Most designers using generative AI today cannot fully explain the decisions embedded in the work they are shipping. That should concern us. Generative AI entering design workflows is reshaping how designers think, what problems they can see and which capacities they are losing the habit of using. This is urgent – but not new. Design thinkers have wrestled with these questions for decades, and their frameworks offer guidance for what to protect and how. © 2026, Logos Verlag Berlin GmbH. All rights reserved.

In recent years, the concept of lifelong learning has been emphasized in relation to higher education, with a bearing idea of the possibility for the individual for a continuous, self-motivated pursuit of gaining knowledge for both personal and professional reasons, provided by higher education institutions (HEI:s). But how can this actually be done in practice? In this paper we present an ongoing project called MAISTR, which is a collaboration between Swedish HEI:s and industry with the aim of providing a number of flexible courses within the subjects of Artificial intelligence (AI), Design, and Innovation management, for professionals. Our aim is to describe how the project is setup to create new learning opportunities, including the development process and co-creation with industry, the core structure and the pedagogical design. Furthermore, we would like to discuss both challenges and opportunities that come with this kind of project, as well as reflecting on early stage outcomes. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

This book is about how to design AI-powered services where human experience and impact are at the center. It is written for you as a professional designer who wants to learn how to wield the power of AI to improve your design skills and creativity. The book also serves as course literature in Computer Science, Design, Informatics, and Human-Machine Interaction. Demystifying and ensuring that today’s AI technology is accessible and creates value requires both technical knowledge and an understanding of how humans experience technology. The success of AI-powered services relies on human-centered design. The book therefore introduces machine learning and other AI techniques to expand the toolbox for designers. It addresses how AI can improve existing services and how designers can use advanced AI to create services that can take the initiative and perform tasks on behalf of humans. The emphasis is on using data, algorithms, and computation as a design material that can give rise to new user experiences and effects for individuals, organizations, and society. The book provides 54 practical guidelines for designing successful human-centered AI-powered services.

Artificial Intelligence (AI) has provided user experience (UX) designers with a richer toolset. To use technologies such as Machine Learning (ML) that can expand their creative capacity to design intelligent services. ML has the capability to enhance the user experience, for example, by improving efficiency, personalization, and context-aware adaptation. However, research suggests ML as a challenging design material in UX practice, such as difficulties in comprehending data dependencies when prototyping, or the lack of tools and methods for evaluating adaptive user experiences. Previous research indicates that lack of knowledge transfer into the UX design practice may hamper innovative potential. This work aims to provide new insights on how designers think about – and experience – design for AI-powered services. It is important to make ML-powered services beneficial and sustainable for end-users, organizations, and society. Therefore, we explore UX designers’ reflections and experiences of using ML in a design context. We have performed nine deep explorative interviews with professional designers that work with ML. The respondents have different backgrounds, seniority, and work in different sectors. The collected interview material was qualitatively analyzed and resulted in five conceptual themes for how UX designers experience the design context surrounding AI-powered services: 1) Absence of competence, 2) Lack of incentive for competence development, 3) Challenges in articulating design criteria, 4) Mature vs. Immature clients, and 5) Lack of support for ethical concerns. We provide implications for how these themes affect the design context and practice. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

This study investigates interactive behaviors and communication cues of heavy goods vehicles (HGVs) and vulnerable road users (VRUs) such as pedestrians and cyclists as a means of informing the interactive capabilities of highly automated HGVs. Following a general framing of road traffic interaction, we conducted a systematic literature review of empirical HGV-VRU studies found through the databases Scopus, ScienceDirect and TRID. We extracted reports of interactive road user behaviors and communication cues from 19 eligible studies and categorized these into two groups: 1) the associated communication channel/mechanism (e.g., nonverbal behavior), and 2) the type of communication cue (implicit/explicit). We found the following interactive behaviors and communication cues: 1) vehicle-centric (e.g., HGV as a larger vehicle, adapting trajectory, position relative to the VRU, timing of acceleration to pass the VRU, displaying information via human-machine interface), 2) driver-centric (e.g., professional driver, present inside/outside the cabin, eye-gaze behavior), and 3) VRU-centric (e.g., racer cyclist, adapting trajectory, position relative to the HGV, proximity to other VRUs, eye-gaze behavior). These cues are predominantly based on road user trajectories and movements (i.e., kinesics/proxemics nonverbal behavior) forming implicit communication, which indicates that this is the primary mechanism for HGV-VRU interactions. However, there are also reports of more explicit cues such as cyclists waving to say thanks, the use of turning indicators, or new types of external human-machine interfaces (eHMI). Compared to corresponding scenarios with light vehicles, HGV-VRU interaction patterns are to a high extent formed by the HGV’s size, shape and weight. For example, this can cause VRUs to feel less safe, drivers to seek to avoid unnecessary decelerations and accelerations, or lead to strategic behaviors due to larger blind-spots. Based on these findings, it is likely that road user trajectories and kinematic behaviors will form the basis for communication also for highly automated HGV-VRU interaction. However, it might also be beneficial to use additional eHMI to compensate for the loss of more social driver-centric cues or to signal other types of information. While controlled experiments can be used to gather such initial insights, deeper understanding of highly automated HGV-VRU interactions will also require naturalistic studies. © 2022 Fabricius, Habibovic, Rizgary, Andersson and Wärnestål.

Companies face several challenges when adopting Artificial Intelligence (AI) technologies in their service and product offerings. Adaptive behavior that changes over time, such as personalization, affects end-user experiences in sometimes unpredictable ways, making designing for AI-powered experiences difficult to prototype and evaluate. To fully make use of AI technologies, companies need new tools, methods, and knowledge that relate to their specific design context. This includes learning how to adapt design and development processes to fit AI-powered services, communication in cross-functional teams, and continuous competency development strategies. This paper reports on an innovation and learning program called AI.m that facilitates practical learning about how to use emerging AI technologies for human-centered design. The program has been executed for 15 companies and evaluated using interviews with researchers, design practitioners, and company representatives that have worked within the learning program. This study suggests and verifies a productive and efficient learning environment and process where companies, university research departments, and design agencies collaborate to produce AI-powered services and at the same time develop their competency in AI and human-centered design. The qualitative analysis provides a set of categories of learning implications organized as a framework of prompts to help organizations develop AI and design capabilities. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Den här boken handlar om hur man designar AI-drivna tjänster där upplevelse och nytta för människan står i centrum. Den är skriven för yrkesverksamma designers som vill lära sig att hantera detta nya designmaterial för att bredda sin designkompetens och kreativitet. Boken är också tänkt att fungera som kurslitteratur inom informatik, datavetenskap och människa-maskininteraktion. För att avmystifiera och göra dagens AI-teknologi tillgänglig och värdeskapande krävs inte bara teknisk kunskap, utan också en förståelse för hur teknologin samverkar med människan. Design av AI-drivna tjänster utgår från människocentrerad design. Den närmar sig maskininlärning och andra AI-tekniker för att utöka verktygslådan för designers. Boken tar både upp hur enkla AI-stöd kan förbättra redan befintliga tjänster och hur mer avancerad AI kan användas för att skapa tjänster som kan ta eget initiativ och utföra uppgifter å människans vägnar. Tyngdpunkten ligger på att använda data, algoritmer och beräkning som ett eget designmaterial som kan ge upphov till nya typer av användarupplevelser och effekter för individ, organisation och samhälle. Pontus Wärnestål är docent i informatik vid Högskolan i Halmstad. Hans forskning fokuserar på människocentrerad AI och digital tjänsteinnovation. Pontus är också Design Director vid designbyrån inUse och har över 20 års erfarenhet av akademiskt och praktiskt designarbete.

The knowledge demand in the intersection between human-centered design and Artificial Intelligence (AI) has increased rapidly in both the private and public sectors. However, higher education is struggling to provide relevant content to already established senior professionals in a flexible and timely way. Mobile learning (m-learning) provides a promising way, but more research and practice is needed to design and launch efficient m-learning initiatives. In this paper, we share our experiences in designing and launching a flexible and self-paced podcast-based, free university course for established professionals on the topic of human-centered design and AI. We present our design process and highlight the findings from our on-going student survey evaluation. The questions addressed in this paper are: (1) How can educators design podcast-based courses for professionals in Higher Education? (2) What impact does a podcast-based format have on a student’s engagement in higher education? Preliminary results indicate that the podcast-based format is an appreciated form of flexible learning, and that the content of human-centered design and AI is of high interest for a multidisciplinary community of professional practitioners. © 2020, Springer Nature Switzerland AG.