Definition

What is Dynamic Knowledge Rendering (DKR)?

Dynamic Knowledge Rendering (DKR) is the process of delivering information to suit individual requirements. The term was coined by Lee Mallon, who introduced it in his book DKR: Dynamic Knowledge Rendering (March 2024).

In the book’s words

“Imagine a world where knowledge transcends the limitations of static text confined to the pages of a book, evolving into a dynamic, living essence that adapts and conforms to your mode of comprehension. […] DKR doesn’t just present information; it reshapes it, ensuring that it aligns perfectly with your learning style, whether visual, auditory, kinesthetic or a blend of multiple approaches.”

— Introduction

The idea in brief

DKR starts from a distinction between information and knowledge. A book, a report or a training course holds information. Knowledge is what happens when that information meets a particular mind: it is filtered through that person’s experience, interests and way of learning. Two people can read the same page and come away knowing different things.

For generations we have delivered information one way to everyone, because producing it any other way was too expensive. Generative AI removes that constraint. The same knowledge can now be rendered as a short video for one person, a detailed first-person text for another, or a podcast for someone on their commute, and the effort of creating each version no longer outweighs the benefit.

How DKR renders information

The book sets out the inputs a system weighs before it renders anything for a person:

%% The inputs to dynamic rendering
flowchart TD
    Context[Context Data] --> Info([Hyper-Personalised Information])
    Temporal[Temporal Scope] --> Info
    Signifiers[Modality Signifiers] --> Info
    Knowledge[Knowledge] --> Info
    Preferences[Preferences] --> Info
    Modality[Modality] --> Info

• Context data: where you are, what time it is, what device you are on, who you are with, whether you are driving.
• Temporal scope: when a request happens and how long it should take.
• Modality signifiers: the qualities that make a format work, such as tone and length for text, or pace and frame rate for video.
• Knowledge: the actual knowledge that answers the request.
• Preferences: what you like, how you learn and how long you can give your attention.
• Modality: text, audio, images and video, but also code, games and 3D models.

Read more in Dynamic Rendering, Learning Styles and Attention.

Examples from the book

• Fire safety training. Laura, a volunteer firefighter who has done the course four years running, gets only what has changed, as a few bullet points. Alice, two months into the job and dyslexic, gets a full course built around how she learns.
• Meetings to podcast. Stewart leaves early for his son’s football match. The meetings he missed are turned into a podcast episode in the style of his favourite hosts, ready for his commute.
• Pokémon months. A six-year-old who cannot remember the months of the year but knows 200 Pokémon learns them as Pokémon birthdays, set to a personalised song.

The building blocks

To make DKR real, the book introduces digital doppelgangers (AI counterparts that represent an asset or assist a person), Preference Prompts (plain-language statements about you that you control), the Preference Exchange Protocol for sharing preferences with websites and services, truth graphs to check what AI produces, and the digital brain, a network of doppelgangers working together. Every term is defined in the glossary.

Frequently asked questions

What is Dynamic Knowledge Rendering (DKR)?

Dynamic Knowledge Rendering (DKR) is the process of delivering information to suit individual requirements. It uses AI to reshape the same knowledge into whatever form, length and style suits a particular person at a particular moment, whether that is text, audio, video, a song or an interactive experience.

Who coined the term Dynamic Knowledge Rendering?

Lee Mallon coined the term Dynamic Knowledge Rendering (DKR) and introduced it in his book DKR: Dynamic Knowledge Rendering, published in March 2024. When he conceived the idea, a search for “dynamic knowledge rendering” returned no existing references.

How is DKR different from ordinary personalisation?

Most personalisation today chooses between pre-made options, such as which thumbnail or product to show. In DKR nothing is pre-made: the content itself is generated for one person, at one moment, in one modality, after every aspect of their preferences and context has been considered.

What is a digital doppelganger?

A digital doppelganger is an AI counterpart that mirrors human knowledge, preferences and behaviour. Representative doppelgangers make a static asset, like an employee handbook, conversational. Assistive doppelgangers act as a proxy for a person, carrying out requests in line with their preferences.

Does hyper-personalisation mean we lose shared experiences?

Not if it is designed well. The book proposes Shared Preferences: rules that everyone’s personalised view must keep, such as “the ball has to remain a ball” in a football match, so people can still share and discuss the same experience.

Where can I read the book?

The full book is free to read online at dynamicknowledgerendering.com/book, and can be downloaded as a PDF.

To reference DKR in your own work, see how to cite the book.