The use of artificial intelligence (AI) in the user experience (UX) design process is reshaping design practice and underlining the importance of understanding how AI is used to support the UX design process. This systematic literature review has identified and analyzed 83 empirical studies to answer the question: How does human–computer interaction research literature address the use, development, and perception of AI-supported UX design tools? Findings illustrate that the most common use for AI is to boost efficiency in evaluative activities of the design process. In addition, there is some use of generative AI tools to support ideation and prototyping or to simulate users. The reviewed literature underscores issues of potential overreliance on automation and a technology-first approach in developing AI design tools without involving designers. The study outlines future directions for developing AI-enabled design tools that support creativity and design work while preserving human-centric values. Copyright © 2025 Yi Luo. Advances in Human-Computer Interaction published by John Wiley & Sons Ltd.

Data-driven design is believed to be empowered by machine learning (ML) with advanced pattern classification and prediction. However, research on how ML can be used to support automotive human-machine interface (HMI) design is lacking. We presented a case study of truck HMI design to understand the current data use and expectations of ML in the design process. Findings show decentralized data practices, the role of expertise in decision-making, and the envisioned reactive use of ML, where we underscore the implications for advancing human-ML collaboration in designing future truck HMI systems. © 2024 Proceedings of the Design Society. All rights reserved.

As artificial intelligence continues to permeate working life, the integration of AI in truck UX design is gaining prominence. While the majority of AI research, especially in the field of Explainable AI (XAI), is rooted in a technical perspective, this work explores and unpacks the user perspective by addressing the research question: “How do UX designers of truck HCI systems perceive AI explanations in an AI-powered data analytics platform?”. To address this question, a prototype of such a platform was co-designed and evaluated by 17 experts in truck UX design. Findings highlight that for AI explanations to be perceived as useful, they need to be understandable, contextually relevant, and verifiable, with the ability to dynamically adapt to users’ evolving knowledge and objectives. These findings extend prior research by emphasizing the importance of contextual and human-centered values in designing and developing AI-enabled systems with explainability, and by calling for future transdisciplinary collaboration to address evolving contextual user needs in truck UX design and beyond. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

The rapid growth of artificial intelligence (AI) in recent years is observable by its increasing adoption in various domains. Generative AI technologies have furthered this trend, yet AI remains opaque by nature and design. This poses challenges for designers of AI-enabled products, who need to understand materials properties to design effectively, emphasising a need for AI literacy, a multidimensional competency encompassing ethical, cognitive, and practical skills. The study explores how three distinct design practices, Research through Design, Human-Centred Design, and Human-Centred Artificial Intelligence, can help students develop AI literacy. We carried out the study with a class of eighteen graduate students, who engaged in designing an AI-enabled service, applied the three design practices, and documented their design process. By analysing their design deliverables, the study discusses how students evolved their AI literacy over one design course and provides preliminary insights into how a designerly approach can help cultivate AI literacy.

Designers face opportunities and challenges in leveraging AI to make sense of increasingly available data. There is limited research on how designers engage with AI in data sensemaking to inform design decisions, especially in safety-critical contexts. To address this knowledge gap, we co-designed and evaluated an AI-supported prototype that supports data exploration using truck interaction design as a case study. Our analysis highlighted a bidirectional relationship between AI and data, where designers used AI to gain an overview of user scenarios, and used data to contextualize and validate AI outputs. We identified three sensemaking styles, illustrating how designers flexibly orchestrated expertise, data, and interaction to interpret AI outputs. Our findings contribute to an in-depth characterization of AI-supported data sensemaking and offer implications for designing tools that empower diverse, situated, and critical engagement with data through AI in high-stakes settings. © 2026 Copyright held by the owner/author(s).