I closed last week’s article with a reference to Information Mapping, promising to explain more about it and explain why, when used in combination with Markdown, it helps to produce clear, structured performance support content for your learners.
So, let’s explore all this a bit more and discover why you would want to use a combination of Markdown and Information Mapping principles to achieve this goal.
Mapping has been around for decades – long before AI entered the conversation. It offers a straightforward way to structure knowledge so that people can find what they need, understand it quickly, and act upon it immediately.
Chunking with Relevance: Information Mapping’s Mantra
At the heart of Mapping is the idea of chunking with relevance. In other words, breaking a long piece of content into small, manageable units; and (very important) making sure each chunk is self-contained. In other words, only one item or idea per chunk. This is not simply about shortening; it’s about scoping, too.
It’s a principle that works well for general business communications, procedures and policies, learning materials and, of course, performance support materials. No surprise then (as noted in last week’s article) performance support experts have long emphasised the importance of well-structured and logically sequenced content, even if they didn’t talk about Information Mapping principles by name.
The Power of Information Types
Supporting and reinforcing the principle of chunking with relevance is the idea of different types of information. These are six categories of information that neatly define the purpose of 90% of content that gets used in a business or workplace setting.
Ensuring that each chunk of information contains only one type of information is an easy way to ensure the chunk remains well scoped and focused only on content related to that purpose
Chunking and Information Types combined
In practice, combining chunking with relevance and information types make it much easier to create a learner-centred “flow of clarity.”
This is a sequence of support content that allows a learner to move seamlessly from a question to a confident action, without having to wade through irrelevant detail or lose context along the way.
What this means in an AI world
So, what does this all mean in the age of AI?
Well, the first important point is that AI is pretty well aligned with these principles already. Thanks to Markdown, generic AI output is generally well-structured, already.
The second important point is that by applying Mapping principles on top of that, you are adding an extra layer of design thinking that is particularly helpful in a performance support context.
What this means for PerformaGo
This means that PerformaGo will use a Markdown framework and Mapping principles in backend templates, which will manage the structure and sequencing of content when the GPT outputs a response to a learner question.
By blending the logic of Markdown andMapping principles with the power of AI automation, PerformaGo ensures that every piece of support material stays clear, consistent and focused on learner need.
In short: structure that once had to be painstakingly built by hand now comes built-in.
Mapping provides the principles. Markdown provides the framework. PerformaGo offers the means to bring both together seamlessly.
