Everyday work · Three free lessons
AI at Work.
Three practical lessons for turning everyday information into a useful draft, and knowing what still needs checking.
The first three lessons
Give AI a useful job
Turn a vague request into a short assignment you can check.
Give the right information
Help the writer see what is current, what is background, and what is still unknown.
Check it before you share it
Repair a plausible draft and explain the corrections.
A better way to get
everyday work done.
AI can help with an email, a meeting summary, a first draft, or an action list. I want the person using it to stay able to explain the result: what it says, where the details came from, and what remains undecided.
This first course follows one familiar task: prepare an internal update about a hybrid team workshop. You will frame the request, organize a small set of sources, and repair a convincing but inaccurate draft.
Before you start
No AI account is required. Work in a document or on paper, try the task, then compare your answer with the example. If you choose to try a prompt in an organization-approved AI tool, use only this fictional packet. The examples and feedback are written learning material, not a live AI assessment.
The suggested 15–20 minutes per lesson is a starting estimate. Take more time, use written or spoken answers, and keep the packet open. Try the practice task before opening its answer, then explain what you would change.
The scenario: A workshop update with a few moving parts
You are helping prepare an internal team update and action list for Noor, the workshop organizer. Everyone and everything in this packet is fictional. The exercise reference date is 19 September 2026. Use D1–D3 as sources. D4 is a deliberately flawed draft to review, not evidence of what happened.
Take the materials
with you.
For facilitators and teams
The editable pack includes all three lessons, fictional source documents, a blank worksheet, and a facilitator guide. It works for individual practice or a small team session.
Research and source notes
Reviewed Sep 19, 2026. These sources inform the exercises; they do not establish results for this course.
S1 · Get started writing prompts in Microsoft Copilot
What it supports: Start with a clear goal, add useful context, expectations and sources, iterate, and verify responses against trusted material.
In this course: Lessons 1–3 use a bounded assignment, a labeled source packet, and claim-by-claim review.
Limits: Product guidance, not measured proof of productivity or this teaching method. The course needs no Copilot account; access to organizational work data in Copilot depends on the product entitlement.
Revisit when: Recheck when the guidance, intended tool, or organization's permitted data use changes.
S2 · Learn about prompts for Gemini in Docs, Sheets, Slides, Vids & Forms
What it supports: Use natural language, a specific task, relevant context and an appropriate format; review and refine. Not every suggested prompt component is mandatory.
In this course: Lessons 1–2 use ordinary wording and a compact context note rather than a required prompt formula.
Limits: Vendor recommendations for Google features, not evidence that one format works best everywhere. Product eligibility is separate; no Gemini account is required for the exercises.
Revisit when: Recheck if Google revises the guidance or if a lesson starts depending on a specific product feature.
S3 · How AI assistance impacts the formation of coding skills
What it supports: In a randomized study of 52 mostly junior Python developers learning Trio, the AI group averaged 50% on an immediate quiz versus 67% without AI; the completion-time difference was not statistically significant.
In this course: Ask learners to make an attempt, explain a correction, and distinguish task output from understanding.
Limits: Small coding study with an immediate assessment. Interaction patterns were qualitative associations, not randomized teaching methods. It does not establish lasting skill loss, results for everyday writing, or effectiveness of this course.
Revisit when: Revisit after relevant replications, longer follow-up, or evidence from comparable employee writing tasks.
S4 · Generative AI without guardrails can harm learning: Evidence from high school mathematics
What it supports: Assisted practice improved in both AI conditions. Ordinary answer-oriented assistance harmed later unassisted exam performance; the teacher-informed tutor largely avoided that harm without showing an unassisted advantage over the control.
In this course: Offer a first attempt, an optional hint, an answer explanation, and a changed-case question; keep assisted and independent observations distinct.
Limits: One high school in Turkey, mathematics, a Fall 2023 deployment, and short-term outcomes. This was not employee training or a test of static feedback. It does not demonstrate this course's learning effect.
Revisit when: Revisit when studies test comparable workplace tasks, longer-term outcomes, or different assistance designs.