Personas have been widely used to understand and communicate user needs in human-centred design. Despite their utility, they may fail to meet the demands of iterative workflows due to their static nature, limited engagement, and inability to adapt to evolving design needs. Recent advances in large language models (LLMs) pave the way for more engaging and adaptive approaches to user representation. This paper introduces Interactive Virtual Personas (IVPs): multimodal, LLM-driven, conversational user simulations that designers can interview, brainstorm with, and gather feedback from in real time via voice interface. We conducted a qualitative study with eight professional UX designers, employing an IVP named "Alice" across three design activities: user research, ideation, and prototype evaluation. Our findings demonstrate the potential of IVPs to expedite information gathering, inspire design solutions, and provide rapid user-like feedback. However, designers raised concerns about biases, over-optimism, the challenge of ensuring authenticity without real stakeholder input, and the inability of the IVP to fully replicate the nuances of human interaction. Our participants emphasised that IVPs should be viewed as a complement to, not a replacement for, real user engagement. We discuss strategies for prompt engineering, human-in-the-loop integration, and ethical considerations for effective and responsible IVP use in design. Finally, our work contributes to the growing body of research on generative AI in the design process by providing insights into UX designers' experiences of LLM-powered interactive personas.

Working in the UX industry means living in a constant state of flux. Every day seems to bring new technologies, skills, business challenges, and user expectations to absorb. That's why every December, I eagerly await the release of UX Collective's State of UX report, in which authors Fabricio Teixeira and Caio Braga (plus collaborators) synthesize thousands of articles in order to put the past year into perspective and highlight emerging themes for the road ahead. The underlying theme for this year's report was anxiety. Massive layoffs at tech stalwarts like Facebook, Google, and Amazon, along with headlines about an economic slowdown, have some designers thinking about how to recession-proof their jobs. With headcounts shrinking, design teams are expected to do more with less, and former managers are returning to hands-on work.

Well, I've got good news for you - ChatGPT is here to revolutionize the way we conduct user research. Recently, my team and I have been working on a new patient record interface for a big hospital in Israel (no pressure, right?), and we knew that creating use cases for different types of users was going to be a crucial step in the process. So, Instead of spending hours and hours writing them out by hand, we decided to put AI to the test! We did our brainstorming like usual, we identified the personas, their roles, jobs to be done, and only then we used ChatGPT to generate the use cases, and it was like having a wizard at our disposal! Chat responded with a variety of scenarios that we were able to use in the next brainstorming session.

The rise of AI is creating a lot of buzz in almost every modern sector. While it remains unclear what we can expect from AI for designers, there have been recent developments that signify that something huge is going to happen. We may see some groundbreaking development in the way we handle interactions. Digital humans are becoming more present on the internet, and may revolutionize how we interact with the world around us. Our creativity can also expect a huge boost from AI.

But as more and more experiences are built with ML, it's clear that UXers still have a lot to learn about how to make users feel in control of the technology, and not the other way round. In the Google UX community, we've started an effort called "human-centered machine learning" (HCML) to help focus and guide that conversation. Born out of our work with UX and AI teams at Google (and a healthy dose of trial and error), these points will help you put the user first, iterate quickly, and understand the unique opportunities ML creates. When doing user research with early mockups, have participants bring in some of their own data -- e.g. This can result in "conspiracy theories" where people form incorrect or incomplete mental models of a system and run into problems trying to manipulate the outputs according to these imaginary rules.

Machine learning (ML) is the science of helping computers discover patterns and relationships in data instead of being manually programmed. It's a powerful tool for creating personalized and dynamic experiences, and it's already driving everything from Netflix recommendations to autonomous cars. But as more and more experiences are built with ML, it's clear that UXers still have a lot to learn about how to make users feel in control of the technology, and not the other way round. As was the case with the mobile revolution, and the web before that, ML will cause us to rethink, restructure, displace, and consider new possibilities for virtually every experience we build. In the Google UX community, we've started an effort called "human-centered machine learning" (HCML) to help focus and guide that conversation.

In this blog, Dr Mani Hussain, Director of Primary and Community Care, talks about CQC's involvement in the multi-agency advice service on artificial intelligence. Artificial intelligence (AI) and Data-driven technologies have exciting potential to improve the quality of care for people using services. For example, hospitals are now using AI to support radiologists with their decision making. In diagnostics, AI can help analyse x-rays leading to the quick identification of abnormalities. In research, AI is used to analyse large swathes of data which helps to discover and validate new drugs.

Communication is a natural part of our everyday lives. People interact using voice and text, forming sentences to express what they desire. And yet, most of the search and discovery patterns out there rely on menu items and filter facets. Building on our mission at Booking.com: "Making it easier for everyone to experience the world", the ML & AI Product teams based in Tel Aviv decided to challenge the conventional search patterns by allowing the most natural way for everyone to communicate: using their voice. This is the story of how we built a native in-app voice assistant at Booking.com, and as far as I know, the first voice search available today by a global online travel company.

I joined the AgriTech hackathon in October 2021. AgriTech seeks to advance the world of agriculture by solving new problems by using the latest in technology. I was put on a team with five other members from all across the world. Their skills varied from technology to agriculture to business. I was over leading in the design and prototype of the product.

This Friday the 11th of December, Nikolas Martelaro (Assistant Professor at Carnegie Mellon's Human-Computer Interaction Institute) will give an online seminar on ways robot design teams can do remote user research now (in these COVID-19 times) and in the future. Nikolas Martelaro is an Assistant Professor at Carnegie Mellon's Human-Computer Interaction Institute. Martelaro's lab focuses on augmenting designer's capabilities through the use of new technology and design methods. Martelaro's interest in developing new ways to support designers stems from my interest in creating interactive and intelligent products. Martelaro blends a background in product design methods, interaction design, human-robot interaction, and mechatronic engineering to build tools and methods that allow designers to understand people better and to create more human-centered products.