Designing a College Course with AI

Course design with AI works best when the conversation changes direction three times. First, AI serves as an adviser. Then it becomes a developer. Finally, it acts as a critic. These roles should be used in sequence and kept separate. When they blur together, AI tends to produce attractive ideas, expand them too quickly, and defend the result before anyone has tested whether it will work.

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.

AI Summaries Are Not Killing Reading. They Reveal What Reading Already Is

Naomi S. Baron, a linguist who studies technology and literacy, worries that AI is making books feel obsolete. Her concern is easy to understand. When a chatbot can reduce a book to ten points, students may decide that reading hundreds of pages is an inefficient way to collect information. Baron argues that AI may weaken our motivation to read for analysis, empathy, escape, and pleasure. In her view, letting a machine read for us risks weakening the habits that reading develops.

This argument identifies a real danger, but it also treats AI summarization as more novel than it is. Education has always placed summaries between readers and difficult texts.

A scholarly introduction to a classic novel tells us what historical background matters, identifies the main themes, explains difficult references, and suggests how the work should be interpreted. A professor does much the same in a lecture. Few lecturers read an entire article aloud. They select its central claims, translate technical terms into familiar language, supply examples, and omit details that do not serve the course. Textbooks summarize fields. Literature reviews summarize studies. Review essays summarize books. Even a table of contents is a small prediction about what deserves attention.

We do not usually accuse the professor who explains Kant in ordinary language of destroying philosophy. We do not complain that an introduction to The Republic has prevented us from reading Plato. We understand these forms as intellectual support. They reduce the cost of entry.

AI summaries differ in speed, scale, and uncertain accuracy, but not in their basic educational function. They compress and translate. They can provide a map before we enter unfamiliar territory. I have argued elsewhere that educators should not hide from students that AI can be a powerful tutor. A tutor who summarizes a difficult chapter is not necessarily replacing reading. The tutor may be making reading possible.

The deeper problem with the claim that summaries replace reading is that much nonfiction reading is itself a form of summarization. We rarely read a policy report, history book, or research article in order to memorize every sentence. We read to construct a smaller mental representation that we can retain and use.

Walter Kintsch and Teun van Dijk described text comprehension as movement between local propositions and a global “macrostructure.” Their model includes mental operations that reduce the information in a text to its gist. These operations are shaped by the reader’s goals. In plain language, understanding a text requires deciding what matters, combining related points, and discarding much of the rest.

Memory research supports this view. Jacqueline Sachs found that people quickly became less able to recognize changes in the exact wording and syntax of passages, even when they retained their meaning. Fuzzy-trace theory makes a similar distinction between memory for exact form and memory for gist. Adults often rely on the simplest level of meaning that is sufficient for the task. We preserve the substance while losing many of the words that carried it.

The summary, then, does not arrive after reading. It is one of the products of reading.

Human readers are compressors with uneven settings. One reader sees a central argument where another sees an interesting detail. Prior knowledge changes what we notice. Goals change what we retain. Fatigue changes everything. Human summarization is slow and inconsistent, although it is not always less accurate than machine summarization. AI is faster and more repeatable, but it can omit qualifications, flatten disagreements, or present an error with perfect confidence.

The relevant comparison is not between rich human reading and lifeless machine compression. It is between different forms of compression, each with its own strengths and failures.

This does not mean that all reading can be replaced by summaries. Reading a novel for pleasure is not mainly an information-retrieval task. Style, rhythm, suspense, voice, and emotional sequence are part of the experience. A summary of a joke is rarely funny, which may be the shortest available proof that compression has limits.

Language learners also need contact with actual sentences. Close reading requires attention to word choice and form. Legal, medical, and scientific work may depend on qualifications that disappear in a short account. In a randomized study of 195 participants, AI reading tools produced different effects for different readers. Summaries could help some lower-performing readers, but they reduced comprehension among stronger readers, apparently because details and nuance had been removed.

The proper question is therefore not whether a summary counts as reading. The question is what kind of reading a task requires.

Sometimes we need orientation. Sometimes we need a central claim. Sometimes we need to check whether a document deserves closer attention. An eight-week study of college students found that some students used AI summaries to decide which parts of assigned texts merited deeper reading. This practice can become passive, but it can also support triage in a world where no scholar can read every relevant source from beginning to end.

We should teach students to move between levels of compression. They can begin with an AI overview, inspect the original text, compare the two, identify what was lost, and revise the summary. That process makes summarization visible. It turns a hidden mental act into an object of judgment.

The educational task is not to protect every page from compression. It is to teach students when compression is the work, and when the missing page is the point.