This open access book addresses the thriving trend of embedding artificial intelligence (AI) and machine learning (ML) capabilities in products and services reaching the lay public, focusing on the user experience (UX) they prompt from a designerly perspective. It offers a UX evaluation method designed explicitly for AI-infused systems to answer one of the core problems affecting the relationship and interactions people have with such artefacts. The work investigates how people perceive and make sense of systems integrating AI capabilities, trying to understand how their meaning and significance can affect the experience of such products and what design challenges may arise. Given the fundamental premise that current UX methods cannot address AI-infused artefacts, it introduces the results of Meet-AI, a research project exploring specific ways to tackle these problems. The book then presents a comprehensive analysis of current UX methods, and a literature review focused on detecting possible gaps and the most suitable qualities to describe AI-infused systems, and summarizes the findings from all previous investigations into a UX evaluation scale: AIXE (AI user eXperience Evaluation). The book also portrays how the tool has been validated and expanded to become a more comprehensive method. It further describes how the scale has been applied to a comparative study of domestic smart speakers, and introduces a reversed interpretation of the outcomes, framing them as heuristics to inform the early phases of the design process and paving the way for future experimentations in the meta-design dimension.
This open access book addresses the thriving trend of embedding artificial intelligence (AI) and machine learning (ML) capabilities in products and services reaching the lay public, focusing on the user experience (UX) they prompt from a designerly perspective. It offers a UX evaluation method designed explicitly for AI-infused systems to answer one of the core problems affecting the relationship and interactions people have with such artefacts. The work investigates how people perceive and make sense of systems integrating AI capabilities, trying to understand how their meaning and significance can affect the experience of such products and what design challenges may arise. Given the fundamental premise that current UX methods cannot address AI-infused artefacts, it introduces the results of Meet-AI, a research project exploring specific ways to tackle these problems. The book then presents a comprehensive analysis of current UX methods, and a literature review focused on detecting possible gaps and the most suitable qualities to describe AI-infused systems, and summarizes the findings from all previous investigations into a UX evaluation scale: AIXE (AI user eXperience Evaluation). The book also portrays how the tool has been validated and expanded to become a more comprehensive method. It further describes how the scale has been applied to a comparative study of domestic smart speakers, and introduces a reversed interpretation of the outcomes, framing them as heuristics to inform the early phases of the design process and paving the way for future experimentations in the meta-design dimension.
