Keep Your Course and Add a Skill: Where Each Belongs

If your course already teaches a useful method, start by adding a skill to one exercise where learners repeatedly get stuck. Keep the lesson that explains the idea. Let the skill help them apply it, then return a clear piece of work to the course.

The handoff should be easy to describe: “After this lesson, use the practice guide to prepare your own version. Bring the result to the next exercise.” If you cannot name the result, the companion may be adding conversation without improving the learning experience.

You do not need to turn the entire curriculum into a chatbot. Nor do you need to remove the parts that work well because another format is available. The useful question is where a responsive question, example, or critique would help a learner take the next step.

This guide is a design method, with an illustrative course outline. It is not a report of a tested integration or a claim that adding AI improves completion rates.

Review learner questions, submitted exercises, and support requests. Look for a specific recurring difficulty. Perhaps learners can repeat a concept but cannot choose an example from their own work. Perhaps their drafts omit a constraint. Perhaps they do not know when an exercise is finished.

Name that difficulty before selecting technology. “Students need more engagement” is too broad. “Students write a presentation opening without identifying what the audience needs to understand” is something you can address and evaluate.

Then check whether the lesson itself needs repair. If everyone misunderstands the same question, a clearer question or worked example may be enough. A skill should not become a permanent workaround for an avoidable teaching problem.

Choose an exercise with a recognizable endpoint and ordinary, manageable inputs. An early prototype is easier to assess when it has one job than when it is expected to answer anything about the course.

The course can establish the sequence, introduce concepts, show demonstrations, and explain why the method makes particular distinctions. A skill can help a learner apply that material to a new situation and inspect an attempt against supplied criteria.

For example, the course may teach the difference between a topic and a message. The skill can ask the learner to state their message, notice that they have supplied only a topic, and ask a more precise question. That behavior needs to be written and evaluated; it does not follow automatically from uploading the lesson transcript.

Separate three sources in your design:

• Teaching material: what the learner needs to understand.
• Practice procedure: what the assistant should ask, in what order, and when to help.
• Completion criteria: what makes this particular exercise adequate.

One training practitioner, u/SAmeowRI, described experimenting with a tutor using separate documents for tutoring instructions and:

“the learning content and desired final skill and knowledge state.”

That is an individual’s account of an experiment, not evidence of a proven teaching system. The useful distinction is between possessing course material and knowing what a learner is supposed to accomplish with it. Original discussion.

If the purpose is practice, the skill should not immediately do all the work. Define a sequence of help: ask for an attempt, give a hint, explain the relevant distinction, and offer a worked example when appropriate.

If the purpose is producing a usable artifact after the learner understands the method, more direct drafting help may be appropriate. Tell the reader which mode they are entering. Confusing practice with production makes both harder to evaluate.

Existing course products illustrate these different interactions. GoSkills documents lesson-based scenarios with question-and-answer or role-play formats, creator review before publishing, and feedback during practice. This is a product description, not proof that every AI exercise teaches effectively. GoSkills’ scenario documentation.

For your own companion, specify what the assistant should do when a learner asks for the answer immediately, provides a reasonable alternative, or supplies too little information. These are normal learning situations, not exceptional cases to postpone until after launch.

Consider a fictional author, Celia, who teaches a short course on welcoming an audience. Her learners need to prepare a three-minute opening for a volunteer orientation. The course already includes recorded demonstrations, a worksheet, and a live rehearsal.

The following outline is an original design exercise. It has not been used with real learners or run through a course platform.

The skill does not need to operate in every row on day one. Celia might begin only with the message exercise because that is where questions recur. The outline shows where later additions might belong without committing her to building them all.

The first handoff

At the end of lesson two, the course gives this instruction:

Prepare one sentence stating what a new volunteer should understand by the end of your opening. You can use the worksheet alone or the optional skill to examine your first attempt. Save the final sentence and the reason you chose it.

The skill’s opening question asks for the learner’s draft and a short description of the audience. It does not require uploading private volunteer records or the whole course.

Suppose the learner writes, “My opening is about our organization, its history, policies, and opportunities.” The desired response is to notice that this is a topic list and ask which idea should organize it. The assistant might offer the distinction from the lesson, then ask the learner to try again.

The completion rule is simple: the sentence expresses a message a listener could understand, rather than merely naming subjects the speaker intends to cover. Celia should include several valid examples so the assistant does not force every learner into the same wording.

The return to the course

The learner returns with the sentence, a brief audience note, and any unresolved question. Lesson three uses that material to develop an outline. The course should not assume it can automatically read the learner’s conversation from another tool.

A visible save-and-return instruction is enough for an early design. If you later add an integration, verify what transfers and what does not. Avoid promising seamless progress tracking based on a demonstration alone.

Celia’s live rehearsal should remain a live rehearsal. An assistant can help prepare a checklist, but a text-only exchange does not show whether a speaker pauses clearly, responds to a confused listener, or makes the room feel welcome.

If a chosen tool accepts audio or video, test that specific capability with the actual exercise. Do not assume that accepting a file means it can reliably assess every behavior you care about.

Keep other human responsibilities explicit too: deciding whether feedback is fair, responding to a sensitive situation, and making a final assessment when the offer promises personal review. A companion can assist with preparation without pretending to be the instructor.

Technical implementation deserves similar care. Khan Academy’s account of improving Khanmigo describes additional tools, authored learning material, and manual review of tutoring conversations. The limited lesson for an author is that a tutoring experience involves design and evaluation beyond providing a language model with content. Its results should not be transferred to your own untested skill. Khan Academy’s engineering account.

If you are adding an optional companion to an existing course, keep the original exercise usable. A learner should know whether the skill is included, what account or tool it requires, and how to complete the same learning task without it.

This matters for accessibility, organizational restrictions, preferences, and simple setup friction. Do not turn a previously complete course into an incomplete experience that silently requires another purchase.

Write the difference plainly. “The worksheet includes the full exercise; the companion asks follow-up questions about your answers” is clearer than “AI-powered learning experience.” Explain what is genuinely additional and what stays available.

Also make clear who can see submitted work and where conversations occur. Describe the actual services and settings you use; the phrase “course companion” does not establish a privacy policy.

Use one lesson, one exercise, and a small set of varied attempts. Include a strong answer, a vague answer, a plausible answer that differs from your example, and an answer based on a misunderstanding.

Check whether the companion gives the right kind of help for each. Does it ask the learner to think when thinking is the task? Does it accept a valid alternative? Does it identify missing information without inventing it? Can the learner save the result and continue the course?

Compare the exercise with and without the optional help where feasible. Keep the learning objective and criteria the same, and account for practice effects if a person tries both. Retain observations and outputs rather than relying only on a satisfaction question.

In the same training discussion, a contributor using the handle u/nathanpitman described building around human-authored course scripts and noted:

“The litmus test will be end user testing”

The contributor identified themselves as working for an elearning provider. Their statement concerns a work in progress, not a completed study. That is the right distinction to preserve in your own launch claims too. Original comment.

Give each exercise a maintained reference containing its purpose, questions, examples, and completion rule. When you revise the lesson, check whether the companion still teaches the same distinction.

Keep a short change note: what changed, why, and which practice cases were checked. If the skill gives advice that conflicts with the course, learners should have a clear way to report it. Repair the source of the conflict rather than adding a vague instruction to be more accurate.

Our guide to measuring agent skill quality provides a deeper approach to checking the work a skill produces. For a course companion, keep those checks tied to the exercise’s learning purpose.

To explore a companion for a course you already teach, visit Skillfully and choose Book onboarding. Bring one lesson, the exercise learners struggle with, and a strong example of the work you want them to complete.