context, a revision, then check the work

The app list changes every week. The habits do not. Before I care which model a teacher uses, I care whether they can give context, ask for revision, and check the answer before it lands in front of students.

The three skills here are the ones I kept coming back to in class: a simple prompting framework, a revision loop, and a way to cite AI use without pretending it did not happen.

That is less flashy than a new app list, but it survives the next product launch.

Video version: YouTube

Audio version: Podcast

Mindset before skillset

Before the skills, the mindset matters. My brain works in analogies, so here is mine for teachers: if you had a student teacher who read every book in the library, every blog online, every textbook about everything, who didn't sleep, who always wanted to make you happy and did things way faster than you could, what jobs would you give them?

That assistant would be useful and annoying at the same time. It knows a lot, wants to please you, sounds confident when it is wrong, and rarely asks the question you wish it had asked.

How would we use such an assistant? I would start with work I already know how to judge, where I can tell the difference between helpful and wrong. I would not give it a lesson topic I do not understand and let it plan alone. That is the AI habit I want teachers to build: know where your judgment belongs before you hand work to the tool.

Skill 1: prompting

The first habit is giving the tool enough context to be useful. The fastest way I know is a framework simple enough to remember and specific enough to improve the answer. I use a framework called RAFT:

• Role: Who would you go to for help on this task? (e.g., a master level history teacher, an expert instructional coach)
• Audience: Who are you creating this content for? (e.g., your students, a co-teacher, a mentor)
• Format: What format do you want the output in? (e.g., graphic organizer, narrative, bulleted list, table, email)
• Topic: What are you talking about? (e.g., the curriculum, the unit, the theme, a certain perspective)

Here is an example of a RAFT prompt:

Role: Expert instructional coach and curriculum designer

Audience: An eighth grade algebra teacher

Format: A clear, step-by-step workshop outline

Topic: A project-based learning unit on linear equations where students understand real-world applications

When you use RAFT, you are doing what you would do with a human helper: giving enough context that the first draft usually lands closer to what you meant.

Skill 2: iteration

No matter how precise your prompt is, don't think the first output is all your gen AI tool can do. Think of it like a first draft from your students.

Here are three ways I would revise before I trusted the answer:

• "And what else?" Before generating a response, ask the AI tool, "What else do you need to know from me to execute this successfully?" That question catches context you may have missed. It works even better if you have an exemplar, pasted example, or rubric.
• Guardrails: Tell the AI what not to do. For example, "Don't use academic words," or "Don't search the internet, just use this source." This further limits the context and improves the output.
• Split testing: Ask the AI for multiple different approaches or versions. This gives you a menu of options instead of one "right" answer.

Skill 3: research and citing

Once students can make a decent first draft, citation becomes the next problem. They need to show what source the answer came from and how much help the model gave them.

My rule: do not cite the tool itself. Citing "chatgpt .com" is like citing the public library. It is not a specific source.

Instead, use an AI tool that can cite the websites it used. Then share the chat log. That gives you the question, the answer, and the handoff in one place.

The list is simple: give context, revise the answer, and show your work.

The next question is which apps are worth a teacher's time and which ones I would skip.