A "User Experience 3.0 (UX 3.0)" Paradigm Framework: User Experience Design for Human-Centered AI Systems

Wei Xu

13 Februari 2023

The field of user experience (UX) based on the design philosophy of"user-centered design"is moving towards the intelligence era. Still, the existing UX paradigm mainly aims at non-intelligent systems and lacks a systematic approach to UX for intelligent systems. Throughout the development of UX, the UX paradigm shows the evolution characteristics of the cross-technology era. At present, the intelligence era has put forward new demands on the UX paradigm. For this reason, this paper proposes a"UX 3.0"paradigm framework and the corresponding UX methodology system in the intelligence era. The"UX 3.0"paradigm framework includes five categories of UX methods: ecological experience, innovation-enabled experience, AI-enabled experience, human-AI interaction-based experience, and human-AI collaboration-based experience methods, each providing corresponding multiple UX paradigmatic orientations. The proposal of the"UX 3.0"paradigm helps improve the existing UX methods and provides methodological support for the research and applications of UX in developing intelligent systems. Finally, this paper looks forward to future research and applications of the"UX 3.0"paradigm.

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.

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.

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.

Mikael Wiberg E. Bergqvist

10 November 2023

Interaction design/HCI seems to be at a crossroads. On the one hand, it is still about designing for engaging user experiences (UX). Still, on the other hand, it seems to be increasingly about reducing interaction and automating human–machine interaction through the use of AI and other new technologies. In this paper, we explore this seemingly unavoidable gap. First, we discuss the fundamental design rationality underpinning interaction and automation of interaction from the viewpoints of classic theoretical standpoints. We then illustrate how these two come together in interaction design practice. Here we examine four examples from already published research on automation of interaction, including how different levels of automation of interaction affect or enable new practices, including coffee making, self-tracking, automated driving, and conversations with AI-based chatbots. Through an interaction analysis of these four examples, we show (1) how interaction and automation are combined in the design, (2) how interaction is dependent on a certain level of automation, and vice versa, and (3) how each example illustrates a different balance between, and integration of interaction and automation. Based on this analysis, we propose a two-dimensional design space as a conceptual construct that takes these aspects into account to understand and analyze ways of combining interaction and automation in interaction design. We illustrate the use of the proposed two-dimensional design space, discuss its theoretical implications, and suggest it as a useful tool—when designing for engaging user experiences (UX), with interaction and automation as two design materials.