Using generative AI to control the UI

Using generative AI to control the UI

A useful technique for improving user interface design and interaction is the use of generative AI to control UI. They include the following approaches.

1. adjusting UI design through prompt generation: using a generative AI (e.g. ChatGPT or image generation AI) to input specific prompts and have it output ideal UI components or design ideas. For example, prompts such as ‘easy-to-use menu layout’ or ‘suggested elements to improve user experience’ can be entered to get ideas for optimal UI elements and designs.

2. dynamic control of UI using natural language: a method where users instruct the AI to change the behaviour or appearance of the UI in natural language, and the AI interprets this and automatically adjusts the corresponding UI, for example, dynamically updating the UI in response to instructions such as ‘change this button to blue’ or ‘add more emphasis’. 3. based on user behaviour data.

3. optimising UI based on user behaviour data: Generative AI can learn user preferences and trends from user behaviour data and feedback, and make UI suggestions and adjustments based on this, for example, finding frequently used functions and moving their buttons and icons to more visible positions. For example, it is possible to find frequently used functions and move their buttons and icons to a more visible position. 4.

4. real-time content generation and display: for example, using generative AI to generate custom content in real-time (e.g. a list of recommended products or related information), allowing users immediate access to this information on the interface. By providing content based on the user’s current situation and actions, the UI can be made more interactive.

These specific configurations and implementation examples are described below.

Specific configuration and implementation examples

Examples of concrete configurations that use generative AI to control UI could include the following.

1: Prompt generation and UI design suggestion system

<Configuration overview>

1. a generative AI server: using ChatGPT or similar natural language generative AI, the system proposes UI designs and elements based on user intentions. For example, inputting a user request such as ‘a simple menu screen’ will generate suggestions for the arrangement of UI components and colour schemes accordingly. 2.

2. UI design tool API integration: link with design tool APIs such as Figma and Adobe XD to visualise the generated UI proposals and make them available for preview; prototypes can be generated within the design tool based on the AI-generated prompts, so that users can immediately check 3. feedback loop: a feedback loop that enables the user to preview the generated UI proposal.

3. feedback loop: users can provide feedback on the generated UI prompts, allowing the AI to learn improvements and suggest further optimised designs for the next time around.

<Example implementation>

An example of an implementation that uses prompt generation to dynamically adjust UI design is optimising the display and placement of UI elements according to user input and situation. The following is a basic implementation example of dynamically adjusting the UI design through prompt generation.

1. prompt generation based on user needs: when a user enters requirements for the chatbot, prompts are generated based on those requirements. For example, if the user indicates ‘more visually prominent buttons’, the following prompts are generated.

"To attract the user's eye, increase the size of the button by 1.5 times and set the background colour to a bright blue. Also, add a shadow effect to the button!"

2. reflect prompts in the UI design: set design attributes based on the generated prompts and apply them to the UI elements in code. The following is an example of implementation in CSS.

/* CSS based on prompt generation */
.highlight-button {
  background-color: #007BFF;
  color: #FFF;
  font-size: 1.5em;
  padding: 10px 20px;
  box-shadow: 0px 4px 8px rgba(0, 0, 0, 0.2);
  transition: transform 0.3s ease;
}

.highlight-button:hover {
  transform: scale(1.1);
}

3. example implementation in JavaScript: if you want to receive prompts in JavaScript and change attributes dynamically, do the following

// Function to dynamically set attributes from generated prompts.
function applyDynamicStyles(element, styles) {
    for (const [property, value] of Object.entries(styles)) {
        element.style[property] = value;
    }
}

// Style generated by prompt
const buttonStyles = {
    backgroundColor: "#007BFF",
    fontSize: "1.5em",
    padding: "10px 20px",
    boxShadow: "0px 4px 8px rgba(0, 0, 0, 0.2)"
};

// Apply styles dynamically to buttons.
const button = document.querySelector('.highlight-button');
applyDynamicStyles(button, buttonStyles);

4. testing and adjusting the UI design: once design changes have been made through prompts, actually display them to users to check their effectiveness. Conduct usability tests to help optimise the accuracy of prompt generation and UI adjustments.

Applications: by applying these methods, the chat UI, navigation and notification messages can also be dynamically adjusted according to the user’s situation.

2: UI operation via natural language interface

<Configuration overview>

1. natural language interface: the user can instruct the customisation of the interface in natural language, e.g. ‘change the colour of the side menu to red’ or ‘increase the size of the buttons a little’, allowing the user to control the details of the UI.

2. command analysis and processing by the generative AI: instructions are sent to the generative AI, which analyses them and converts them into appropriate changes (e.g. CSS property changes or HTML structure adjustments).

3. real-time UI updates: the results of the AI analysis are immediately applied to the UI and the display is updated in real-time. This system is often used in combination with front-end frameworks (React, Vue, Angular, etc.) in particular, to build an interactive UI that allows users to immediately check the results of changes.

<Example implementation>

In an implementation example where the UI is dynamically controlled using natural language, the user gives instructions to the interface using natural language, and the UI changes in real-time accordingly. This section describes how design layout and display elements can be adjusted based on user speech and input.

2.1. Natural Language Understanding (NLU) set-up: First, Natural Language Understanding (NLU) is used to analyse the user’s natural language input and understand their intentions, e.g. OpenAI API or Dialogflow as NLU engines.

When a user types ‘make the button stand out’ or ‘make the background blue’, NLU analyses these instructions and translates them into appropriate design elements.

2.2. defining intents and entities: extracting intents (user objectives) and entities (target elements of the UI) from natural language instructions.

• • Intents: actions for the UI, such as ‘highlight’, ‘change layout’, ‘hide’, etc.
  • Entities: specific parts of UI elements such as ‘button’, ‘background’, ‘text colour’, etc.

2.3. implementation of UI control scripts: real-time UI control in JavaScript based on NLU analysis results.

// UI update functions based on intents and entities obtained from NLUs
function updateUIBasedOnCommand(intent, entity, value) {
    const element = document.querySelector(entity);
    
    if (!element) return;

    switch (intent) {
        case 'highlight':
            element.style.backgroundColor = value || '#FFD700'; // Emphasis in gold colour.
            element.style.boxShadow = '0px 4px 8px rgba(0, 0, 0, 0.3)';
            break;
        case 'changeBackground':
            document.body.style.backgroundColor = value || '#0000FF'; // Default is blue.
            break;
        case 'hide':
            element.style.display = 'none';
            break;
        case 'show':
            element.style.display = 'block';
            break;
        default:
            console.log("Unknown command");
    }
}

// Example: update the UI based on commands from the user
const userCommand = {
    intent: 'highlight',
    entity: '.button',
    value: '#FF6347' // Specify colour (tomato colour).
};

updateUIBasedOnCommand(userCommand.intent, userCommand.entity, userCommand.value);

2.4. testing natural language UI controls: test various natural language commands from the user to see if the intentions are correctly reflected in the UI. Examples include ‘make the button red’ and ‘hide the header’ to see if they are properly processed and reflected in the UI.

Application example: interface operation using a chatbot

In some cases, the UI can be controlled in natural language via a chatbot. In this method, when a user types in an instruction such as ‘make this image larger’, the chatbot analyses the instruction and makes the corresponding UI change. In this way, voice assistants and real-time operation feedback become possible.

3: UI optimisation system based on user behaviour data

<Configuration overview>

1. behavioural data collection module: tracks user clicks, scrolling, time spent, etc., and sends the data to the generative AI for use.

2. UI optimisation proposals by the generative AI: Learns user intentions and behaviour patterns from the behavioural data collected, and automatically proposes moving frequently used menus to more visible positions or deleting unused buttons.

3. dynamic UI component rearrangement: the arrangement and design of the UI is dynamically updated based on the AI’s suggestions. A personalised UI experience is provided according to user behaviour, improving user engagement.

<Example of implementation>

Implementation of UI optimisation based on user behavioural data, whereby behavioural data such as user clicks, scrolling and time spent on the page are collected and analysed to dynamically adjust the UI. This can improve the user experience, increase conversion rates and user satisfaction.

The following is an example of an implementation of UI optimisation using user behaviour data.

1. collecting behavioural data: use JavaScript to monitor user behaviour (clicks, scrolling, time spent, etc.) and collect data. For example, this could include tracking the number of clicks on a particular button or the depth of scrolling.

// Event listeners for collecting behavioural data.
document.addEventListener('click', function(event) {
    const target = event.target;
    if (target.matches('.cta-button')) {
        trackUserAction('click', 'cta-button');
    }
});

window.addEventListener('scroll', function() {
    const scrollDepth = window.scrollY / (document.body.scrollHeight - window.innerHeight);
    trackUserAction('scroll', 'scrollDepth', scrollDepth);
});

// Transmission of user behaviour data to the server.
function trackUserAction(action, element, value = null) {
    fetch('/track', {
        method: 'POST',
        headers: {
            'Content-Type': 'application/json'
        },
        body: JSON.stringify({ action, element, value, timestamp: new Date() })
    });
}

2. data analysis: analyses the data collected on the server side. For example, if a particular button is not clicked, it is possible that the button is in an awkward position to be seen, and hints can be given on how to change its placement.

Based on the analysis, the following conclusions can be drawn.

• • ‘Many users leave when the scroll position is less than 50 per cent of the way down.’
  • ‘Certain buttons are not clicked’

3. actions for UI optimisation: based on the analysis results, implement UI optimisation. For example, if many users leave after less than 50% scroll position, reduce the amount of information and make the CTA button immediately visible.

// Dynamic adjustment of the UI based on conditions
function optimizeUI(data) {
    if (data.scrollDepth < 0.5) {
        const ctaButton = document.querySelector('.cta-button');
        ctaButton.style.position = 'fixed';
        ctaButton.style.bottom = '10px';
        ctaButton.style.right = '10px';
    }

    if (data.buttonClickRate < 0.05) { const targetButton = document.querySelector('.cta-button'); targetButton.style.backgroundColor = '#FF5733'; // 鮮やかな色で目立たせる } } // サーバから解析データを取得しUIを最適化 fetch('/get-optimization-data') .then(response => response.json())
    .then(data => optimizeUI(data));

4. optimisation AB testing: based on the data collected, try out different designs (e.g. CTA button placement and colours) and AB test them to find the most effective design. After testing, compare the data and reflect the most effective version in the production environment.

Application example: real-time personalisation

A more advanced application is to display a personalised UI based on historical behavioural data each time a user views a page. If it is determined that a user is interested in a particular product category, the layout is changed based on the user’s interests, e.g. by displaying product information in that category first.

4: Dynamic display of real-time generated content

<Configuration overview>

1. real-time content generation API: generates custom content such as product lists and news in real-time using generative AI. For example, AI estimates and suggests products of interest based on user behaviour and past purchase history on an e-commerce site.

2. custom UI components: AI-generated content is dynamically displayed in dedicated UI components, utilising frameworks such as React and Vue, which allow users to view new content in real-time without reloading.

3. continuous user feedback loop: feedback on whether the content is in line with the user’s interests is collected, enabling the AI to make further personalised suggestions next time.

<Example of implementation>

Implementations that generate and display content in real-time are used in a variety of situations, such as chat messages, live feeds and the display of dynamic UI elements. The following are basic implementation examples of generating and displaying real-time content on the screen in response to user input and events.

1. setting up real-time communication using WebSockets: real-time communication between server and client is possible by using WebSockets. This section describes an example where new content is immediately sent to the client as soon as it is generated by the server.

// Initialise WebSocket connections.
const socket = new WebSocket('wss://yourserver.com');

// Processing when a message is received via WebSocket.
socket.addEventListener('message', function(event) {
    const data = JSON.parse(event.data);
    displayContent(data);
});

// Function to display new content
function displayContent(data) {
    const contentArea = document.getElementById('content-area');
    const newContent = document.createElement('div');
    newContent.className = 'content-item';
    newContent.innerText = data.message;
    contentArea.appendChild(newContent);
}

2. content generation (server side): whenever new content is generated on the server side, it is sent to the client. For example, this can be implemented using Node.js and WebSockets.

const WebSocket = require('ws');
const wss = new WebSocket.Server({ port: 8080 });

// Simulation of content generation
function generateContent() {
    return { message: `New content generated at ${new Date().toLocaleTimeString()}` };
}

// Processing when a new client connects.
wss.on('connection', function(ws) {
    setInterval(() => {
        const content = generateContent();
        ws.send(JSON.stringify(content));
    }, 5000); // Generates new content every five seconds.
});

3. real-time content generation in response to user input: if the content is displayed in real-time in response to user input, the UI is updated in response to input events. In the example below, the display area is updated in real-time each time the user enters text.

<input type="text" id="user-input" placeholder="Type here..."> 
<div id="display-area">
</div> 

<script>
document.getElementById('user-input').addEventListener('input', function(event) { 
const displayArea = document.getElementById('display-area'); 
displayArea.innerText = event.target.value; }); 
</script>

4. example implementations of live feeds and chats: live feeds and chats, which display messages and notifications in real-time, communicate with the server via WebSockets or periodic polling to retrieve and display the latest content.

// Functions for sending chat messages
function sendMessage(message) {
    socket.send(JSON.stringify({ message }));
}

// Event listeners for message input forms.
document.getElementById('chat-form').addEventListener('submit', function(event) {
    event.preventDefault();
    const input = document.getElementById('message-input');
    sendMessage(input.value);
    input.value = ''; // Clear input fields.
});

5. optimise the display of content and optimise performance: when dealing with large amounts of real-time data, maintain performance by deleting old data where appropriate and prioritising the display of new data. For example, if more than a certain number of messages are displayed, delete the first message.

function displayContent(data) {
    const contentArea = document.getElementById('content-area');
    const newContent = document.createElement('div');
    newContent.className = 'content-item';
    newContent.innerText = data.message;
    
    // Latest content added.
    contentArea.appendChild(newContent);

    // Limit of 100 pieces of content to be displayed.
    if (contentArea.children.length > 100) {
        contentArea.removeChild(contentArea.firstChild);
    }
}

Application: real-time content generation using AI

A further application is the use of AI models for automatic response and content generation based on user input. User input triggers the AI to immediately generate responses and related content, which is then displayed on the screen in real-time.

reference book

The respective reference books are described below.

1. prompt generation and natural language UI control:.

– 『Designing Voice User Interfaces: Principles of Conversational Experiences』
Written by Randy Allen Harris
This book provides a comprehensive overview of the fundamentals and applications of interface design in natural language. It teaches the concepts for dynamically controlling the UI via speech and text input.

– 『Natural Language Processing with Transformers』
By Lewis Tunstall, Leandro von Werra and Thomas Wolf
Deals with the basics and applications of prompt generation using natural language processing (NLP), in particular how to control the UI with natural language and practical knowledge on the implementation of prompt generation.

– 『Conversational Design』
Written by Erika Hall.
A book on the basics and applications of natural language interface design, with a wealth of design concepts and practical examples for interactively controlling UIs.

2. user behaviour data-based UI optimisation:

– 『Designing for Interaction: Creating Innovative Applications and Devices』
Written by Dan Saffer.
A comprehensive overview of how to optimise interaction design through the analysis of user behaviour, providing practical guidance on how to improve design.

– 『Lean Analytics: Use Data to Build a Better Startup Faster』
By Alistair Croll and Benjamin Yoskovitz
This book on using data to improve and optimise products details an approach to UI optimisation based on user behaviour data.

– 『Web Analytics 2.0: The Art of Online Accountability & Science of Customer Centricity』
By Avinash Kaushik.
Provides a methodology for UI optimisation through data collection and analysis. Provides a detailed description of effective optimisation methods based on user behaviour data.

3. real-time content generation and display:

– 『Real-Time Web Apps: With HTML5 WebSocket, PHP, and jQuery』
Written by Jason Lengstorf and Phil Leggetter
This book covers implementation techniques for real-time communication and dynamic content generation using WebSockets and other methods, and in particular how to implement UIs that require real-time updates, such as chat and notifications.

– 『Building Progressive Web Apps』
Written by Tal Ater.
Provides useful knowledge for designing real-time interactive web apps. Suitable as a reference for implementing real-time UI performance optimisation and offline support.

– 『JavaScript and JQuery: Interactive Front-End Web Development』
Written by Jon Duckett
Provides the basic and applied knowledge required to generate real-time content and update dynamic UIs using JavaScript and jQuery, and is useful for implementing UIs that reflect data in real-time.