AI for UX Design: From Apps to AI-Driven Natural Language Interfaces

This article examines the development of AI for UX design, emphasizing the recent move towards artificial intelligence (AI)-driven natural language experiences.

Our interactions with digital devices and software have changed significantly in the quickly changing world of technology. User interfaces have evolved over time to satisfy the shifting demands and expectations of users, from the early days of command-line interfaces to the experiences of today powered by AI. This article examines the development of user interfaces, emphasizing the recent move towards artificial intelligence (AI)-driven natural language experiences.

The Evolution of UI 

The history of user interfaces is a story of increasing accessibility and intuitiveness. As illustrated in the timeline above, we've seen several major shifts:

1. 1960s - Command Line Interface (CLI): UNIX shells ushered in the age of text-based communication. To communicate with computers, users had to commit specific commands to memory, which limited computing access to specialists and hobbyists.

2. 1970s - Early Graphical User Interfaces (GUIs): Xerox PARC pioneered the development of GUIs, introducing concepts like windows, icons, menus, and pointing devices (WIMP). This marked the beginning of more intuitive computer interactions.

3. 1980s - Mainstream GUI:  With the release of Apple's Macintosh, GUIs became widely available, opening up computers to a far larger audience. In the realm of personal computers, the desktop metaphor emerged as the dominating paradigm.

4. 1990s - Web Interfaces: The introduction of a new paradigm for networked information access was brought about by the World Wide Web and browsers such as Mosaic. This era saw the birth of many interface conventions we still use today in web design.

5. 2000s - Mobile Touch Interfaces: The launch of the iPhone in 2007 revolutionized mobile computing, introducing multi-touch interfaces and gestural interactions. This shift made powerful computing truly portable and personal.

6. 2010s - Voice Assistants: The 2007 release of the iPhone brought multi-touch interfaces and gesture interactions to the world of mobile computing. Powerful computing became genuinely portable and personal with this change.

7. 2020s - AI-Driven Interfaces: The current era is seeing the rise of AI-powered interfaces like ChatGPT and Claude, which can understand and respond to natural language in sophisticated ways. This represents a convergence of natural language processing, context awareness, and adaptive interfaces.

Each of these stages has built upon the previous ones, gradually reducing the cognitive load required to interact with technology. The progression has been towards interfaces that are more intuitive, more aligned with natural human communication, and more capable of understanding and anticipating user needs.

The Rise of AI-Driven Interfaces

The latest revolution in user interfaces is being driven by advancements in Artificial Intelligence, particularly in the field of Natural Language Processing (NLP). This shift is moving us beyond the app-centric world we've used to, towards more integrated, language-based experiences.

AI-driven interfaces, integrate several key components:

1. Natural Language Processing (NLP): Allows the system to understand and interpret human language.

2. Context Awareness: Enables the AI to consider the user's current situation, preferences, and history.

3. Action Execution: The ability to perform tasks across multiple apps or services.

4. User Input: Accepts various forms of input, including text, voice, and potentially even gestures.

This new paradigm is breaking down the barriers between individual apps, creating more seamless and intuitive user experiences.
Let’s see a couple of examples to have a better understanding of what changes this has led to.

Jasper AI and Claude

To illustrate the power of AI for UX design, let's look at two examples: Jasper AI and Claude.

Jasper AI

Jasper AI is a content creation platform that uses advanced language models to assist users in writing various types of content. Its interface represents a significant departure from traditional word processors:

1. Natural Language Commands: Users can instruct Jasper using plain English, such as "Write a blog post about the benefits of meditation."

2. Context-Aware Suggestions: Jasper understands the context of what's being written and can offer relevant suggestions and continuations.

3. Multi-Format Output: The AI can adapt its output to various formats, from blog posts to social media updates, without the need for separate apps.

What happens and we will see more in detail, it’s how the application is able to become a friendly collaborative tool and with which the user is able to adjust on the fly. In fact a key component in Jasper AI, but also in Custom GPTs and other AI tools is the ability to input the preferences and define their properties.

Let’s also see another key example that shows how the UX for AI-driven interfaces is still extremely under development.

Claude

Claude, an AI assistant created by Anthropic, showcases another dimension of AI-driven interfaces:

Conversational Interaction: Users can engage with Claude in a natural, conversational manner, similarly to other products such as ChatGPT, Gemini.

1. Task Flexibility: Claude can handle a wide range of tasks, from answering questions to helping with analysis and coding, without requiring different interfaces for each task type.

2. Context Retention: Claude can maintain context throughout a conversation, allowing for more coherent and productive interactions. 

3. More collaborative GUI: Claude has also integrated a more collaborative GUI, by offering not only a chat-like interface, but allowing the user to see real time generated-schemes, summaries, and react application from the code. This can already be seen as a second step towards the adoption of new GUIs in AI.

While the first three points are already (almost) thought to be taken for granted when speaking of new chatbot interfaces, the fourth one could be already seen as a step forward  a new ability of these models, that needs to help users not to get lost in the amount of information that could be given to them, but helping to highlight the most important information or helping enriching the result.

In general both Jasper AI and Claude demonstrate how AI is reshaping user interfaces to be more intuitive, flexible, and powerful.

Context Bundling and User Curation

Two key concepts emerging in AI-driven interfaces, that we have partly seen in the example above are context bundling and user curation.

Context Bundling

Context bundling refers to the ability of AI interfaces to combine related information and actions into a single, streamlined interaction. This concept addresses the challenge of conveying complex instructions to achieve desired outcomes.

Let’s take an example, a simple user command like "Plan my vacation" triggers a series of actions across multiple domains (calendar, preferences, weather, flights, hotels). The AI bundles all these contexts to provide a comprehensive vacation plan. While we are still used to using a specific app for a specific task, the adoption of a context enriched environment with the possibility to directly interact with multiple sources, speed up and improve the usability.

Think for example of the first version of ChatGPT in which there was no possibility of giving context apart from the conversation history. Now it has changed to give more context and bundle it with the possibility to connect to your folder, upload documents or search on the web for additional sources of information.

User Curation

User curation involves allowing users to refine and personalize their AI interactions over time. This idea acknowledges that although preliminary AI results might not satisfy users entirely, they offer a starting point for additional improvement.

For instance, in a situation involving the creation of content utilizing Jasper AI, a user may choose to highlight particular words or concepts from an initial output, utilizing these as starting points for more content creation or improvement.

Or taking the GPTs environment you can see how we are now able to instruct giving the right context in order to reduce the friction in not having the expected response.

Designing for Trust in AI Interfaces

We are getting more and more used to play with AI interfaces, the problem arise when dealing with crucial tasks, entrusting with a more prominent role and access to our sensitive data

As AI takes on a more key role in user interfaces, designing for trust becomes crucial. The key properties that we have to take into account will be:

1. Transparency: Clearly communicate what the AI is doing and why. Not having full control of what is happening can deeply affect the user experience, using AI interfaces we are delegating sub tasks, and when it comes to more sensible data, communication becomes key to know what is happening.

2. Control: Give users the ability to verify and modify AI outputs. Starting from a response it should be possible to build upon it and refine the output 

3. Privacy: Implement strong data protection measures and give users control over their data. What if we can access a bank account directly through a chatbot interface, we would need to be sure that all the data is protected and that we are aware of what is happening.

4. Consistency: Ensure that AI behaviors are predictable and align with user expectations. Errors should be easily put under control, otherwise frustration can arise.

5. Error Handling: Manage and communicate when the AI makes mistakes or is uncertain.

The Future: Context Ecosystems and Multimodal Interactions

As we peer into the future of human-computer interaction, two interrelated trends stand out: the rise of context ecosystems and the dominance of natural language interfaces. Let's explore these concepts in more detail.

1/ Context Ecosystems

AI interfaces are operating on a new paradigm thanks to context ecosystems. Rather than operating only inside the boundaries of specific apps or services, these systems will be cross-platform compatible, resulting in a unified, connected experience.

Within this particular ecosystem:

1. Comprehensive Perception: The AI core at the centre can access information and functions from the job, home, health, and social spheres of an individual's life. This makes it possible to comprehend the user's context completely.

2. Cross-Domain Optimisation: The AI can make more wise choices and recommendations if it comprehends how many facets of life interact with one another. For example: The AI may recommend scheduling time for a fast workout or a walking meeting if it notices a busy work schedule (Work context) and deteriorating activity metrics (Health context).It may automatically change the home thermostat and postpone dinner preparations if a late meeting is scheduled (Work context).

3. Predictive Actions: With access to multiple data streams, the AI can anticipate needs more accurately. If it notices a pattern of late nights at work leading to skipped gym sessions and unhealthy eating, it might proactively suggest meal prep services or home workout options.

4. Smooth Transitions: When users move between contexts (e.g., from work to home), the AI may make sure that the transition goes smoothly. It can do this by, for example, gradually switching the focus of notifications from work to personal or by readying the home according to the user's condition when they leave work.

5. Integration with Privacy Consciousness: The ecosystem is interconnected, yet the AI must be mindful of its bounds. Users should to be in charge of what data is shared and how it is utilised in different circumstances.

The power of context ecosystems lies in their ability to break down the artificial barriers we've created between different aspects of our lives, offering a more holistic and supportive technological environment.

2/ Multimodal interactions

As natural language processing (NLP) continues to advance, we're moving towards a future where Natural language—spoken and written—will become the primary means of communication between humans and our technologies and artificial intelligence (AI) assistants.

This transition to natural language interfaces is defined by a number of significant advancements:

1. Conversational user interfaces (UI): Users will be able to communicate with their devices in a way similar to that of a human assistant as interfaces grow more conversational. To book a meeting and order lunch, for instance, a user might say, "Schedule a team meeting for tomorrow at 2 PM and order lunch for everyone," rather than having to go through several apps.

2. Context-Aware Interactions: Natural language processing (NLP) systems will get better at comprehending context, enabling more fluid and productive interactions. They'll be able to deduce intentions, recall prior exchanges, and comprehend requests that are subtle or implied.

3. Multimodal Interactions: These interfaces will incorporate additional modes of interaction when necessary, however language will always be the main medium of communication. For example, a user may request to view their calendar and then make changes using a touch or gesture.

4. Adaptive Language Models: Over time, AI assistants will adjust to each user's unique speech patterns, vocabulary, and preferences, resulting in a more tailored engagement.

5. Cross-Lingual Capabilities: With advanced natural language processing (NLP), users will be able to communicate with people in their native tongue while still interacting in their preferred language.

6. Proactive Interactions: AI assistants may, like human assistants, start up conversations based on recognised opportunities or needs rather than waiting for orders.

The app-free phone showcased by Deutsche Telekom at the 2024 Mobile World Congress is an early indicator of this trend as the rabbit R1 which similarly performs tasks across various apps. Yes, they are the first trials of surfing this trend with many possible defects and imprecisions but they summarize a possible vision of the future. Instead of navigating through a series of apps, users would simply express their needs in natural language, and the AI would coordinate the necessary actions across various services and platforms.

Credits: Reuters, CNET

Implications and Challenges

While these developments promise significant improvements in user experience and productivity, they, of course, also come with challenges:

1. Privacy and Security: With AI systems having access to more personal data across various contexts, ensuring robust privacy protections and data security becomes crucial.

2. User Control: As AI systems become more proactive, maintaining the right balance between helpful assistance and user control will be essential.

3. Accessibility: While natural language interfaces can make technology more accessible for many, they may present challenges for users with speech impairments or in noisy environments. Ensuring multiple modes of interaction remains important.

4. Cultural Sensitivity: As these systems operate across cultural and linguistic boundaries, they'll need to be designed with cultural nuances and sensitivities in mind.

5. Transparency: As AI makes more decisions on behalf of users, ensuring transparency in its decision-making processes becomes increasingly important.

The Impact on User Experience

The user experience has been significantly impacted by this evolution:

Every advancement in technology, from CLI to AI, has increased accessibility to a wider audience. Using natural communication, one can now perform tasks that formerly required specialized knowledge. We are moving away from an app-centric world and towards a language-centric one.

With the help of contemporary interfaces, users may do difficult activities faster and with less mental strain. A voice command to an AI assistant, for example, can take the place of several steps in typical app interface navigation, and as interfaces have developed, they have grown increasingly cognizant of user context. In order to deliver more relevant and customized interactions, AI-driven interfaces can take into account variables like user history, preferences, location, and even emotional state.

The newest interfaces allow for more than one way to engage. Text, speech, touch, and even gesture inputs can all be combined with ease, giving users the flexibility to communicate anyway seems most comfortable or natural at the time. Modern AI-driven interfaces are not limited to reacting to user input; instead, they can anticipate user needs and make recommendations or take proactive actions. One such example is Claude and the artifacts, which can generate and run applications directly for the user, thereby providing a significant step towards meeting yet unmet needs.

Looking Ahead: The Convergence of Interfaces

We should expect to witness a confluence of these different interface paradigms in the future. In the future, it might not be necessary to decide between speech, GUI, or AI; instead, these modalities could be seamlessly combined to produce rich, context-aware experiences.

Interfaces utilizing speech, AI, and GUI are merging to produce a more comprehensive user experience. The distinctions between various interface modalities become less clear in this future, enabling more effortless and productive interactions.

Speaking with an AI assistant to begin a task, for instance, may be followed by a smooth transition to manipulating data on a graphical interface, all the while the AI keeps offering context-aware actions and recommendations. The future of user interface design is represented by this convergence, which promises more potent, intuitive, and consistent user experiences.

Conclusion

The transition from command-line to AI-driven user interfaces is indicative of a move towards more powerful, intuitive, and natural human-computer interaction. As we've seen, this evolution has progressed by building on the advantages of each step while resolving their drawbacks.

The smooth integration of several modalities—visual, audio, and conversational—supported by intelligent artificial intelligence (AI) that can recognise context, predict needs, and adjust to specific users is the key to the future of user interfaces. This convergence holds the prospect of improving technology's efficiency, accessibility, and cognitive fit with humans.

Approaching the dawn of a new era, it is imperative that developers, designers, and companies embrace these shifts. Businesses who can successfully employ AI to build user experiences that seem more like dealing with an intelligent collaborator and less like using a tool will be the ones that survive in the years to come.

The transition from applications to natural language interfaces is a fundamental rethinking of our relationship with technology, not just a change in technology. We are getting closer to a time when technology actually comprehends and anticipates human wants, blending into every part of our life as we keep pushing the envelope of what's possible.