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10 ethical challenges of AI

1. Transparency

AI systems must be transparent and their decisions understandable to users. This involves clearly explaining and making visible how AI systems make decisions. The challenge is to balance the complexity of AI algorithms with the need for them to be interpretable by humans, regardless of their digital literacy.

2. Equality and biases

AI systems can perpetuate existing biases or introduce new ones if they are trained incorrectly or with biased data. Ensuring fairness involves careful curation of training data and continuous monitoring to detect and mitigate any biases present.

3. Privacy and data protection

AI systems often rely on large datasets, which may include sensitive personal information. Protecting this data and ensuring it is used ethically is a major challenge. Therefore, we as designers must understand how to implement data protection measures and ensure respect for user privacy.

4. Accountability

As AI systems become more autonomous, it becomes more difficult to identify accountability behind any action. This also raises legal and moral challenges when AI systems have a negative impact or make wrong decisions.

5. Human oversight and control

Maintaining human control over AI systems is essential to avoid unintended consequences. This involves creating ways to monitor and control these systems, ensuring continuous improvement and that there is room for human decision-making rather than replacing it.

6. Safety

These systems must be safe and reliable, particularly when applied in sensitive contexts and critical areas such as healthcare. This requires rigorous testing and validation to ensure everything works as expected.

7. Social Impact

The impacts on various social areas, such as employment, must be considered. Those who design for AI need to assess potential social pros and cons, with the goal of improving societal well-being while minimizing negative impacts.

8. Ethical decision-making

Unlike traditional software, AI can involve decision-making with moral implications. It is therefore important to align with human ethical standards, which is a complex and ever-evolving challenge.

9. Upholding human values

AI must be designed and used to strengthen, not weaken, human values such as dignity, respect, and autonomy, involving the social sciences, engineers, policymakers, and other stakeholders to ensure these technologies are developed and implemented responsibly and ethically.

10. Global and cultural considerations

AI-powered systems are being used all over the world, and their design must take into account cultural differences and global contexts to ensure they are fair, inclusive, and respectful of local norms and values.

These challenges highlight the need for multidisciplinary work when designing AI systems. Bringing together perspectives from design, technology, ethics, law, and social sciences is the only way to ensure AI is developed in a way that is truly beneficial to society.

Any doubts? Leave them in the comments.

Conversational AI refers to technologies such as chatbots: programs or applications that simulate a conversation with human users, either through text or voice, and virtual assistants that enable natural language processing and understanding. All of this relies on large volumes of data and models trained to replicate human interactions.

The first chatbot

ELIZA is the name of the first publicly known chatbot, created in 1966 by Joseph Weizenbaum at MIT.

It is one of the earliest examples of a natural language processing program and was developed for a healthcare context, designed to simulate a conversation between a therapist and a patient.

The negative "legacy" of chatbots

When talking about conversational AI, we need to address the typical chatbots and their legacy. For a long time, interacting with chatbots was terrible — and you probably remember those days.

These conversational agents are nothing like today's, as they were built with deterministic scenarios, based on decision trees and simple algorithms. With the generative AI revolution, conversational agents gained new attributes, more elasticity, and consequently, more intelligence.

However, much of what users experienced in the past still lives on in their memory and mental models. This last point raises another challenge. Those who used old chatbots, or who are outside the tech bubble, tend to maintain the mental model of searching by keywords rather than conversing naturally.

As soon as the user realizes they are interacting with a system, they apply what they know best: system language.

In recent years, we have seen this mental model shifting, allowing for more natural conversations between humans and AI. Still, recognizing this obstacle is essential to designing good experiences with conversational agents.

How conversational AI works

A conversational AI uses a combination of technologies that allow it to reason, understand intentions, and maintain a human-like conversation, such as:

• ML (Machine Learning) — automatic learning

• DL (Deep Learning) — deep learning

• NLP (Natural Language Processing) — natural language processing

• NLU (Natural Language Understanding) — natural language understanding

• NLG (Natural Language Generation) — natural language generation

The various "modes" of conversational AI

Designing a conversational AI requires considering a set of functional modes:

• Voice-to-voice

• Text-to-text

• Mixed mode, which converts voice to text and vice versa (Kusal et al., 2022)

These conversations can occur in voice interfaces, such as Siri or Google Assistant, in graphical interfaces, or in a combination of both.

How to design for conversational AI

I've been sharing some snacks on conversational design, but here's a more holistic view of important points:

• Evaluate whether conversational is truly the best approach
  Ask yourselves: is this a task people would naturally do out loud? Is it a topic they already talk about?

• Analyze what is most practical and natural for the user
  Speak or type? Or would a traditional interface be better?

• Start with prompt flows
  You can learn more about them in this post.

• Simulate and test conversations

• Think about edge cases
  Conversational is probabilistic, unlike traditional design, which is deterministic. Avoid defining everything rigidly and explore possibilities.

• Include error scenarios

• Work together with engineering and data science
  Participate in prompt engineering.

• Also test the model or prompt output

• Define AI strategies together
  For example, fallback ladder or human in the loop.

• Run dogfooding or bug bashing sessions
  Ensure the entire team has the opportunity to test and explore as many cases as possible.

• Consider context, intention, and emotion in each response — this changes the perception of the experience.

Take-away

One last snack before closing 🍪
Designing for conversational AI is about understanding how people think, their mental models, and their beliefs. Deciding to create a conversational flow requires intention and several steps to ensure quality of experience.

Any doubts? Leave them in the comments.

Apps inside ChatGPT

After some experiments, ChatGPT will finally be able to call apps within the conversation so you can take actions without leaving the interface. In everyday use, this could be helpful for travel planning, house hunting, or even restaurant reservations. These are just a few cases; many others are ready to jump into the conversational context.

Is this the same as Operator?

No. Operator was an "early access" feature. Despite some e-commerce tests, it focused on autonomous agents navigating an internal browser, using existing service websites — often not prepared for AI agents. On top of that, any required authentication raised privacy and security concerns, as OpenAI did not guarantee data confidentiality.

With apps in ChatGPT, integrations move into the conversation context, and authentication happens outside the agent's browser, through a more secure and practical process. Instead of the agent going to Booking to search and return results, the results arrive natively in the conversation, in real time and fluidly.

Shared examples

These apps will be maintained by the companies themselves, significantly improving the experience compared to agents crawling regular websites. Already announced partners include Booking.com, OpenTable, Uber, Spotify, among others:

Imagine sketching a diagram and calling Figma inside ChatGPT to turn it into a functional diagram, created directly in FigJam, so you can continue working there.

Other examples included Coursera, which can bring courses into the ChatGPT interface and allow in-context questions at a specific video moment, and Canva, now also with an embedded experience inside ChatGPT.

Outros exemplos incluiram a coursera que pode trazer cursos para a interfcace do chatGPT e a funcionalidade de fazer perguntas em contexto ao momento do video, assim como o canva que agora tem também ele uma experiência embebida na app do chatGPT.

Building agents with the Agent Kit

Agent builder

The Agent Builder is a visual canvas for rapidly prototyping agents with logical steps. It makes it easier to test flows and publish agents with less friction.

Take a look at the example video shared by OpenAI:

ChatKit

ChatKit allows embedding the chat interface, connecting conversational components to agents that execute tasks, creating native experiences with visual elements and tools within the dialogue itself.

New Evals

OpenAI has been investing in Evals to assess agent quality. The discussion around how to measure agent experiences is ongoing, but OpenAI has shown a promising path, including automatic prompt optimization.

Agents + connectors

Connectors are coming to enrich ChatGPT usage, with secure connections to apps like Google Drive and GitHub. This enables searching files, creating content directly at the destination, and referencing materials in the conversation.

New Codex

The new Codex brings improvements for navigating repositories, editing files, running commands, and executing tests. For designers, perhaps the simplest option is to use Cursor or the VS Code add-on.

(These past weeks I've been trying to build an app using only Codex — I'll share the result later!)

Voice as the primary form of interaction in the future?

Sam Altman mentioned he believes voice will be the primary way of interacting with AI. The team has been working on models to reduce real-time costs. This could transform many experiences and services as we know them.

Sora news

The availability of Sora 2 on the OpenAI API was demonstrated, along with advances in image/video and audio generation (ambient sound and voices).

Another highlight was the Mattel example: from a sketch, it was possible to generate a video to visualize the final product.

What does this mean for Design?

These updates bring new challenges and opportunities for Product and Design.

• It is crucial to think about services for the "agentic era", including integrations with ChatGPT.

• Designers need to understand how agents work, their capabilities, and adapt their working framework to this reality.

• Conversational will gain weight due to technological pressure, but it is essential to balance product decisions with real usability outside the tech bubble.

• Measuring agent quality becomes increasingly important; designers should dive into metrics and qualitative evaluation of these experiences.

• Image and video generation capabilities are getting better and better, offering new ways to make ideas and concepts tangible.

One last snack before closing

OpenAI has been releasing support materials for this new era of agents. I'd highlight the practical booklet for building agents:
https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf

Any doubts? Leave them in the comments.

Generative AI has brought countless opportunities. One of them is the freedom and control to create our own apps or tools. This is already possible today for the more curious, through vibe-code or no-code platforms. Among the most recent and well-known are Lovable, V0, Bolt, and Cursor. However, what Nothing is bringing goes beyond that.

After Generative UI, we have AI-native devices

Nothing's provocation points to the hyper-personalization of operating systems — something that emerges as a next step beyond what we already know from these platforms. This vision contrasts with the current app store model, focused on ready-made solutions that serve the majority. The idea here is different: creating something 100% dedicated to each person's individual use case, in a conversational way.

Nothing's first step in that direction was launching the Essential Apps platform, which allows users to create tools and apps through conversation, but with a crucial difference — they are integrated directly into the operating system.

The future of hyper-personalization

Until now, hyper-personalization focused on using real-time data to adjust experiences or journeys. With UI generation, a new scenario opens up: apps and tools that create themselves, shaping to fit each user's context.

If we think about it carefully, the future of hyper-personalization may lie in a mix between adaptive interfaces — which respond to each user's context and goals — and the ability for users to create their own solutions within the operating system.

In other words, we won't just be proactive in offering hyper-personalization. We'll also give people the freedom to build what they feel is missing from their experience. Creating unique contexts and filling specific gaps in individual journeys.

The current challenges of the prompt → creation → iteration flow

But if this future is promising, today's reality still has several obstacles.

The last time I created an app using vibe coding tools, I always had to do some manual work to reach the final result I wanted. I felt that many back-and-forths were needed to properly instruct the model. Even so, it's undeniable that this process is faster than coding everything from scratch.

• We live in a tech bubble. Not everyone shares the same level of digital literacy, and this creates barriers. Furthermore, there are still no clear mental models to help us structure prompts capable of faithfully translating our ideas.

• Creativity doesn't always lend itself to being captured in words. Natural language is ambiguous and the same prompt can be interpreted in very different ways. That's why the first output almost never matches what we want. You need to iterate, adjust, and learn to phrase things better.

• Another challenge is the lack of structured feedback. Many tools don't allow you to change just one already-generated detail. You have to go back to the prompt and redo all the logic, which breaks the flow of the creative process.

• And there's still an invisible learning curve. Over time, we discover how to 'speak the machine's language', but that knowledge isn't documented. It's learned in practice, almost tribally, and this creates yet another entry barrier for those who aren't used to it.

How to overcome some of these challenges through prompting strategies

Normally the best strategy is to use Few-shot and Chain-of-thought in combination, but here is a general explanation of these strategies.

• Zero-shot
  Prompt: "Create an app to order pizza."
  
  ✅ The model immediately gives a basic flow: choose pizza, cart, payment.
  
  ❌ Important details may be missing (delivery tracking, ingredient customization, promotions).

• Chain-of-thought (CoT)
  Prompt: "Explain step by step how you decide the essential features of a pizza app, thinking about the cozy Italian experience. Then list the final app flow."
  
  ✅ The model will reason: "The user starts hungry → should see popular pizzas on the home screen → can customize ingredients → needs quick checkout → wants to track delivery → can save preferences for next time."
  
  ✅ Features are better justified.
  
  ❌ Longer and less immediate.

• Few-shot
  Prompt:

  • Exemplo 1: App de sushi → funcionalidades: escolher prato, personalizar molho, agendar entrega.

  • Exemplo 2: App de padaria → funcionalidades: encomendar pão fresco, subscrição semanal, notificação de pronto a levantar.

  • Exemplo 3: App de hambúrgueres → funcionalidades: toppings, combos, programa de fidelidade.

  • Agora com base nestes exemplos cria uma app para encomendar pizza: inclui funcionalidades como escolha de massa fina ou grossa, extra queijo, combos familiares, vinhos italianos, programa de pontos e tracking em tempo real.

  • ✅ O modelo percebe diferentes tipos de funcionalidades recorrentes e aplica ao negócio da pizzaria.

  • ✅ Garante riqueza e diferenciação.

  • ❌ Requer esforço de preparar exemplos.

• Chain-of-thought (CoT)
  Prompt: “Explica passo a passo como decides as funcionalidades essenciais de uma app de pizza, pensando na experiência cozy italiana. Depois lista o fluxo final da app.”

  • ✅ O modelo vai raciocinar: “O utilizador começa com fome → deve ver pizzas populares na home → pode personalizar ingredientes → precisa de checkout rápido → quer acompanhar a entrega → pode guardar preferências para a próxima vez.”

  • ✅ Funcionalidades ficam melhor justificadas.

  • ❌ Mais longo e menos imediato.

We are only at the beginning of a new era in which each person can shape their own digital experiences. Whether through interfaces that adapt to context or tools built by conversation, the goal is the same: technology that fits us, not the other way around.

Second, I'd like to thank everyone who has subscribed to this newsletter and let you know that the current address is uxsnack.com (uxsnack.design will no longer be linked to this publication).

Now then, let's go!

Designing for conversational AI opens up new challenges and opportunities to experiment with innovative methods. It's worth creating a separate post on how to start designing for conversational, but today we'll focus only on prompt flows.

We designers are familiar with user flows, right? Prompt flows are similar, but focused on dialogue examples.

Some reasons why you might want to use this method:

• No need to draw screens and mockups

• You can explore dialogues much more easily and quickly

• Macro view of the conversation, but also allows you to go into detail and see the branches

• Facilitates alignment between design, development, and product teams

But conversational is not deterministic

Even when we think about designing just the flow, we can fall into the trap of thinking we're designing, as before, for deterministic experiences — but in reality we can't think that way. Experiences with generative AI, such as conversational, are probabilistic. This means we don't know what the exact outcome of the interaction will be, as it's impossible to calculate all possible scenarios.

Still, this shouldn't stop you from exploring possible scenarios and example dialogues that help you not only prepare better for prompt engineering, but also create control guidelines (guardrails).

How to get started

• Start by identifying the main use case(s)

• Define the "baseline" of the first dialogue

• Explore possible branches and error scenarios

• Think about rich content, not just text (we'll talk about this in another post)

• Take the opportunity to think about what your AI Agent will need to do, or what data it will need to retrieve

• Consider accessibility: how will the agent respond to users with different needs?

• Test with diverse users to identify language patterns and cultural preferences

Prompt richness and intent

Capturing the user's intent is not always easy and may lead to "disambiguation". You can also try to capture strategies in prompt flows to handle these situations.

Simply put, "prompt richness" refers to the quality and clarity of the user's prompt — whether it can be well understood by the conversational agent, whether disambiguation is possible, or whether it's truly impossible to understand.

Examples of different levels of prompt richness:

• High: "I need a table for 4 people tomorrow at 8pm, we have a child who is allergic to gluten"

• Medium: "Table for tomorrow night"

• Low: "Dinner"

I'll talk about strategies and frameworks for getting better results with conversational AI agents in a separate post!

A communication and technical alignment tool

We used to use mockups and screens to discuss with technical teams, in addition to that being our main handoff. When designing for conversational, you need to go beyond that — prompt flows are an excellent communication tool for technical teams and can even serve as a test.

Tips for technical alignment:

• Document the types of data needed for each response

• Specify required APIs or integrations

• Define success metrics for each interaction

• Establish timeouts and fallbacks for error scenarios

• Consider rate limiting limitations and API costs

Once again, conversational is probabilistic — we don't know what will happen, but we should still anticipate some scenarios in order to instruct the LLMs and conversational agents according to what we consider to be the best experience. Prompt flows are allies in that mission.

How far to guide the conversational agent

We can get into an endless discussion about how far to "guide" conversational agents, since good human-AI conversations in terms of conversation also want to feel natural and intelligent. For that, we need to keep in mind that we must leave some freedom for the agent to respond in the best way — not just rigidly following our rules.

Considerations on diversity and inclusion:

• The agent should recognize and respect different forms of communication

• Avoid assuming genders, family relationships, or cultural contexts

• Provide alternatives when communication is unclear

• Take into account different levels of digital literacy

Testing and iteration

Recommendations for testing your prompt flows:

• Test with real users from different backgrounds

• Simulate stress scenarios (multiple requests, contradictory information)

• Validate with technical stakeholders the feasibility of the interactions

• Document failure patterns to improve guardrails

• Measure metrics such as resolution rate, satisfaction, and interaction time

As mentioned earlier, I'll have a dedicated post on this topic where we'll discuss some strategies such as the fallback ladder and other advanced techniques.

Welcome to the Agentic Era

Is this the first time you've heard of the "Agentic Era"? Let's get you up to speed. In 2025, AI agents are taking on a central role, transforming the way we interact with technology.

Remember the phrase "The best interface is no interface"? Well, that may be truer than ever. AI agents are evolving to execute tasks in the background, reducing the need for traditional interfaces like screens, menus, and buttons.

Imagine simply saying: "Organize my trip to London," and your AI agent takes care of everything for you — no clicks, no searches, no interfaces. This shift suggests that, over the next 10 to 20 years, interface design as we know it may become less relevant.

But before we go further, let's clarify the difference between AI assistants and AI agents.

AI Assistants vs. AI Agents: What's the difference?

Key Characteristics of AI Assistants

• Reactive: They wait for user commands before acting.

• Conversational: They interact in dialogue, answering questions and helping in conversations.

• Simple tasks: They execute direct tasks, such as fetching information or setting reminders.

• User-dependent: They require constant guidance and input.

• Everyday utility: They help with common, quick tasks.

• Context understanding: They use NLP (Natural Language Processing) to provide relevant responses.

• Easy to use: They feel like a conversation with a friend — no complex setup needed.

Key Characteristics of AI Agents

• Autonomous: They initiate and complete tasks without constantly depending on the user.

• Goal-oriented: They work toward broader objectives, such as researching trends or planning entire projects.

• Proactive thinking: They break complex tasks into steps and execute them independently.

• Handle complexity: They deal with intricate problems, from analyzing user data to generating multiple design options.

• Scalable: They can manage multiple tasks simultaneously or focus intensely on just one, as needed.

• Learn and adapt: They integrate external information (such as online research) to refine their actions over time.

• Operate silently: They run in the background and only intervene when necessary.

What changes in User Experience (UX)?

1. Design for AI interaction
   Designers will need to create interfaces that facilitate effective communication between users and AI agents. This includes conversational design and proactive interactions (AI-First UX Design in 2025: Shaping Smarter User Interactions), where the agent anticipates needs and acts on behalf of the user.

2. Adaptive user experience
   As agents learn and adapt with use, the experience will evolve over time. Design must anticipate this dynamic to preserve usability (The Future of UX Design: Adapting to a World of AI Agents).

3. Ethical considerations
   The growing use of autonomous agents brings concerns about privacy, bias, and transparency. It's essential to create systems that explain AI decisions and build user trust (UX of AI).

4. Required technical knowledge
   Designers must understand capabilities such as natural language processing, data analysis, and autonomous decision-making to effectively integrate AI agents into products (AI capabilities and examples).

How Mental Models Will Change

In this new era, it's essential to consider how users perceive and trust AI agents. Users will need to understand and trust AI capabilities in order to leverage their full potential. This will require a shift in mental models from traditional interfaces to more conversational and autonomous interactions.

We are also dealing with dynamic systems that learn and adapt, rather than static interfaces. This means the user experience will evolve over time, requiring designers to think more about long-term adaptability and less about fixed solutions.

Final thoughts

These are exciting times! As designers, we need to stay alert to this shift and be prepared to adapt our practices and skills. The Agentic Era is not just a technological change — it's a paradigm shift in how we think about interaction and product design.

Are you ready for this new era?

Today's Snack: 5 types of Human-AI interaction 🍪

Looking at the 5 types of Human-AI interaction (Figure 1), proposed by Silvana Hinsen, we can see that there are several ways we can interact with AI:

• Guardian Angel
  
  The guardian angel includes interactions that are entirely unconscious for people, up to and including their results — and sometimes beyond.

• Informant
  
  While an informant also takes on basic activities like a pixie, the informant interaction is more focused on retrieving information.

• Best Friend
  
  The best friend is an extraordinarily complex type of interaction due to its high interdependence. At the same time, the best friend only interacts within the scope of a predefined social action.

• Colega (Colleague)

  • Um colega está orientado para os objetivos e apresenta um resultado informativo, de assistência ou de consultoria em interações conscientes.

• Melhor Amigo (Best Friend)

  • O melhor amigo é um tipo de interação extraordinariamente complexo devido à sua grande interdependência. Ao mesmo tempo, o melhor amigo só interage no âmbito de uma ação social pré-definida.

Thinking about all these interaction possibilities allows us to innovate and avoid falling into the easiest and most "shiny" solutions for users.

An AI system that helps people doesn't always have to be a chat, nor something that is always present. These solutions can also be omnipresent and autonomous.

The State of UX 2025 ✨

The State of UX 2025 has been released, where we can see UX trends for the coming year.

Tech news 🤓

• Americans will be able to start interacting with ChatGPT by voice call by dialing a phone number.

• GitHub has launched its AI companion.

• Flipboard is trying to create a new social network through its recent project Surf.

• The "Fertilo" method is the new alternative to traditional in vitro fertilization (IVF), which increases efficacy and reduces the need for hormone injections by 80%.

Share

Respect for autonomy
Human autonomy must be respected so that people can make their own decisions and carry out their own actions.

Privacy and data protection
People have the right to privacy and the protection of their data, which must be respected at all times.

Equality
We should all have equal rights and opportunities, and therefore AI systems should not unfairly favor or disadvantage anyone.

Individual, social, and environmental well-being
AI systems must be designed to contribute to individual, social, and environmental well-being — never the opposite.

Transparency
The purpose and workings of AI systems must be exposed transparently and must also be easy to understand for all those involved.

Accountability and oversight
The stakeholders involved in the development of artificial intelligence systems must take responsibility for how these applications work and for the consequences resulting from their actions.

These ethical principles are based on the fundamental rights shared by the European Commission. You can learn more about this and other references on the page I created specifically for AI ethics 👇

Measuring experience is perhaps one of the greatest challenges in Product, and consequently the one we pay least attention to. This happens because there are no easy or straightforward ways to measure 'experience'. However, it is critical to prove the value of Design activities and their impact on user journeys. That's why today we're talking about KEIs — similar to KPIs (Key Performance Indicators), Key Experience Indicators can be a strategic ally to help you measure the outcome of your research methods and methodology.

It's important to keep in mind that numbers alone don't always tell the full story. While metrics like page load time and conversion rate are important, they don't offer a complete picture of the user experience.

Neste artigo vamos abordar:

• What are Key Experience Indicators

• Why KEIs are Important

• Types of KEIs

• Examples of key experience indicators

• How to use Key Experience Indicators

• How to start measuring

What are Key Experience Indicators?

Key Experience Indicators focus on the quality of the user experience. Unlike traditional metrics that may measure performance or functionality, KEIs are designed to capture the subjective aspects of user interactions.

KEIs can include metrics such as user satisfaction, ease of use, and engagement. They help teams focus on what truly matters: the user experience.

Why are KEIs Important?

KEIs are essential for several reasons:

• User-Centered Design: They ensure the design process remains focused on the needs and experiences of users.

• Strategic Decision-Making: KEIs provide actionable insights that can guide product and service development decisions.

• Measuring Success: They offer a way to quantitatively measure the success of UX and research efforts, beyond functional outcomes.

Types of KEIs

KEIs can be categorized into three major areas, each addressing different aspects of the user experience:

1. Ease of use

2. Engagement

3. Satisfaction

Examples of key experience indicators

1. Customer Satisfaction Score (CSAT): Measures the degree of user satisfaction with a product or service at a specific interaction point or overall.

2. Net Promoter Score (NPS): Assesses loyalty by asking users how likely they are to recommend a product or service to others.

3. Task Success Rate: Evaluates the effectiveness of a product or service by measuring the percentage of tasks users can successfully complete.

4. Time-on-Task: Measures the amount of time users spend completing a specific task, which can indicate the efficiency of the product or service.

5. User Error Rate: Tracks the frequency of errors made by users during interaction with a product or service, highlighting areas that may need simplification or improvement.

6. System Usability Scale (SUS): A tool for assessing the usability of a product or service. It consists of a 10-question questionnaire that provides an overall view of subjective usability evaluations.

There are other metrics you can use beyond those presented in this article.

How to use Key Experience Indicators?

1. Identify your goals: Understand what you want to achieve with your product or service. This will guide which KEIs are most relevant to your project.

2. Select relevant KEIs: Choose KEIs that align with your goals and are measurable. It's better to start with a few and expand as needed.

3. Measure and analyze: Collect data on your selected KEIs and analyze the results. Look for patterns and insights that can inform design improvements.

4. Iterate based on insights: Use the insights gained from KEIs to make informed design decisions and iterate on your product or service.

How to start measuring?

User research

A common way to measure KEIs is through methods such as online questionnaires, interviews, or focus groups. The questions should be designed to assess the user experience across different aspects, such as ease of use, usefulness, and overall satisfaction.

Behavioral data analysis

Another way to measure KEIs is through behavioral data analysis. This involves using tools like Google Analytics or Mixpanel to track user behavior and identify patterns.

Usability testing

Usability testing can also be a great tool for measuring KEIs. These tests involve observing users as they interact with your product or service and identifying areas where they encounter difficulties.

Final thoughts

Measuring the success of User Experience activities and methods is essential to ensure that design efforts are aligned with user needs and business goals. KEIs are a valuable tool for this, as they focus on the quality of the user experience rather than just the technical performance of the product.

Remember that KEIs should be adapted to your specific product or service and to the needs of your users. What works for one company may not work for another.

In summary, KEIs become an easier and clearer way to use qualitative (and not just quantitative) research methods, translating them into a more tangible language for the whole team.

There are several factors that should be taken into consideration to promote the accessibility of these types of methods, and today we share some key points to pay special attention to and transform your next workshops into a more inclusive activity.

Social accessibility

Social accessibility in workshops ensures equitable and meaningful participation from everyone. Several studies on design methods highlight the importance of taking into account different cultural contexts, as well as language barriers and other specific needs.

This is most visible in physical barriers, where we must also ensure that the workshop space is accessible.

Understanding each individual's context also means we need to consider each person's preferences regarding their presence — whether they prefer to be physically or digitally present. If we're working in the healthcare area, it's common in some cases for people to prefer remote workshops, given their possible fragile and often complex situation, which may not allow travel or could put their health at risk.

Tone of voice and posture can play a fundamental role in some conditions, so it's important to have a script for workshops, even if it's loosely structured. This allows us to ensure we maintain assertive communication, clear language, and a proper posture.

Activities can exclude participants

The activities you choose may present accessibility challenges. Some examples include:

• Activities that require specific motor skills, such as fine motor skills, which can exclude participants with physical and motor disabilities.

• Activities that heavily depend on verbal communication, which may disadvantage participants with speech or hearing difficulties.

• Activities that involve the use of complex technology, which may exclude participants with low digital literacy or limited access to these tools.

• Activities that require long periods of attention or physical effort, which can be tiring and challenging for participants with attention disorders or other health conditions.

Before offering help, make it clear that you know the person is capable of doing anything on their own

Rather than presuming the limitations of any participant, it is essential to communicate confidence in each individual's abilities and offer help in a way that respects their autonomy and dignity.

This is important to promote inclusion and respect for each person's capabilities, especially in the context of research in healthcare, where the stigma of the disease can be a real barrier to participation.

Different ways of participating

Some participants may prefer verbal communication, while others express themselves better in writing or through visual methods. Recognizing and accommodating these differences is crucial to ensuring inclusive participation.

This can involve creating activities that allow verbal expression, as well as participation through written forms, visual media, or even digital tools.

Sharing experiences and building empathy

In these cases, it is important to have more than one person in the group who shares the same condition, experience, or background. This helps create a safe and comfortable environment where participants feel understood and represented.

This representation makes the workshop itself more fair.

Make the session flexible

It is important to mitigate existing psychomotor barriers, so you need to be prepared to adapt activities in real time. This could mean offering alternative ways to complete a task, extending time for activities, or providing additional support when needed.

The value of the indirect mode

The use of indirect methods is important to make it easier and safer for participants to express themselves, particularly on sensitive topics.

• Persona development

• Indirect storytelling techniques

• Visual and creative tools

That was today's snack — don't forget to share it with your network 🍪

Partilhar

Hello, UXers, Product folks, Designers, and curious minds! Today I decided to share a checklist I've been working on over the past few weeks to make heuristic analysis easier.

It is common for only one or two professionals to carry out this analysis. However, it's important to note that better results are obtained in heuristic analysis when it is performed by 4 to 6 evaluators.

What is heuristic analysis

Heuristic analysis or evaluation (Nielsen and Molich, 1990; Nielsen 1994) is a method for evaluating the usability of a system. This method identifies up to 40% of the most serious problems. It is therefore important to complement it with other validation methods, such as usability testing.

This evaluation is done based on usability principles, also known as "heuristics". For this, we rely on Nielsen's 10 heuristics:

• Visibility of system status

• Match between system and the real world

• User control and freedom

• Error prevention

• Recognition vs. Recall

• Consistency and Standards

• Flexibility and Efficiency of Use

• Aesthetic and Minimalist Design

• Help Users Recognize, Diagnose, and Recover from Errors

• Help and Documentation

What is the difference between a heuristic evaluation and an expert review?

According to usability.gov, the main difference lies in the expert review, which assumes the professional already has enough knowledge and experience to carry out an analysis with the heuristics in mind. It thus becomes a less formal analysis and does not need to specify which particular heuristic is causing a problem.

Inscrever-se agora

The checklist

👉🏻 This is a tool that can help newer professionals in the field carry out this evaluation or to maintain consistency across projects. Share your feedback in the comments.

Podes aceder a esta checklist aqui👇
Google Sheets Checklist Template

How it works

Log in to your Google account, or create one. Then simply go to File > Make a copy. You can then start editing the file. Use the "results" sheet to access the outcome of the analysis.

Today, I find it crucial to discuss the concept of the "product trio" — something I emphasize constantly when talking about creating and developing digital products. This trio forms the foundation for a product's success.

What is a product trio

The term "product trio" refers to the core team involved in creating a digital product. This concept was explored by Teresa Torres in her book "Continuous Discovery Habits." Typically, this trio is composed of a Product Manager, a Designer, and a Developer.

Responsibilities of each member

Despite having great admiration for Teresa Torres, I partially disagree with the description of the trio she presents in her book "Continuous Discovery Habits." Torres suggests that the Designer's role in this trio is primarily focused on visual and system materialization, facilitating customer interaction with the product. In my opinion, this view is limiting.

Therefore, my description of this trio aligns with the team structures I've encountered and with what I believe to be most valuable in design teams — being the advocate for user value.

Especially in companies with low design maturity. In other words:

Product Manager
Responsible for conveying value and vision to the business.

Developer/Programmer
Responsible for validating the technical feasibility of what will be proposed.

Designer
Responsible for being the voice of the user, showing what the value looks like for the user.

The importance of a good trio

There is a direct relationship between a strong trio and the value of your product or service. In other words, we can also describe this trio as the well-known "sweet spot" of innovation, which sits between business viability, technical feasibility, and the desirability of the people who will use it.

It is therefore important for this trio to have a clear understanding of each member's role and, more importantly, to adopt a human-centered approach. By primarily using qualitative research methods, they can discover user needs and bring the expected value into the discussion.

In summary, a mindset based on the following points should be adopted:

• Focus more on impact (outcome) than just output.

• Human-centered: Put users' needs and experiences at the forefront.

• Collaboration: Work together as a team and leverage diverse perspectives.

• Visual communication: Use visual means to convey ideas and concepts effectively.

• Experimentation: Embrace a culture of exploration and learning through prototyping and testing.

• Iteration: Continuously refine and improve the product based on feedback and insights.

By adopting this mindset, the trio can enhance their ability to create products that truly meet users' needs and deliver meaningful outcomes.

Suggested reading

https://www.goodreads.com/book/show/58046715-continuous-discovery-habits

Biodesign is a design approach that uses biology and life sciences as inspiration and a resource for innovation in various fields, including healthcare.

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The Biodesign Innovation Process is a robust, multidisciplinary, and systematic approach for identifying important needs in healthcare, enabling new solutions that improve patient health.

Focus on needs

Biodesign is adapted from the design thinking process, which first defines the problem and then implements solutions, always with the needs of users and stakeholders at the center of concept development. This process focuses on identifying needs, generating concepts, and implementing. By creating and testing something, we can continue learning and improving on the initial ideas.

Benefits of this process

Similar to Design Thinking and other Design methodologies, this process has several benefits:

• Human-centered approach

• Efficient and effective solutions

• Cost reduction

• Greater sustainability

• Focus on innovation

Biodesign is an adapted method for exploring new innovation solutions at the intersection of healthcare and well-being. The first step in this method is finding needs — and we'll talk about this phase in our next article.

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“A designer who does not present their own work is not truly a designer.”

– Mike Monteio, Design is a Job

The importance of Design presentations

• Helps build relationships with clients, colleagues, or teams;

• Makes you accountable for the work you've done;

• Gives the opportunity to ask questions directly to the person who executed the work;

• Allows you to practice your presentation and storytelling skills.

If you're reading this and you're an Art Director or Team Lead, make sure it's the designers who present their own work. They deserve that space and it only brings advantages for everyone.

If you're a Designer and it has happened that someone else presented your work (which is not that uncommon), try to explain the benefits of presenting your own work yourself.

Rule #1 — Focus on the goals of what you're trying to solve

Rule #1 is simple. Focus on the goals you're trying to solve, not the features. Your entire presentation should be prepared, created, and delivered around the objectives and the problem you're trying to solve.

By focusing on the objectives and less on the visual character or features of what you're presenting, you'll be able to spare yourself unnecessary comments. I'll also spare examples for those with PTSD.

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Preparing for the presentation

Before we go further, you should know that being nervous is normal. It's our body telling us that what we're about to do matters. It's fine.

Agenda

Presentations should be treated like meetings and as such, they should have an agenda. This makes the session more focused and helps everyone be better prepared.

The agenda should include:

• The purpose of the meeting / presentation;

• The topics and details of what will be covered;

• The expected outcomes.

• Tempo e uma vaga ideia das atividades que vão acontecer durante a reunião;

• Responsabilidade de cada um envolvido.

Meeting costs

In smaller companies you can skip this part, however when we talk about the enterprise world, having a large number of people in a meeting can be extremely costly. Consider it: a one-hour meeting with 10 people = 10 hours of work.

So we should only invite people who can contribute and those who are essential to the decisions being made.

Preparation

Presenting requires preparation, and that's why this is one of the most important steps. Presenting Design should be taken seriously, whether you're presenting user journeys on a napkin or wireframes in a PowerPoint. Here are some tips:

• Write a high-level outline of the idea and goal of the presentation and how you envision it in practice;

• Share that first high-level detail with colleagues and collect feedback;

• Review the goals and high-level definition of your presentation;

• Keep sharing the progress of your presentation within the team as you work on it;

• Do an informal internal test. You can do this by presenting to your team members.

• Once you feel more confident with your presentation, you can present informally to a selection of stakeholders.

• Altera o que vires necessário com base no feedback anterior;

• Envia a apresentação antes da reunião.

Presentation

This is the part where you should introduce the topic, explain what you're going to present, and introduce any team member that makes sense.

If you're presenting together with other people, don't forget to help them if you see they're stuck.

Avoid making exhaustive presentations where you go through every single design detail or everything you did. Focus more on how what you're presenting solves the problem you're trying to solve, or how it aligns with the initiative's objectives.

Also keep in mind:

• Pay attention to your posture;

• Maintain connection with the people you're presenting to;

• Show enthusiasm for what you're presenting;

• Adopt a slower speaking pace;

• Look more at the people and less at the screen where your presentation is;

• Never use sarcasm. (yes, this happens)

Receiving feedback

Being prepared to receive criticism is important, especially if you have someone in your audience who doesn't understand the role of Design. It's therefore important to define the format and type of feedback you're looking for. This can be included in the agenda and reinforced at the end of your presentation.

Defining the scope of feedback will also allow those involved to feel more comfortable sharing their opinion since you've created the space for it.

Here are some tips for creating your rules:

• Avoid assumptions;

• Give feedback in question format;

• Avoid personal opinions;

• Focus on the Design, not the Designer;

• Define where feedback should be given;

Some professionals suggest that feedback should be separate from the meeting. Others place this moment at the end of the presentation.

My advice and what has worked best for me is to leave a section at the end of the presentation just to clear up doubts about what was presented, and to schedule a second, separate, short meeting for feedback.

During the time between the presentation and the feedback meeting, I additionally ask stakeholders to send their feedback by email. — This is very important.

Conclusion

I may have gone a bit long this time, but I hope this information has been useful. Don't forget to leave a comment, subscribe to receive the next articles in your email, and follow my Instagram — https://www.instagram.com/uxsnack.design

What is story mapping?

Story mapping is a method for organizing user stories and allows for a more holistic view of the entire experience and process. And yes, it is also a method that can be proposed, used, or facilitated by designers.

I've used this method when working in product teams that mostly follow agile methodology and where maturity is low and they need some guidance.

This is because it forces and educates teams to write user stories correctly, focusing more on users and less on solutions or technical limitations.

At the end of a Story Mapping session you should have:

• A holistic view of the product (backlog)

• Features broken down into user stories

• User stories broken down into technical tasks

Benefits

Story mapping can be used at various moments in your project, for example during sprint planning. It should be a living document and updated as needed.

• Story mapping promotes the product vision

• Technical teams can access a view of future work

• Easier to map everything (visually) and decide what the MVP will be for the first release.

Who should be present in this workshop?

• User Experience teams

• Development teams

• Product owner / manager

• Scrum master

How to do a story mapping

It's important to remember that Story mapping should represent user tasks in a narrative way.

Also remember that a user story should represent an intention and goal rather than a feature. We'll talk about user stories in another article…

Don't focus on technical complexity, but rather on the user's tasks.

It's important that the people involved know all the steps of the process so they feel comfortable participating.

I've done this type of workshop with developers who would start to panic if we didn't talk about technical tasks or features. In this case, tell them that we will get to the technical tasks in time, but it's important to start with tasks from the user's perspective.

First, let's define our persona and describe their tasks in their journey.

To make it easier, and if something already exists, you can place that task as a central point and fill in the user tasks that happen before and after that moment.

Don't forget to write tasks in narrative form and correctly define the persona.

Map tasks into activities

Now that you have the user's tasks, you can categorize or organize them into activities. In the future, these can become the epics used in product management.

Create the user stories

At this point you have the base of your map and we are going to start creating user tasks in greater detail. Always from the user's perspective.

The best way to create user stories is to use the most well-known format:

"As a [persona], I want to [what]. [so that]."

This would result in something like: "As Sarah, I want to organize my work, so that I can manage my team's tasks."

Define priorities and what will be in the next release

It should now be possible to prioritize user stories. You can help the Product Manager divide them into releases. This prioritization typically involves the effort of each user story and its importance to users.

This mapping allows you to understand what will be delivered over time and the value each release brings.

Priorities can always be changed later, especially after understanding the technical difficulty of each user story.

Break each user story into technical tasks

In the final phase of this workshop we should break down user stories into technical tasks. These should allow the development teams to understand what tasks need to be done in order to support each user task. This approach also allows for a better method of estimating and prioritizing technical work according to user value.

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I've tried to summarize this activity as much as possible. You can find more information here:

• What is Story Mapping — Agile Alliance

• Story Mapping Template — Miro

• User Story Mapping: Discover the Whole Story, Build the Right Product — Jeff Patton

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Recently, while talking with some product designers from various companies, I noticed that teams are becoming increasingly segregated. Between those who do research and those who only materialize the findings into something visual, ready to hand off to developers.

This creates a separation and segregation in the process, where the depth of insights is lost and consequently ends up affecting the quality of production deliverables.

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After working with several companies and teams, I believe that the product designer should never be completely separated from the research phase. When this happens, a gap starts to exist between what was learned and what is being designed.

Hours with users, an important metric

Hours with users is a metric that can define the design maturity of a company or designer, as well as the quality of what is produced.

Product designers who spend few hours with users are product designers who have not observed the behaviors nor investigated the attitudes of those they are designing for.

I believe the negative impact of this is clear. Even if there are documents with findings or guidelines from the research team to follow a certain path, it is necessary for the person designing the interface or service to experience those same findings and investigate or observe them for themselves.

So how can we structure teams with both capabilities?

It has always seemed right to me to have one team more dedicated to research and another that is more operational. However, I never divided the methods that both teams use, but rather their purpose. That is, both teams should use research and design validation methods, but with different purposes or at different stages of the product.

In my case, when forming teams, the research team takes on a more strategic role, where the focus is on exploratory research, using more formative research methods. It tracks the Key Experience Indicators and is concerned with informing the business about new opportunities and changes to the current product that result in increased user value and consequently maintain the KEIs.

In addition, this team should support the operational side of research and help product designers produce their scripts, materials needed for the study, and supervise the use of best practices.

In the case of product designer teams, they are responsible for the operational side of research. Dealing daily with the materialization of new interfaces or experiences, these teams should be focused on discovering the value for the user, finding the best way to deliver the proposed value, and validating, through usability testing, what is produced.

This was the "snack" of the week. What is your opinion on this topic?

See you soon!

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Usability tests refer to the evaluation of a product or service by testing it with representative users. It generally involves observing people as they attempt to complete tasks given by the test moderator.

Usability studies allow us to collect qualitative behavioral data, as well as learn about cognitive behaviors and patterns.

The importance of testing

We cannot predict exactly how people will respond to an interface without first testing it. Usability tests allow teams to identify, prioritize and fix usability issues before launch.

The sooner problems are identified and fixed, the less costs there will be, the better the experience will be, and the less impact it will have on development time.

Planning - What you will need

• Define objectives and start a research plan

• Determine the format and direction of the study

• Determine and recruit participants

• Write the test tasks

• Do a first pilot test

• Decide on metrics and success criteria

• Motivate other people on the team to observe

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Determine what you want to learn, identify the questions, concerns, and the objectives of the test

Example: Will it be easy for users to find the library's opening hours?

Don't commit to too many objectives in a single study.

Determine the study format

Tests can be conducted in a lab or in context. In addition to choosing the location, you will need to set a time for each participant and have an organized schedule.

Moderado ou não moderado

Moderated tests tend to provide more valuable insights and opportunities due to their nature. You can also complement them by asking questions or reminding participants of the think aloud protocol.

Em pessoa ou remoto

Generally and whenever possible, studies should be conducted in person, as it is possible to analyze more data, namely body language. However, with remote opportunities, it is very common to opt for remote testing using tools such as Zoom, Lookback, etc.

Determine the number of users

According to Jakob Nielsen, 5 users should be sufficient for a usability test. Not to be confused with contextual interviews where this number is different.

5 users discover on average 75% of the most serious usability problems. It is preferable to conduct several studies with 5 people rather than a single study with 20 participants.

Don't forget to recruit one or two extra people to account for those who may not show up.

Recruit the right participants

The best insights come from obtaining feedback through real users. Therefore, it is preferable to recruit the most representative users possible.

Write tasks according to the test objectives

In a usability test, people are invited to complete a series of activities while using an interface, product or service. These activities or tasks are generally written within scenarios and fulfill the study objectives.

Writing contextual and realistic tasks is essential for conducting a valid usability test.

Use exploratory tasks to learn more about behavior

Exploratory tasks allow you to learn how people discover or explore an interface without giving them a specific direction to accomplish.

Example: You are interested in booking a holiday for your family. See if you can find anything useful on this website.

In more quantitative tests or to validate flows, specific tasks can be used:

✅ Good example: Find the opening hours of the municipal library.

❌ Bad example: Find the contacts page.

Decide which metrics to collect

The time needed to complete the task, degree of satisfaction, success or error rate, and the number of errors are some of the most common metrics.

Run a pilot test

The pilot test helps refine the details of some tasks or your script. It also helps you validate the feasibility of what you are planning to do and estimate the time it will take.

These pilot tests can be even more critical when it comes to unmoderated remote tests.

Let's test!

We're ready to start testing and validating our path. 😎

And don't forget to bring a team member or the client themselves, if that's the case. There's nothing better than having stakeholders watch real users interact with the product.

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Some say that growing old is the best thing that can happen to us (1), yet there is a clear marginalization and segregation of the senior population in society.

Just like the negative connotation we associate from an early age with "old", "elderly", "senior citizen".

None of us wants to be old, much less called old. Ask my 82-year-old grandmother who insists she's not a senior citizen.

We associate being old with being bad — we are put aside, we have many health problems and we will end up in a nursing home. But does it have to be this way?

By 2070, Portugal will only have 4.2 million people of working age

According to the 2018 Ageing Report highlighted by Público, only 4.2 million people will be of working age (2), and so we are heading towards a new paradigm.

The stigma of existing solutions

Aging is linked to the stigma created by society. Current solutions, whether mobility aids like walkers or of another nature, scream old age. They are typically products that nobody wants to use because they are associated with the stigma.

We need to innovate towards new solutions that add more value and that are desired not only by the senior population, but by everyone who has that need.

We could create a walker so incredible that even young people wouldn't feel stigmatized using it — like a portable chair they can take to hang out on the street with friends or to a concert where they'll be standing for hours.

Interface design

Best practices for designing interfaces for seniors are also part of human-centered design and therefore shouldn't be a novelty for many. However, it's always worth reinforcing:

• Make use of "progressive disclosure". This age group is especially vulnerable to episodic and working memory lapses. We should present new options and features gradually.

• One task per screen, high contrast and legible fonts.

• Provide the clearest possible feedback on progress and interaction.

• Promote reminders for habitual actions.

• Use captions for all videos.

• Do not use text on top of images.

• Avoid using iconography alone.

• Due to the common loss of attention at these ages, we also need to be as explicit as possible.

Beyond interfaces

Working in this area goes beyond interface design. It extends to architecture, policy, public transport, services, and new forms of access. There is a whole world to be explored and made accessible so that we can extend an active and long-lasting aging.

It's also important to emphasize that we want to create products that are inclusive and therefore don't scream old age, prejudice or stigma. Products that can be used by everyone to improve quality of life, because "the world is aging and there is nothing more modern than growing old" (3).

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References

1 Kalache A. «Growing old is the best thing that can happen to you» | Alexandre Kalache explains ageism and prejudice [video] [Internet]. Youtube. 2020.

2 Público «By 2070, Portugal will only have 4.2 million people of working age»

3 Kalache A, Carbonari P. "The world is aging and there is nothing more modern than growing old", says gerontologist Alexandre Kalache. Future Health. 2021.

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They can be understood as shortcuts our brain uses to process the information around us.

Sometimes these shortcuts are not good and lead us to process information incorrectly, which we call judgment distortion.

Currently, more than 188 cognitive biases are known. These deviations or errors can appear in various forms. The most common ones are linked to information processing through shortcuts, distortion in how we store events in our memory, limitations in our brain's processing capacity, social influences, or emotional changes.

Understanding what cognitive biases are is important for us, designers, because we are constantly designing products and services in an era where "dark patterns" are increasing, cognitive load is also rising, and unconscious human error is growing. This is very concerning, especially in hospital environments where human lives can be put at risk.

How can we use this knowledge to our advantage?

Now I have an interesting "twist". Learning about cognitive biases is just as essential for better understanding the people who will use our products and services as it is for understanding our stakeholders!!

I would say that a large part of stakeholders make decisions based on the "bias" around them, which often leads us to poor choices when it comes to user experience and thinking more about business objectives than the real value we are delivering. One of those biases could be the Confirmation Bias.

We can therefore use cognitive biases to our advantage to better understand and persuade our stakeholders to make better decisions. At the same time, we can use this knowledge to recognize our own "biases", how to work around them, and logically design and create human-centered experiences.

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