Conference Program

Asking after the dynamics of artificial intelligence’s extraordinary recent rise recalls Hemingway’s famous line about going bankrupt: “In two ways. Gradually, then suddenly.” That combination underscores the emergence of many technologies, of course. It creeps up on us, and then is suddenly moving at speed, everywhere. This makes it hard for cities and places to work with the grain of tech, in order to produce equitable or sustainable outcomes. Although we rarely do it, tech asks us to step back and ask the deeper questions lurking behind all the noise.

In this talk I’ll describe how everyday technologies, digital and physical combined, define how we live together; how they tend to articulate what we stand for as a society, or how our cities work — and what’s on the table now. Drawing together inspiring projects and cases ranging from Norway to Japan, and from new cities to reimagined regions, I’ll suggest how we might align design practices in order to address our contemporary shared challenges, like climate breakdown, social cohesion, and sweeping demographic changes. As AI moves beyond shuffling playlists or improving grammar and starts coordinating mobility, energy, and water systems, or how housing is allocated or buildings are made, we must rapidly figure out how design, governance, and community best understand and take advantage of these new distributed, decentralised and collaborative technologies. In doing so, we might well challenge our preconceptions of technology, economy, and community themselves.

Following the emergence of Generative AI as a potential revolution in the UX field, a great deal of AI-driven tools arose to enhance the efficiency of UX research, including data analysis. Qualitative data analysis is a process that conventionally relies on human intelligence to discern patterns, establish connections, and derive actionable insights and frameworks. Many studies have involved comparing the quality of qualitative analyses generated by humans with those produced by AI language models like ChatGPT (Hamilton et al., 2023).

Despite the undeniable appeal of automation and speed, there is ongoing debate about AI’s ability to replace human intelligence in qualitative analysis, which may be unlikely at this moment. Then the question is: To what extent can AI contribute to qualitative data analysis?

In this case study, I delved into the thematic analysis and post-analysis stage, i.e. synthesizing insights into a framework. Framework, in this context, refers to a conceptual structure that illustrates the components of a human experience and how the components interconnect and operate within the structure. It is a concise model that encapsulates the entirety of research insights.

The topic of my case study is “trust relationships between job seekers and hirers in the marketplace,, aligning with the business focus of my company. From my secondary research, I found that, ChatGPT needed multiple rounds of training using diverse prompts to conduct precise and comprehensive thematic analysis. ChatGPT can execute fine-quality thematic analysis under the help of right prompts, yet it falls short in replacing human intelligence for synthesizing insights and crafting frameworks for engaging narratives.

Its limitation lies in lacking the depth of contextual understanding within a company, such as understanding what’s missing from the company’s mainstream discourse to create a human-centered story based on data analysis. To craft a framework that conveys good storytelling and organizational impact, it requires the researcher’s introspection into knowledge gaps in the specific organizational context. Thus, the best practice is to combine human interpretation and AI production. In my talk, I will demonstrate several principles to guide this practice.

Takeaways

We’ll cover principles of how to employ ChatGPT in qualitative analysis, specifically focusing on its application in synthesizing and crafting frameworks that convey compelling and insightful narratives:

• Effectiveness of ChatGPT in thematic analysis: Learn about my process of training ChatGPT to conduct precise thematic analysis. You’ll gain insights into the capabilities and limitations of ChatGPT in providing accurate and comprehensive analysis for framework construction
• Value of human potential: We’ll address the value of human self-reflection and the ability of interpreting organizational context for crafting engaging frameworks
• Comparison between human and ChatGPT: By comparing the human-driven outcomes against ChatGPT for qualitative analysis, you’ll see how effective the synthesized frameworks are generated by the researcher and ChatGPT separately.
• Collaboration between human and ChatGPT: You’ll also learn when and how to incorporate human interpretation with ChatGPT to achieve the best practice in qualitative analysis and synthesis

It seems like every company is adding a conversational AI chatbot to their website lately, but how do you actually go about making these experiences valuable and intuitive? Savannah Carlin will present a case study on a conversational AI chatbot—Marqeta Docs AI—that she designed for a developer documentation site in the fintech industry. She will share her insights, mistakes, and perspectives on how to use AI in a meaningful, seamless way, especially for companies like Marqeta that operate in highly regulated industries with strict compliance standards.

The talk will use specific examples and visuals to show what makes conversational AI interactions uniquely challenging and the design patterns that can address those challenges. These include managing user expectations, handling errors or misunderstandings within the conversation, and ensuring that users can quickly judge the quality of a bot’s response.

You’ll gain a deeper understanding of the intricacies involved in designing interactions for AI, along with practical advice you can apply in your own design processes.

Take-aways

• What to consider before you add AI to your product to ensure it will be valuable, usable, and safe for its intended workflows
• The interactions that are unique to conversational AI experiences and the design patterns that work for them
• Common challenges in designing conversational AI experiences and how to overcome them

Take a break to grab a bite, stretch your legs, chat with speakers and attendees in the conference Slack, or even take a nap. Just remember to set your alarm and be back in time for the second half of Design in Product!

In this talk, I’ll delve into how AI has helped to catapult me from a researcher to a strategist—a “super researcher” if you like. AI tools have streamlined my research processes, enhanced stakeholder communication, and freed up more time for strategic thinking. I’ll demonstrate how rapid data synthesis and efficient communication via AI have led to faster project turnarounds, allowing me to have better relationships with stakeholders and giving me more time to think and move more strategically in my company. We’ll illustrate the significant time-saving advantages of AI integration in UX research, positioning it as a crucial tool for staying ahead in a fast-paced tech world.

Takeaways

• Strategies for using AI to expedite UX research processes
• Techniques for leveraging AI in enhancing stakeholder communication
• Insights into transforming data analysis into strategic UX decision-making
• Demonstration of the efficiency of AI in UX project turnaround
• Understanding of AI’s role in maintaining agility in fast-paced tech environments
• Practical tips on integrating AI tools into UX research workflows

In this session, I will explore the groundbreaking approach taken by the Austin Public Library’s Innovation Lab in integrating AI with GitHub for UX enhancement. Our primary challenge was to streamline and enrich the documentation process, adhering to the complex guidelines of the U.S. Web Design System (USWDS) while ensuring the content was accessible and user-friendly.

Leveraging AI, we developed a system that not only automates the creation of diverse documentation formats like Markdown, but also ensures alignment with USWDS standards. This innovative method significantly improved the efficiency of our documentation process, making it more coherent and navigable. A key highlight is our recent revamp of the Breadcrumbs navigation strategy, demonstrating AI’s role in enhancing user experience and interface design. The session showcases how AI can transform UX practices, offering practical insights for UX professionals looking to leverage AI in their work.

Takeaways

• Insight into the innovative use of AI for streamlining documentation processes, adhering to USWDS guidelines in a public library setting
• Practical examples of how AI can transform UX practices, with a focus on creating efficient, user-friendly, and standards-compliant documentation
• Demonstration of the AI-assisted update of the Breadcrumbs navigation strategy, demonstrating tangible improvements in UX design and user navigation
• Strategies for leveraging AI to enhance collaboration among UX designers, developers, and content creators, fostering a more integrated and effective workflow
• Tips for utilizing tools like GitHub Pages for rapid deployment and continuous integration, highlighting the role of AI in accelerating project development

In this high-pressure scenario, the challenge was to conduct 17 user interviews in three days and synthesize a comprehensive report in just one additional day. I’ll explore how we used AI to streamline the research process, from transcription to synthesis, and how tools like ChatGPT contributed to efficient data processing and insight generation. We’ll reflect on the potential and pitfalls of using AI in accelerated user research, from practical aspects to more philosophical considerations on potential changes to the research process.

Takeaways

• Practical insights into integrating AI with traditional research methodologies to expedite the research process
• An overview of the effectiveness of AI transcription and synthesis tools in real-world research scenarios
• Critical examination of AI’s role in data processing and how it compares with human analysis
• Strategic considerations for service designers when employing AI to support rapid user research
• Reflection on the ethical implications and potential impact on the quality of insights and researcher well-being when relying on AI to speed up research processes

Panel description to come.

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