonest if it pretended individual cleverness substitutes for institutional support.
This article cannot tell you what to give up. The redesign requires choosing what’s most important and accepting that other things will be done less well. The piece walked through one specific set of choices. Yours might be different. The dropped second essay might be the wrong thing to drop in your course; the kept second essay might be unsustainable in mine. The article is showing you a way of thinking about the tradeoffs, not the answer to the tradeoffs.
What I’d want to hear from adjuncts reading this
One of the things this curriculum is trying to do is be the start of a conversation, not the end of one. There are real adjuncts working through real versions of this problem right now, and what they’re learning is more valuable than what this article can predict.
The things I’d want to know from anyone teaching at the contingent end of the academic labor market:
- What’s working in your courses that this article didn’t anticipate?
- What’s failing that I assumed would work?
- What’s the institutional support that does exist, even partially, that adjuncts can use?
- What unions or faculty governance bodies are taking this on seriously?
- How are your students responding to AI policies their other professors aren’t using?
- What’s the cost you’re paying that the redesign isn’t worth, that you wouldn’t recommend to anyone else?
The closing thought, which is also an invitation: this article is one attempt to walk through one redesign for one fictional adjunct. The real picture is going to require more of these walkthroughs, by more people in more situations, sharing what they actually did and what they actually learned. The Cal Poly syllabus repository is the model for what that looks like at scale. The curriculum this article is part of is one contribution. The contributions that matter most will come from instructors actually doing the work.
If you’re one of those instructors, your version of this article would be more useful than mine. Write it. Send it. Fork this and rewrite it. The curriculum is free to use, free to adapt, and built to grow. The literacy gap CSU created is too large to be closed by any single voice. It’s going to take a lot of people doing the work in their own situations and sharing what they learn.
This article doesn’t have the answer. It has a starting position. The answer is what gets built on top.
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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