Automation of interaction - interaction design at the crossroads of user experience (UX) and artificial intelligence (AI)

Wei Xu

3 Januari 2023

In this chapter, we review and discuss the transformation of AI technology in HCI/UX work and assess how AI technology will change how we do the work. We first discuss how AI can be used to enhance the result of user research and design evaluation. We then discuss how AI technology can be used to enhance HCI/UX design. Finally, we discuss how AI-enabled capabilities can improve UX when users interact with computing systems, applications, and services.

Wei Xu

3 Maret 2024

The human-centered artificial intelligence (HCAI) design approach, the user-centered design (UCD) version in the intelligence era, has been promoted to address potential negative issues caused by AI technology; user experience design (UXD) is specifically called out to facilitate the design and development of human-centered AI systems. Over the last three decades, user experience (UX) practice can be divided into three stages in terms of technology platform, user needs, design philosophy, ecosystem, scope, focus, and methodology of UX practice. UX practice is moving towards the intelligence era. Still, the existing UX paradigm mainly aims at non-intelligent systems and lacks a systematic approach to address UX for designing and developing human-centered AI products and systems. The intelligence era has put forward new demands on the UX paradigm. This paper proposes a"UX 3.0"paradigm framework and the corresponding UX methodology for UX practice in the intelligence era. The"UX 3.0"paradigm framework includes four categories of emerging experiences in the intelligence era: ecosystem-based experience, innovation-enabled experience, AI-enabled experience, and human-AI interaction-based experience, each compelling us to enhance current UX practice in terms of design philosophy, scope, focus, and methodology. We believe that the"UX 3.0"paradigm helps enhance existing UX practice and provides methodological support for the research and applications of UX in developing human-centered AI systems. Finally, this paper looks forward to future work implementing the"UX 3.0"paradigm.

Qiang Yang

14 April 2021

From predictive medicine to autonomous driving, advances in Artificial Intelligence (AI) promise to improve people’s lives and improve society. As systems that utilize these advancesincreasingly migrated from research labs into the real world, new challenges emerged. For example, when and how should predictive models fit into physicians’ decision-makingworkflow such that the predictions impact them appropriately? These are challenges of translation: translating AI systems from systems that demonstrate remarkable technological achievements into real-world, socio-technical systems that serve human ends. My research focuses on this critical translation; on the user experience (UX) design of AI systems. The prevalence of AI suggests that the UX design community has effective design methods and tools to excel in this translation. While this is true in many cases, some challenges persist. For example, designers struggle with accounting for AI systems’ unpredictable errors, and these errors damage UX and even lead to undesirable societal impacts. UX designersroutinely grapple with technologies’ unanticipated technical or human failures, with a focus on mitigating technologies unintended consequences. What makes AI different fromother interactive technologies? – A critical first step in systematically addressing the UX design challenges of AI systems is to articulate what makes these systems so difficult to design in the first place. This dissertation delineates whether, when, and how UX of AI systems is uniquely difficult to design. I synthesize prior UX and AI research, my own experience designing human-AI interactions, my studies of experienced AI innovation teams in the industry, and my observations from teaching human-AI interaction. I trace the nebulous UX design challenges of AI back to just two root challenges: uncertainty around AI systems’ capabilities and the complexity of what systems might output. I present a framework that unravels their effects on design processes; namely AI systems’ “design complexity framework”. Using the framework,I identify four levels of AI systems. On each level, designers are likely to encounter a different subset of design challenges: Current design methods are most effective in eliciting, addressing, and evaluating the UX issues of Level 1 systems (probabilistic systems, systems with known capability with few possible outputs); Current methods are least effective for Level 4 systems (evolving, adaptive systems, systems that can learn from new data postdeployment and can produce complex outputs that resist abstraction or simulation). Level 2 and 3 are two intermediate levels. I further demonstrate the usefulness of this framework for UX research and practice through two case studies. In both cases, I engaged stakeholders in their real-world contexts and addressed a critical challenge in fitting cutting-edge AI systems into people’s everydaylives. The first is the design of a clinical decision-support system that can effectively collaborate with doctors in making life-and-death treatment decisions. It exemplifies Level 1 systems. The second project is an investigation of how Natural Language Generation systems might seamlessly serve the authors’ communicative intent. This illustrates Level 4 systems. It reveals the limits of UX design methods and processes widely in use today. By teasing apart the challenges of routine UX design and those distinctively needed for AI systems, the framework helps UX researchers and design tool makers to address AI systems’design challenges in a targeted fashion.

G. Ren Robert J. Moore Sungeun An

13 Juni 2022

User interfaces that take human conversation as their interaction metaphor work fundamentally differently than those that employ spatial metaphors, such as a desktop or a page. While the fundamental concept in visual interface design is the layout, the fundamental concept in conversational interface design is the sequence. Each provides for the overall structure of the user experience. In the past, user-interface designers have borrowed elements from the various areas of physical design. From industrial design, they have borrowed concepts such as, buttons, levers, wheels, and more from the print industry, they have borrowed the page, typography, iconography, illustration, and photography and more. These concepts from the physical world are then adapted to persistent, visual representations on a computer screen. Of course, virtual buttons are different from physical buttons and displayed words are different from printed words, but they evoke familiar ways of interacting with the real world that are then repurposed for a computer–user interface. And graphical user interface design, web design and mobile design are mature disciplines with shared standards and communities of practitioners. However, the spatial interaction metaphors of these areas of visual design largely do not apply to the design of conversational user interfaces (Moore & Arar, 2019; Moore et al., 2020; Murad et al., 2021; Yankelovich et al., 1995). Human conversational interaction consists primarily of sequences of words and embodied actions, not of layouts of visual elements. Buttons or pages cannot be represented as a stream of words produced by different parties. Conversational interfaces are more akin to command-line interaction, which involves sequences of specialized commands. The interaction conventions of visual design, graphical, web and mobile, were invented as an alternative to language-based interfaces and are not applicable to the design of conversational user experience. Where then can UX designers find inspiration when creating conversational interfaces with their sequences of natural-language utterances? In part, they can turn to Natural Language Processing (NLP), which provides mature methods for recognizing, classifying, and generating natural-language input and output (Chowdhury, 2003; Goldberg, 2017; Graves et al., 2013; López-Cózar et al., 2011; McTear et al., 2016; Reiter & Dale, 1997). These methods help designers understand what the user said and render realistic voice responses. However, NLP provides resources primarily for managing natural language, not for managing natural conversation. NLP addresses language use in any form: novels, poems, tweets, e-mails, conversations, etc. (Berg, 2015; Mitri, 2022; Peng et al., 2018; Zhang & Gao, 2017). Any bit of natural language, be it Spanish, English, Mandarin, etc., is analyzable with NLP.

E. Zamani Åsne Stige Yuzhen Zhu + 1 lainnya

29 Agustus 2023

PurposeThe aim of this article is to map the use of AI in the user experience (UX) design process. Disrupting the UX process by introducing novel digital tools such as artificial intelligence (AI) has the potential to improve efficiency and accuracy, while creating more innovative and creative solutions. Thus, understanding how AI can be leveraged for UX has important research and practical implications.Design/methodology/approachThis article builds on a systematic literature review approach and aims to understand how AI is used in UX design today, as well as uncover some prominent themes for future research. Through a process of selection and filtering, 46 research articles are analysed, with findings synthesized based on a user-centred design and development process.FindingsThe authors’ analysis shows how AI is leveraged in the UX design process at different key areas. Namely, these include understanding the context of use, uncovering user requirements, aiding solution design, and evaluating design, and for assisting development of solutions. The authors also highlight the ways in which AI is changing the UX design process through illustrative examples.Originality/valueWhile there is increased interest in the use of AI in organizations, there is still limited work on how AI can be introduced into processes that depend heavily on human creativity and input. Thus, the authors show the ways in which AI can enhance such activities and assume tasks that have been typically performed by humans.