Learn Mapping in 30 Minutes
You just need to know two things: what your “stuff” is, and how it connects. This tutorial walks you through both.
By the end of this tutorial you will be able to…
- Identify the "things" (nodes) and "connections" (edges) in any curriculum dataset
- Organize your data into semantic groups
- Use AI to extract the structure of your data
- Create, style, and share your first interactive map
The start-from-zero problem
You look at your curriculum. All the content is there — and there is so much of it — but you have absolutely no idea how to map it.
This is the most common experience new users have, and it has nothing to do with the tool. It's a thinking problem, not a software problem. The tool works fine — your brain just hasn't shifted into “mapping mode” yet.
The shift you need to make: Stop thinking about your curriculum as rows in a spreadsheet, or paragraphs in a Word document. Start thinking about it as granular things and connections between things.
That's it. Honestly, that's the whole secret. The rest of this tutorial is just showing you how to do that for your specific situation.
Step 1: Find your “things”
These become nodes
A node is just a dot on the map. Each dot represents one “thing.” In curriculum work, things are usually:
- —Learning outcomes (e.g. "Students will be able to apply the chain rule")
- —Courses (e.g. "Calculus I", "Introduction to Ethics")
- —Modules or units within a course
- —Accreditation standards (e.g. "ABET Criterion 3a")
- —Assessments, assignments, and other learning resources
Quick exercise
Write down 5–10 things from your curriculum. Don't worry about organizing them yet — just list them. These will become your first nodes.
Step 2: Find your connections
These become edges
An edge is a line between two nodes. It means “these two things are related.” The label on the line tells you how they're related.
Edges come in two flavors:
Directed →
The relationship has a clear direction — one thing points to another. An arrow shows you which way.
Example: “Calc I requires Algebra” — not the other way around.
Undirected ↔
The relationship goes both ways equally — neither side is the “source.” A bidirectional arrow is drawn.
Example: “Statistics is related to Machine Learning” — it’s mutual.
In curriculum work, common relationships are:
requiresdirected →"Calc I" requires "Algebra" as a prerequisite
teachesdirected →A module teaches a learning outcome
assessesdirected →An exam assesses a competency
satisfiesdirected →An outcome satisfies an accreditation standard
related toundirected ↔Two topics overlap or reinforce each other — no clear direction
is corequisite toundirected ↔Two courses must be taken together — the relationship is mutual
Wording matters
For directed edges, the way you phrase the label determines which node the arrow points from and which it points to. The same real-world relationship can be expressed two different ways — and each gives you a different arrow:
Both diagrams below describe the same fact — Calc I must come before Calc II — but the label wording flips the direction of the arrow entirely.
“Calc II requires Calc I”
Arrow starts at Calc II — it’s the one with the dependency
“Calc I is required for Calc II”
Arrow starts at Calc I — it’s the one doing the enabling
Neither phrasing is wrong. But you should pick one convention and stick to it throughout your map. Most curriculum maps use the subject-as-source style: “X requires Y” means X is the thing that has the requirement, so the arrow starts at X.
Quick rule of thumb: Read your edge label out loud as a sentence — “source [label] target.” If that sentence makes logical sense, your arrow is pointing in the right direction.
Quick exercise
Look at your list of things from Step 1. Draw a line between any two that are related. Write one or two words on the line describing the relationship — then read it as a sentence to check the arrow direction makes sense. That's your first edge.
Step 3: Organize into groups
These become clusters on your map
Most curricula have natural containers — departments hold courses, courses hold modules, modules hold learning outcomes. In Outcomap, these containers are called groups.
Groups aren't drawn as lines. Instead, they visually pull related nodes together so that the map doesn't look like a random tangle. Think of them as the “zip codes” of your map — they tell you which neighborhood a node lives in.
Groups can be nested. A Department can contain Courses, which contain Modules, which contain Learning Outcomes. Outcomap handles up to three levels of nesting.
Ask yourself
Look at your list of things from Step 1. Do any of them naturally contain other things? A course contains modules. A program contains courses. Those containers become your groups.
Step 4: Putting it together
Let's take a real-looking example and see how it transforms from a spreadsheet into a map.
Imagine you have a spreadsheet with columns: Course, Learning Outcome, Prerequisite(s). It works fine for storage — but it's nearly impossible to see the shape of your curriculum. Where does the knowledge start? Where does it lead? Which outcomes are prerequisites for many others?
How you store it now
Hard to see the big picture. Where does it start? Where does it end?
How Outcomap shows it
Solid = within-course chain · Dashed = cross-course prerequisites
The map on the right contains exactly the same data. But now you can instantly see the flow from foundational concepts to advanced ones — and spot where cross-course dependencies exist (the dashed grey line).
In this map, the things are the learning outcomes (circles). The connections are the prerequisite arrows. The groups are the courses (dashed boxes).
Step 5: Use AI to build it in Outcomap
Now that you know what your nodes, edges, and groups are, it's time to hand your content to Outcomap's AI. Paste in a syllabus, course description, accreditation document, or even a rough set of notes. The AI reads it and generates the nodes, edges, and groups for you — giving you a working first draft in seconds that you can then refine.
A question we hear a lot
“If I paste my institution's unpublished curriculum into Outcomap, could it end up visible to other users — or get used to train an AI?”
This is one of the most common concerns we hear, and it's a completely reasonable one. The short answer is: no, your content stays yours.
Outcomap uses Google Gemini to power its AI features. We have specifically configured our integration so that your content is never used to train or improve Google's AI models. In plain terms: when you paste your syllabus in, Google's AI reads it to generate your map, and then that's it — your content is not stored, fed back into the model, or made available to anyone else. It does not become part of the AI's “memory,” and other Outcomap users cannot see it.
We do not sell or share your data. You can read more in our Privacy Policy.
A note on the future: We configure our AI integration to the highest privacy settings available to us today. While Google's own policies may evolve over time, we commit to reviewing any changes and updating our configuration — and this page — accordingly. If you have specific compliance requirements, please reach out at support@outcomap.com.
Once you have a map…
Customize with colors and styling effects
All colors, sizes, layouts and interaction effects are customizable. Custom styles can be changed at any time.
Click nodes to explore
Click any node and Outcomap highlights everything connected to it. This is how you explore the map — it's interactive, not a static image.
Share or embed
Generate a public link to share with stakeholders, or grab an embed code to drop the live map into your CMS or website.
Next steps
You now have everything you need to build your first map. Here's where to go from here:
Documentation
Deep dives into every feature — blueprints, themes, AI generation, sharing, and more.
🗺️Examples
Browse live maps across accreditation, research, and course syllabi for inspiration.
🚀Start mapping
Head to your dashboard and create your first map right now.
Still have questions?
support@outcomap.com