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Kirsten AlburgLearning Experience Designer
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Workforce Development

AI Workplace Essentials

Practical, responsible AI use for everyday knowledge work

Format
Scrolling module with practice activities
Audience
Corporate employees adopting AI tools
Length
25 minutes
Sections
5

Portfolio demonstration. This course was designed and built by Kirsten Alburg to show instructional design capability. It was not created for a client and is not built in Articulate.

Lesson 1

What these tools actually do

Colleagues gathered around a laptop while one explains an AI workflow
Adoption spreads sideways, between colleagues — not downward from a licence announcement.

Large language models predict likely text. That single fact explains both why they are useful for drafting and structuring, and why they state wrong things confidently.

This module is about matching the tool to tasks where prediction is genuinely helpful — and recognizing the tasks where it is not.

Learning objectives

0/4 tracked
Why it matters
Teams adopt AI unevenly: a few power users, many quiet abstainers, and no shared standard for what is safe to put in. That gap is a risk and a productivity loss at the same time.
Where you will use it
Drafting, summarizing, and analysis in your normal work — plus the moments where the right decision is not to use AI at all.

Lesson 2

Prompting that works

Four parts of a useful prompt

Role

Who should the model act as? 'You are reviewing this for a non-technical audience.'

Context

What does it need to know? Paste the source material rather than describing it.

Task

One clear verb. Summarize, rewrite, compare, outline, critique.

Constraints

Length, format, tone, and what to leave out. Constraints do more work than adjectives.

Before and after

The output will be generic because the request is generic. Nothing in the prompt tells the model what makes this email different from any other.

Lesson 3

Prompt practice lab

Build a stronger prompt

Weak prompts produce generic output. Add each missing ingredient and watch the request become something a colleague could actually act on.

Before — weak prompt

Summarize this customer feedback.

Add the missing ingredients, one at a time:

Role: Tell the tool whose judgment to apply.

Context: Describe the material and who will read the result.

Task: State the specific output you want.

Constraints: Set format, length, tone, and what to leave out.

After — your prompt

Summarize this customer feedback.

Knowledge check

Which details are safe to include in a prompt to a general-purpose AI tool? Select all that apply.

Lesson 3

Where it belongs in your workflow

Three realistic workflows

First drafts of documents you would write anyway. You remain the author and the editor; the tool removes the blank page.

Scenario

You need to summarize a customer complaint thread for your manager. The thread contains the customer's name, contract details, and a health-related reason for a missed deadline.

What is the responsible approach?

Lesson 4

Practice

Practice activity 1

0 words

Responses stay in your browser — this is a portfolio demonstration, nothing is submitted.

Knowledge check

Which task is the poorest fit for an AI assistant?

Resources

  • Four-part prompt cardRole, context, task, constraints — printable one-pager.
  • Task suitability checklistFive questions to ask before using AI on a work task.
  • Data minimization guideWhat to strip before pasting anything.

Job aid

Responsible AI Quick Card

The checks to run before pasting anything into an AI tool, and before sending anything it produced.

Performance support like this is what keeps the behavior alive after the course ends.

Course complete

Module complete. You can explain what these tools do, write a structured prompt, and make a defensible decision about when to use one.

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First MoveMomentumDeep ExplorerDesign Thinker

Points, streaks, and badges are part of the demonstration — they model how light gamification can keep adult learners moving without turning training into a game show.

Behind the build

Design Decisions

The instructional thinking behind this learning experience. Every interaction has a purpose; every design decision supports a learning objective.

Learning challenge

Teams were told to use AI tools and given no basis for judging when the output could be trusted, so adoption split into over-reliance and refusal.

Audience

Primary learners
Corporate employees across functions adopting AI tools.
Prior knowledge
Ranges from daily users to complete beginners in the same room.
Learning need
A shared standard for what these tools do well, and where the human stays accountable.
Context of use
Everyday knowledge work, under policies most staff have not read.

Learning objectives

  • Describe what a language model is doing when it answers.
  • Write a prompt that produces usable output on the first attempt.
  • Identify the tasks where AI use is inappropriate or prohibited.
  • Verify output before it leaves the desk.

Design case study

Read the full breakdown

Challenge

Organizations roll out AI tools with access but without judgment. Employees either avoid the tools entirely or use them on tasks where the risk is highest.

Instructional strategy

Concept, model, practice. Every principle is followed by a real work artifact the learner produces. Risk content is taught through a sympathetic scenario rather than a policy list, because policy recall does not transfer to time pressure.

Reflection

I use AI tools daily in my own instructional design workflow, and that shaped this course: the honest framing — useful for drafting and structuring, unreliable for fact — earns more trust from skeptical learners than enthusiasm does.

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