A concept map answers a question most notes cannot: not just what the ideas are, but how they relate. Where a list stacks facts on top of each other and leaves you to hold the connections in your head, a concept map draws those connections on the page, with a labelled line between every pair of ideas. The result is a diagram you can read like a set of sentences, which is exactly why teachers, researchers and knowledge teams have relied on it for fifty years.
Key takeaways
- A concept map is a diagram that shows concepts as labelled nodes and the relationships between them as labelled lines, so every connection carries a meaning.
- It was developed in 1972 by Joseph Novak and his team at Cornell University, built on David Ausubel's theory of meaningful learning.
- Two concepts joined by a labelled link form a proposition, the basic unit of meaning in a concept map.
- A large meta-analysis found that learning with concept maps produced a moderate, significant gain, and that building a map helps more than studying a ready-made one (Schroeder et al., 2018).
- Concept maps suit any task where the relationships between many ideas matter: literature reviews, systems, teaching a subject, and surfacing gaps or misconceptions.
What is a concept map?
A concept map is a visual diagram that represents knowledge as a set of concepts connected by labelled relationships. Each concept sits in a box or circle (a node), and each connecting line carries a linking word or phrase that names how the two concepts relate. Read a node, then the line, then the next node, and you get a short statement such as "photosynthesis requires sunlight" or "inflation reduces purchasing power".
That labelled line is what sets a concept map apart from most other diagrams. In a plain flowchart or outline, the connection between two items is left unstated. In a concept map, the connection is the point: any node can link to any other, and every link says something. The technique was defined and formalised by Joseph Novak and Alberto Cañas, whose paper on the theory underlying concept maps remains the standard reference for how they are built and why they work.
Concept maps are usually organised from general to specific, with the broadest ideas near the top and finer detail below, and they are often built to answer a single focus question. They are used in education, research, government and business to represent both what a learner understands and what an expert knows.
What is concept mapping?
Concept mapping is the process of building a concept map: choosing the concepts, ranking them from general to specific, and connecting them with labelled links until the relationships are explicit. Where a concept map is the finished diagram, concept mapping is the thinking that produces it, and much of the value is in that process rather than in the final picture.
The act of naming every relationship forces a kind of precision that reading and highlighting do not. You cannot draw a link between two concepts without deciding, in a word or two, exactly how they relate, and that decision is where understanding either holds up or falls apart. This is why concept mapping is used as much for diagnosis as for note taking: a vague or missing link usually marks a spot where the underlying knowledge is thin.
How does a concept map work?
A concept map works by turning knowledge into propositions. A proposition is two concepts joined by a linking phrase, for example "cells contain organelles", and it behaves like a small sentence. A finished map is a network of these propositions, so instead of a list of facts you get a structure of connected claims that you can follow and check one link at a time.
This structure reflects a specific idea about how people learn. Novak's work was grounded in the psychology of David Ausubel, whose central claim was that learning happens when new ideas are assimilated into the concepts a person already holds, a process explained in Ausubel's theory of meaningful learning. A concept map makes that existing structure visible, which is why it works well both for building understanding and for showing what someone has understood.
Focus question first. A good concept map usually starts from a focus question, such as "What causes climate change?" The question keeps the map anchored to a purpose, so every concept and link earns its place by helping to answer it, rather than the map sprawling into a loose collection of related terms.
What are the parts of a concept map?
Every concept map is built from the same small set of parts. Understanding them makes the difference between a true concept map and a diagram that only looks like one.
Concepts (nodes)
Concepts are the ideas themselves, each shown as a short label inside a box or circle. Novak defined a concept as a perceived regularity in events or objects, so a good concept label is usually one or two words: "gravity", "market demand", "the water cycle". Keeping labels tight is what lets the relationships, not the wording, carry the meaning.
Linking words and phrases
These sit on the connecting lines and name the relationship, for example "causes", "is a type of", "requires" or "leads to". Naming the link is what turns two boxes into a statement rather than a loose pairing, and choosing the phrase precisely is the hardest and most useful part of the whole exercise.
Propositions
A proposition is a concept, a linking phrase and a second concept read together as a statement. Propositions are the basic units of meaning in a concept map, and a map is essentially a connected set of them. If a proposition does not read as a true, sensible statement, the link needs rethinking.
Hierarchical structure
In the classic form, the most general and inclusive concepts sit at the top of the map and the more specific ones spread out below. This ordering is not decoration: it mirrors the way broad ideas subsume narrower ones, and it makes the map easier to read from overview down to detail.
Cross-links
Cross-links connect concepts that live in different regions of the map, cutting across the main branches rather than running down them. Novak and Cañas treated these as a marker of creative, integrated understanding, because spotting a valid relationship between two distant ideas usually means you have grasped how the whole subject fits together.
Focus question
The focus question is the specific question the map sets out to answer. It is not always drawn on the map, but it governs everything on it, keeping the concepts and links relevant and giving you a clear test for whether the map is finished.
Who invented concept maps?
Concept maps were developed in 1972 by Joseph D. Novak and his research team at Cornell University. The team was running a long study of how children's understanding of science changed over time, and they needed a way to capture and compare that understanding that interview transcripts could not provide. Representing each child's knowledge as a map of connected concepts turned out to be the answer.
The method rests on the learning psychology of David Ausubel, summed up in his line that the single most important factor influencing learning is what the learner already knows. Novak built on that to argue that meaningful learning involves assimilating new concepts into the frameworks a person already holds, and the full account of how the tool grew out of that research is set out in the IHMC record of the origin and development of concept maps. Novak later joined the Florida Institute for Human and Machine Cognition, where the widely used CmapTools software was created.
What are the types of concept maps?
Concept maps take a few common shapes depending on the structure of the knowledge you are mapping. The underlying rules are the same; only the layout changes.
Hierarchical (tree) maps
The classic form, with the most general concept at the top and increasingly specific ones branching downward. Best for subjects that have a clear order of inclusion, such as a topic broken into themes and sub-themes.
Spider maps
A central concept in the middle with related concepts radiating outward. This is the layout closest to a mind map, though a true concept map still labels every link. Good for exploring one theme from several angles.
Flowchart maps
Concepts arranged in sequence to show a process or a chain of cause and effect, with links describing each step. Useful when order and direction matter, for example a decision process or a biological pathway.
Systems maps
A denser network with many cross-links and often feedback loops, used to show how the parts of a whole system interact. This form leans hardest on labelled relationships and is common in research and complex problem solving.
What are the benefits of a concept map?
The main benefit of a concept map is that it makes relationships explicit, which is where most of its other advantages come from.
- Shows how ideas connect, not just what they are. Every labelled link states a relationship, so the structure of a subject is visible at a glance.
- Surfaces gaps and misconceptions. A link you cannot name, or one that reads as false, points straight to shaky understanding, which makes concept maps a strong self-check and assessment tool.
- Supports meaningful learning over rote memorising. Building the map ties new ideas to what you already know, which is the mechanism Ausubel's theory describes.
- Helps synthesise many sources. Ideas from different readings can be placed on one canvas and linked, so a literature review or research topic becomes a single connected picture.
- Captures expert knowledge. The same structure that helps students learn also lets teams record and share how an expert or a group thinks about a domain.
Are concept maps effective? What the research says
Yes. Concept mapping is one of the better studied learning techniques, and the evidence is consistently positive. An early meta-analysis by Nesbit and Adesope (2006) pooled 55 studies covering 5,818 learners from Grade 4 up to university, and found that using concept maps was associated with better knowledge retention, with the size of the gain depending on how the maps were used.
A larger and more recent meta-analysis by Schroeder and colleagues (2018) analysed 142 effect sizes across roughly 11,800 participants and reported a moderate, statistically significant benefit overall. Its most useful finding for practice was the difference between two ways of using maps: constructing your own concept map helped more than studying a ready-made one, and the effect held across both STEM and non-STEM subjects.
Building beats reading. The practical takeaway from the research is that the benefit comes from doing the mapping, so the point of a concept map tool is to make building and reshaping a map fast enough that you actually do it.
Who uses concept maps?
Concept maps started in education and spread from there into research and industry, wherever relationships between many ideas need to be made clear.
Students and educators
This is the original home of the technique. Students use concept maps to learn subjects with many interacting parts, especially in the sciences, and teachers use them to check understanding before and after a lesson, because a map reveals misconceptions that a test score hides.
Researchers and academics
Concept maps are well suited to literature reviews and theory building, where the task is to see how many findings, methods and ideas connect. Laying sources out as linked concepts makes the shape of a field visible and shows where the open questions are.
Business and knowledge teams
In organisations, concept maps are used to capture expert knowledge, model how a system or process works, and give a team a shared picture of a complex domain. The same labelled structure that helps a student learn helps a team agree on how things actually connect.
When should you use a concept map?
Use a concept map when the relationships between ideas matter as much as the ideas themselves. It is the right tool when you are working with many concepts that interact, when you need to explain or teach how a subject fits together, when you are reviewing a body of research, or when you want to test your own understanding by forcing every connection into the open.
It is not the right tool for everything. Straightforward, step-by-step information is usually clearer as a numbered list, and if you only want to expand a single topic quickly without labelling relationships, a mind map is faster. Concept maps earn their extra effort precisely when that effort of naming each link pays off in clarity.
Concept map examples
The clearest way to understand a concept map is to picture a few. The same rules produce very different maps depending on the focus question.
A science topic map
"The water cycle" sits at the top, with concepts such as evaporation, condensation, precipitation and collection linked by phrases like "leads to" and "returns water to". Cross-links tie the stages back into a loop, so the map reads as a working explanation rather than a list of terms.
A literature review map
A research question sits at the centre, with key theories, studies and methods as concepts. Links label each relationship ("supports", "contradicts", "builds on"), so gaps and disagreements in the field become visible at a glance.
A systems map
A software architecture or a business process laid out as components linked by what they do to each other ("sends data to", "depends on", "triggers"). The dense cross-links are the value, because they show how a change in one part ripples through the rest.
Concept map vs mind map
Concept maps and mind maps are often confused, but they solve different problems. A concept map links many concepts in a network and labels every relationship, so it is best for showing how several ideas interact. A mind map radiates from one central topic in a hierarchy and rarely labels its links, so it is faster and better for expanding a single subject. If you want the full picture of the other format, see our guide to what a mind map is.
| Feature | Concept map | Mind map |
|---|---|---|
| Structure | Network of linked concepts | Radial hierarchy from one centre |
| Links | Labelled with a relationship | Usually unlabelled branches |
| Centres | Can have several, plus cross-links | One central topic |
| Best for | Showing how many ideas interact | Expanding a single subject fast |
| Origin | Novak, Cornell, 1972 | Popularised by Tony Buzan, 1970s |
If you are specifically choosing software for this format, our tested comparison of the best concept map makers scores eleven tools on fit, AI, collaboration, export and free-plan value.
What is an AI concept map?
An AI concept map is a concept map generated by artificial intelligence from a topic or a body of text, rather than built by hand. You give the tool a subject, a set of notes or a document, and it identifies the key concepts, proposes the relationships between them, and lays out the nodes and labelled links automatically. The result is a normal concept map; only the first draft is produced differently.
The point is speed and a starting structure. Instead of facing a blank canvas, you get a complete draft in seconds that you can then correct, relabel and extend, which fits neatly with the research finding that the real learning happens when you build and reshape the map yourself. This suits long source material in particular, such as turning a research paper or a set of readings into a first map without restructuring it by hand. Tools like MindMap AI can generate that draft from a prompt, a file or a recording, and you stay in control of the final shape.
How do you create a concept map?
Creating a concept map follows six steps that work on paper, on a whiteboard or in software.
- Set a focus question. Decide the exact question the map should answer, so every later choice has a test to pass.
- List the key concepts. Brainstorm the important concepts and keep each label to a word or two.
- Rank them general to specific. Put the broadest concepts near the top and the detail below.
- Connect and label the links. Draw lines between related concepts and write the linking phrase on each, so every pair reads as a proposition.
- Add cross-links. Join concepts in different parts of the map where a real relationship exists, since these show integrated understanding.
- Review and refine. Read the map as a set of statements, fix any link that does not hold, and reorganise until it answers the focus question.
AI concept map tools can produce the first draft of that structure for you: you enter a topic or paste your notes, the tool builds a labelled map in seconds, and you edit and extend it from there.
FAQ
A concept map is a diagram that shows ideas as labelled boxes and the relationships between them as labelled lines. Reading a box, then the line, then the next box gives you a short statement, so the map shows not just what the ideas are but exactly how they connect on a single page.
A concept map links many concepts in a network and labels every relationship, so it is best for showing how several ideas interact. A mind map radiates from one central topic in a hierarchy and usually leaves its branches unlabelled, which makes it faster for expanding a single subject. Concept maps trade speed for the precision of naming each connection.
Concept maps were developed in 1972 by Joseph Novak and his research team at Cornell University, while studying how children's understanding of science changed over time. The method is built on David Ausubel's theory of meaningful learning, which holds that the most important factor in learning is what the learner already knows.
Yes. A meta-analysis by Schroeder and colleagues (2018) analysed around 11,800 participants and found a moderate, significant benefit to learning with concept maps, and an earlier meta-analysis by Nesbit and Adesope (2006) reached the same conclusion across 55 studies. Both found that constructing your own map helps more than studying a ready-made one.
The core parts are concepts (short labels in boxes), linking words on the connecting lines, and propositions (a concept, a link and a second concept read as a statement). Most maps also use a hierarchy from general to specific, cross-links between distant concepts, and a focus question that the whole map answers.
References
- Novak, J. D., & Cañas, A. J. The Theory Underlying Concept Maps and How to Construct and Use Them. Technical Report, Florida Institute for Human and Machine Cognition (IHMC).
- Novak, J. D., & Cañas, A. J. The Origin and Development of Concept Maps. IHMC.
- Joseph D. Novak. Biography, developer of concept mapping at Cornell University (1930 to 2023).
- Nesbit, J. C., & Adesope, O. O. (2006). Learning With Concept and Knowledge Maps: A Meta-Analysis. Review of Educational Research, 76(3), 413 to 448.
- Schroeder, N. L., Nesbit, J. C., Anguiano, C. J., & Adesope, O. O. (2018). Studying and Constructing Concept Maps: a Meta-Analysis. Educational Psychology Review, 30, 431 to 455.
- University of Illinois Springfield. Concept Mapping. Overview of the technique and Ausubel's meaningful learning theory.
- CmapTools. Concept mapping software from the Florida Institute for Human and Machine Cognition.
