Here is an actual log of a still-unfinished conversation with Claude. I began with a loose problem rather than a settled assignment. I wanted students to use AI throughout the course, but I also wanted work that remained intellectually demanding after AI entered the picture. (The AI-Native course framework). I wanted an authentic final product, a manageable teaching load, and a clear connection between broad readings and sustained practice. The log shows the process in its untidy form. Ideas appear, disappear, return in altered form, and sometimes collapse under practical pressure. It shows what AI-assisted design looks like before the polished version hides the uncertainty.
The first role is adviser. At this stage, AI should help us see the design space. It can generate different assignment types, identify tensions, connect ideas to known methods, and point out options we have not considered. The purpose is not to draft the course. The purpose is to improve the quality of the decision that will guide the course.
My early conversation moved through several possible projects. Students might reconstruct disagreements, keep a calibration journal, examine real decisions, or study their own use of AI. Each possibility highlighted a different meaning of critical thinking. One emphasized argument. Another emphasized prediction. Another emphasized judgment. AI was useful because it could hold these options next to one another and compare their strengths.
This is where many course-design conversations go wrong. We ask AI for “the best assignment,” and it provides one. The answer may sound convincing because AI is good at giving coherence to whatever it has just proposed. But coherence is not the same as fit.
In adviser mode, the instructor should resist early closure. Ask for several genuinely different approaches. Ask which educational goals are in tension. Ask what each option teaches well and what it neglects. Ask how the assignment would change for beginners, advanced students, large classes, or limited class time.
The rule is to use AI to widen judgment, not replace it.
Adviser mode must also end. AI can brainstorm forever. More possibilities do not always lead to a better course. At some point, the instructor has to choose a central project and state which constraints are fixed.
That decision begins developer mode.
The developer does not keep reopening the main question. It takes the chosen direction and turns it into a teachable structure. It identifies stages, dependencies, supports, deadlines, common materials, and likely points of failure. It asks what students must know before they can complete each step. It also asks what the instructor can realistically support.
In my case, the central idea became a first-person study of AI use. Students would document selected AI sessions, record their expectations, use think-aloud methods, and analyze patterns in their own judgment. Their final product would be a chapter in a class-edited digital volume.
Once that direction was fixed, new questions became possible. How many observations would students need? Which parts of the method should be common across the class? How could students produce distinct chapters if everyone followed the same protocol? What should be submitted before the final paper? How could AI assist with coding without taking over the analysis?
These were development questions, not brainstorming questions.
AI helped convert the broad idea into a sequence: proposal, pilot, sample analysis, draft, and final chapter. It also helped reveal that variation should come from the research question rather than from the method. A common protocol would make demonstrations, peer support, and grading more manageable. Different questions would prevent the final volume from becoming repetitive.
The wider rule is to standardize what reduces confusion and vary what creates intellectual ownership.
Developer mode should produce an artifact. It might be an assignment sheet, course map, rubric, schedule, or draft syllabus. Without a concrete artifact, the conversation remains too easy. General ideas can appear strong because they have not yet encountered dates, class size, student preparation, or instructor workload.
This is also the stage where AI should be asked to simplify. Course design often becomes more elaborate during development because every useful idea invites another useful idea. A promising assignment can quietly turn into a research program, a publication project, a new assessment system, and an administrative burden.
Ask what can be removed without damaging the main learning goal. Ask which feature creates the most work for the least educational value. Ask for the smallest complete version of the design. AI is not naturally restrained. It needs to be assigned restraint as part of the job.
Critic mode should begin only after the design is visible.
The critic should not continue helping the instructor elaborate the plan. It should evaluate the plan as if it came from someone else. This separation is important because AI tends to preserve conversational momentum. After helping build an idea, it often continues to explain why the idea is sound.
I had to change the instruction directly: stop developing the project and look for serious weaknesses.
That shift exposed several problems. Students could produce polished reflections without reliable evidence. AI-assisted analysis could smooth over uncertainty instead of revealing it. A shared method could lead to repetitive papers. An ambitious research component could create ethical and administrative work that the course did not need. A flexible reading schedule could disadvantage students with weak time-management skills.
These criticisms were useful because they arrived after the idea had enough structure to be tested.
The rule is to request diagnosis before revision. Ask for the strongest objections without asking AI to fix them immediately. Some objections will reveal real flaws. Others will reflect tradeoffs you are willing to accept. The instructor still has to decide which criticisms matter.
Useful questions include: Where does this plan depend on unrealistic student behavior? What could students do that technically satisfies the assignment while defeating its purpose? Which part will be hardest to explain? What will be hardest to grade? What will fail first in a class of forty? Which feature exists mainly because it sounds impressive?
Critic mode should also test the course against its formal obligations. Does the project assess the stated learning outcomes? Do the readings and activities prepare students for the graded work? Does the grading structure reward the kind of thinking the course claims to value? Does the schedule leave time for revision? Can the instructor explain the design to students in plain language?
The three roles should not be combined in a single prompt. “Brainstorm, build, and critique a course” sounds efficient, but it weakens all three tasks. Brainstorming needs openness. Development needs commitment. Criticism needs distance.
The course remains the instructor’s intellectual responsibility. AI can broaden the options, give structure to a decision, and expose weaknesses in a draft. It cannot decide what is worth teaching, what risks are acceptable, or how much complexity a particular instructor and group of students can carry.
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