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Towards an AI-Driven User Interface Design for Web ApplicationsAndré Costaa, Firmino Silvaa et al. · 2024The increasing exploitation of Artificial Intelligence (AI) technologies has enabled the design of user interfaces in a way that integrating artificial intelligence capabilities has become crucial in the modern digital landscape. Exploring the main features and best practices for designing user interfaces for Web applications, which effectively support and leverage AI functionalities, is currently one of the relevant topics in this context. This research work discusses the fundamental principles of user interface (UI) design, and the challenges posed by the integration of AI into web applications. It emphasizes the need to strike a balance between the AI advanced capabilities and the users' ability to understand and control the system. Furthermore, the paper highlights the importance of creating intuitive and engaging UI designs that empower users to interact with AI-driven features effortlessly. The study presents a comprehensive analysis of various UI design techniques specifically tailored for AI-enabled web applications user interfaces. Additionally, the paper explores the incorporation of AI-driven recommendation systems, personalized interfaces, and adaptive designs, which dynamically adapt to users' preferences and behavior. To validate the proposed user interface design principles, the study presents a proposal for a guidelines structure that promotes empirical evaluations through user studies and usability testing. Results collected via a survey based on measuring the effectiveness and user satisfaction of AI-enabled Web interfaces. User interfaces in real-life scenarios are presented and provides information on the impact of UI design decisions on user interaction and overall experience. The outcomes of this research work contribute to a deeper understanding of UI design for AI-supported Web applications user interfaces and offer practical guidelines for designers and developers. By embracing the suggested principles, organizations and designers can create Web interfaces that effectively harness the power of AI while prioritizing user-centricity, accessibility, and ethical considerations.

UX of AI & PersonalizationResearch Papers

AI Assistance for UX: A Literature Review Through Human-Centered AIYuwen Lu · 2024Recent advancements in HCI and AI research attempt to support user experience (UX) practitioners with AI-enabled tools. Despite the potential of emerging models and new interaction mechanisms, mainstream adoption of such tools remains limited. We took the lens of Human-Centered AI and presented a systematic literature review of 359 papers, aiming to synthesize the current landscape, identify trends, and uncover UX practitioners’ unmet needs in AI support. Guided by the Double Diamond design framework, our analysis un- covered that UX practitioners’ unique focuses on empathy building and experiences across UI screens are often overlooked. Simplistic AI automation can obstruct the valuable empathy-building pro- cess. Furthermore, focusing solely on individual UI screens without considering interactions and user flows reduces the system’s prac- tical value for UX designers. Based on these findings, we call for a deeper understanding of UX mindsets and more designer-centric datasets and evaluation metrics, for HCI and AI communities to collaboratively work toward effective AI support for UX. CCS CONCEPTS • General and reference →Surveys and overviews; • Human- centered computing →HCI design and evaluation methods; Interaction design process and methods; Empirical studies in interaction design; Systems and tools for interaction design.

UX of AI & PersonalizationResearch Papers

Creating Design Resources to Scafold the Ideation of AI ConceptsNur Yildirim · 2023Advances in artifcial intelligence have enabled unprecedented tech­ nical capabilities, yet making these advances useful in the real world remains challenging. We engaged in a Research through Design process to improve the ideation of AI products and services. We developed a design resource capturing AI capabilities based on 40 AI features commonly used across various domains. To probe its usefulness, we created a set of slides illustrating AI capabilities and asked designers to ideate AI-enabled user experiences. We also in­ corporated capabilities into our own design process to brainstorm concepts with domain experts and data scientists. Our research revealed that designers should focus on innovations where mod­ erate AI performance creates value. We refect on our process and discuss research implications for creating and assessing resources to systematically explore AI’s problem-solution space. CCS CONCEPTS • Human-centered computing → Interaction design process and methods.

UX of AI & PersonalizationResearch Papers

Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI ChallengesQiaosi Wang∗ · 2023Technology companies continue to invest in eforts to incorporate responsibility in their Artifcial Intelligence (AI) advancements, while eforts to audit and regulate AI systems expand. This shift towards Responsible AI (RAI) in the tech industry necessitates new practices and adaptations to roles—undertaken by a variety of prac­ titioners in more or less formal positions, many of whom focus on the user-centered aspects of AI. To better understand practices at the intersection of user experience (UX) and RAI, we conducted an interview study with industrial UX practitioners and RAI subject matter experts, both of whom are actively involved in addressing RAI concerns throughout the early design and development of new AI-based prototypes, demos, and products, at a large technology company. Many of the specifc practices and their associated chal­ lenges have yet to be surfaced in the literature, and distilling them ofers a critical view into how practitioners’ roles are adapting to meet present-day RAI challenges. We present and discuss three emerging practices in which RAI is being enacted and reifed in UX practitioners’ everyday work. We conclude by arguing that the emerging practices, goals, and types of expertise that surfaced in our study point to an evolution in praxis, with associated challenges that suggest important areas for further research in HCI. CCS CONCEPTS • Human-centered computing → Empirical studies in HCI;

UX of AI & PersonalizationResearch Papers

Data in design: How big data and thick data inform design thinking projectsStefano Magistretti b, Claudio Dell'Era b · 2023Scholars and practitioners have recognized that making innovation happen today requires renewed approaches focused on agility, dynamicity, and other organizational capabilities that enable firms to cope with uncertainty and complexity. In turn, the literature has shown that design thinking is a useful methodology to cope with ill- defined and wicked problems. In this study, we address the question of the little-known role of different types of data in innovation projects characterized by ill-defined problems requiring creativity to be solved. Rooted in qualitative observation (thick data) and quantitative analyses (big data), we investigate the role of data in eight design thinking projects dealing with ill-defined and wicked problems. Our findings highlight the practical and theoretical implications of eight practices that differently make use of big and thick data, informing academics and practitioners on how different types of data are utilized in design thinking projects and the related principles and practices.

UX of AI & PersonalizationResearch Papers

A study of UX practitioners roles in designing real-world, enterprise ML systemsSabah Zdanowsk · 2022Opportunities for AI and machine learning (ML) are vast in current interactive systems development. However, comparatively little is known about how functionality for the system behind the interface is designed and how design methodologies such as user-centered de- sign have influence. This research focuses on how interdisciplinary teams that include UX practitioners design real-world enterprise ML systems outside of big technology companies. We conducted a survey with product managers, and interviews with interdisci- plinary teams and individual UX practitioners. The findings show that nontechnical UX practitioners are highly capable in designing AI/ML systems. In addition to applying UX and interaction design expertise to make decisions regarding functionality, they employ skills that aid collaboration across interdisciplinary teams. How- ever, our findings suggest existing HCI design techniques such as prototyping and simulating complexity of enterprise ML systems are insufficient. We propose adaptations to design practices and conclude that some existing research should be reconsidered. CCS CONCEPTS • Human-centered computing →Empirical studies in HCI; • Software and its engineering →Designing software.

UX of AI & PersonalizationResearch Papers

AUIT – the Adaptive User Interfaces Toolkit for Designing XR ApplicationsAnna Maria Feit · 2022Adaptive user interfaces can improve experiences in Extended Re­ ality (XR) applications by adapting interface elements according to the user’s context. Although extensive work explores diferent adaptation policies, XR creators often struggle with their imple­ mentation, which involves laborious manual scripting. The few available tools are underdeveloped for realistic XR settings where it is often necessary to consider conficting aspects that afect an adap­ tation. We fll this gap by presenting AUIT, a toolkit that facilitates the design of optimization-based adaptation policies. AUIT allows creators to fexibly combine policies that address common objec­ tives in XR applications, such as element reachability, visibility, and consistency. Instead of using rules or scripts, specifying adaptation policies via adaptation objectives simplifes the design process and This work is licensed under a Creative Commons Attribution International 4.0 License. UIST ’22, October 29-November 2, 2022, Bend, OR, USA © 2022 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-9320-1/22/10. https://doi.org/10.1145/3526113.3545651 enables creative exploration of adaptations. After creators decide which adaptation objectives to use, a multi-objective solver fnds appropriate adaptations in real-time. A study showed that AUIT allowed creators of XR applications to quickly and easily create high-quality adaptations. CCS CONCEPTS • Human-centered computing → Systems and tools for in­ teraction design; Gestural input; Mixed / augmented reality; Virtual reality; User interface toolkits.

UX of AI & PersonalizationResearch Papers

How Designers Find Their Ways in Shaping Algorithmic SystemsJeremie Poiroux · 2022Digital products and services now commonly include algorithmic personalization or recommendation features. This has raised concerns of reduced user agency and their unequal treat- ment. Previous research hence called for increasing the participation of, among others, designers in the development of these features. To achieve this, researchers have suggested the development of better educational material and tools to enable prototyping with data and machine learning models. However, previous studies also suggest designers may find other ways to impact the development and implementation of such features, for instance through collaboration with data scientists. We build on that line of inquiry, through 19 in-depth interviews with designers working in small to large international companies to investigate how they actually intervene in shaping products includ- ing algorithmic features. We outline how designers intervene at different levels of the algorithmic systems: at a technical level, for instance by providing better input data ; at an interface or infor- mation architecture level, sometimes circumventing algorithmic discussions ; or at a organizational level, re-centering the outcome of algorithmic systems around product-centric questions. Building upon these results, we discuss how supporting designers engagement and influence on algorithmic systems may not only be a problem of technical literacy and adequate tooling. But that it may also involve a better awareness of the power of interface work, and a stronger negotiation skills and power literacy to engage in strategic discussions. Key Words: Agency, Artificial intelligence, Interventions, Machine learning, Design, User expe- rience, Algorithmic systems (2024) 33:173–204 J´er´emie Poiroux et al.

UX of AI & PersonalizationResearch Papers

Personalization in Practice: Methods and ApplicationsDmitri Goldenberg · 2021Personalization is one of the key applications in machine learning with widespread usage across e-commerce, entertainment, pro- duction, healthcare and many other industries. While various ma- chine learning techniques present novel state-of-the-art advances and super-human performance year-over-year, personalization and recommender-systems applications are often late-adopters of novel solutions due to problem hardness and implementation complexity. This tutorial presents recent advances across the personaliza- tion industry and demonstrates their practical applications in real case-studies of world-leading online platforms. Key trends such as deep learning, causality and active exploration with bandits are depicted with real examples and demonstrated alongside their busi- ness considerations and implementation challenges. Rising topics like explainability, fairness, natural interfaces and content gener- ation are covered, touching on aspects of both technology and user experience. Our tutorial relies on recent advances in the field and on work conducted at Booking.com, where we implement per- sonalization models on one of the world’s leading online travel platform. CCS CONCEPTS • Information systems →Personalization; Recommender sys- tems.

UX of AI & PersonalizationResearch Papers

UX designers pushing AI in the enterprise: a case for adaptive UIsJohn Zimmerman · 2020- → Adaptive user interfaces (AUIs) provide an untapped opportunity for UX designers to innovate with AI. - → AUIs seem well suited for enterprise applications, where it is easier to estimate the value of saving a worker time by automating mundane and repetitive tasks. - → AUIs raise new ethical questions about user privacy and about the unintended impacts of making workers faster at their work.

UX of AI & PersonalizationResearch Papers

AI and UXGavin Lew Robert M., Schumacher Jr. · 2020This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.

UX of AI & PersonalizationResearch Papers

A Comparative Analysis of Industry of Human-AI Interaction GuidelinesHaekyu Park · 2019With the recent release of AI interaction guidelines from Apple, Google, and Microsoft, there is clearly interest in understanding the best practices in human-AI interaction. However, industry standards are not determined by a single company, but rather by the synthesis of knowledge from the whole community. We have surveyed all of the design guidelines from each of these major companies and developed a single, unified structure of guidelines, giving develop- ers a centralized reference. We have then used this framework to compare each of the surveyed companies to find differences in areas of emphasis. Finally, we encourage people to contribute additional guidelines from other companies, academia, or individuals, to pro- vide an open and extensible reference of AI design guidelines at [https://ai-open-guidelines.readthedocs.io/](https://ai-open-guidelines.readthedocs.io/). Index Terms: Human-centered computing—Human computer in- teraction (HCI); Computing methodologies—Artificial intelligence

UX of AI & PersonalizationResearch Papers

Identifying the Intersections: User Experience + Research Scientist Collaboration in a Generative Machine Learning InterfaceClaire Kayacik, Sherol Chen et al. · 2019Creative generative machine learning interfaces are stronger when multiple actors bearing different points of view actively contribute to them. User experience (UX) research and design involvement in the creation of machine learning (ML) models help ML research scientists to more effectively identify human needs that ML models will fulfill. The People and AI Research (PAIR) group within Google developed a novel program method in which UXers are embedded into an ML research group for three months to provide a human-centered perspective on the creation of ML models. The first full-time cohort of UXers were embedded in a team of ML research scientists focused on deep generative models to assist in music composition. Here, we discuss the structure and goals of the program, challenges we faced during execution, and insights gained as a result of the process. We offer practical suggestions Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). CHI’19 Extended Abstracts, May 4–9, 2019, Glasgow, Scotland UK © 2019 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-5971-9/19/05. https://doi.org/10.1145/3290607.3299059 CHI 2019 Case Study CHI 2019, May 4–9, 2019, Glasgow, Scotland, UK CS09, Page 1 for how to foster communication between UX and ML research teams and recommended UX design processes for building creative generative machine learning interfaces. CCS CONCEPTS • Human-centered computing; • Human computer interaction; • Interaction paradigms; • Collaborative interaction;

UX of AI & PersonalizationResearch Papers

Investigating How Experienced UX Designers Effectively Work with Machine LearningQian Yang · 2018Machine learning (ML) plays an increasingly important role in improving a user’s experience. However, most UX practitioners face challenges in understanding ML’s capabilities or envisioning what it might be. We interviewed 13 designers who had many years of experience designing the UX of ML-enhanced products and services. We probed them to characterize their practices. They shared they do not view themselves as ML experts, nor do they think learning more about ML would make them better designers. Instead, our participants appeared to be the most successful when they engaged in ongoing collaboration with data scientists to help envision what to make and when they embraced a data-centric culture. We discuss the implications of these findings in terms of UX education and as opportunities for additional design research in support of UX designers working with ML. Author Keywords User Experience Design; UX Practice; Machine Learning; Design Material; Interaction Design. ACM Classification Keywords H.5.m. Information Interfaces and Presentation (e.g. HCI): Miscellaneous

UX of AI & PersonalizationResearch Papers

Artificial Intelligence (AI) for User Experience (UX) design: A systematic literature review and future reseaarch agendaAsne Stige · 2018Purpose The 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/Approach This 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. Findings Our 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. We also highlight the ways in which AI is changing the UX design process through illustrative examples. Originality/value While 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, we show the ways in which AI can enhance such activities and assume tasks that have been typically performed by humans.

UX of AI & PersonalizationResearch Papers

UX Design Innovation: Challenges for Working with Machine Learning as a Design MaterialGraham Dove, Kim Halskov · 2017Machine learning (ML) is now a fairly established technology, and user experience (UX) designers appear regularly to integrate ML services in new apps, devices, and systems. Interestingly, this technology has not experienced a wealth of design innovation that other technologies have, and this might be because it is a new and difficult design material. To better understand why we have witnessed little design innovation, we conducted a survey of current UX practitioners with regards to how new ML services are envisioned and developed in UX practice. Our survey probed on how ML may or may not have been a part of their UX design education, on how they work to create new things with developers, and on the challenges they have faced working with this material. We use the findings from this survey and our review of related literature to present a series of challenges for UX and interaction design research and education. Finally, we discuss areas where new research and new curriculum might help our community unlock the power of design thinking to re-imagine what ML might be and might do. Author Keywords UX practice; design material; machine learning; interaction design; ACM Classification Keywords H.5.m. Information interfaces and presentation (e.g., HCI): Miscellaneous;

UX of AI & PersonalizationResearch Papers

User Experience for Model-Driven Engineering:Future Directions · 2017Since its infancy, Model Driven Engineering (MDE) research has primarily focused on technical issues. Although it is becoming increasingly common for MDE research papers to evaluate their theoretical and practical solutions, extensive usability studies are still uncommon. We observe a scarcity of User eXperience (UX)-related research in the MDE community, and posit that many existing tools and languages have room for improvement with respect to UX [26], [44], [37], where UX is a key focus area in the software development industry. We consider this gap a fundamental problem that needs to be addressed by the community if MDE is to gain widespread use. In this vision paper, we explore how and where UX fits into MDE by considering motivating use cases that revolve around different dimensions of integration: model integration, tool integration, and integration between process and tool support. Based on the literature and our collective experience in research and industrial collaborations, we propose future directions for addressing these challenges.

UX of AI & PersonalizationResearch Papers

Research Through Design as a Method for InteractionJohn Zimmerman, Jodi Forlizzi et al. · 2007For years the HCI community has struggled to integrate design in research and practice. While design has gained a strong foothold in practice, it has had much less impact on the HCI research community. In this paper we propose a new model for interaction design research within HCI. Following a research through design approach, designers produce novel integrations of HCI research in an attempt to make the right thing: a product that transforms the world from its current state to a preferred state. This model allows interaction designers to make research contributions based on their strength in addressing under-constrained problems. To formalize this model, we provide a set of four lenses for evaluating the research contribution and a set of three examples to illustrate the benefits of this type of research. Author Keywords design, interaction design, interaction design research, HCI research, research through design, wicked problems, design theory, design method ACM Classification Keywords H5.2. User Interfaces: Theory and methods.

UX of AI & PersonalizationResearch Papers

User experience - A research agendaMarc Hassenzahl, Noam Tractinsky · 2006Over the last decade, ‘user experience’ (UX) became a buzzword in the field of human–computer interaction (HCI) and interaction design. As technology matured, interactive products became not only more useful and usable, but also fashionable, fascinating things to desire. Driven by the impression that a narrow focus on interactive products as tools does not capture the variety and emerging aspects of technology use, practitioners and researchers alike, seem to readily embrace the notion of UX as a viable alternative to traditional HCI. And, indeed, the term promises change and a fresh look, without being too specific about itsdefinite meaning. The present introduction to the special issue on ‘Empirical studies of the user experience’ attempts to give a provisional answer to the question of what is meant by ‘the user experience’. It provides a cursory sketch of UX and how we think UX research will look like in the future. It is not so much meant as a forecast of the future, but as a proposal – a stimulus for further UX research.

UX of AI & PersonalizationResearch Papers

The Behaviour Change Wheel: a new method for characterising and designing behaviour change interventions2011Background: Improving the design and implementation of evidence-based practice depends on successful behaviour change interventions. This requires an appropriate method for characterising interventions and linking them to an analysis of the targeted behaviour. There exists a plethora of frameworks of behaviour change interventions, but it is not clear how well they serve this purpose. This paper evaluates these frameworks, and develops and evaluates a new framework aimed at overcoming their limitations. Methods: A systematic search of electronic databases and consultation with behaviour change experts were used to identify frameworks of behaviour change interventions. These were evaluated according to three criteria: comprehensiveness, coherence, and a clear link to an overarching model of behaviour. A new framework was developed to meet these criteria. The reliability with which it could be applied was examined in two domains of behaviour change: tobacco control and obesity. Results: Nineteen frameworks were identified covering nine intervention functions and seven policy categories that could enable those interventions. None of the frameworks reviewed covered the full range of intervention functions or policies, and only a minority met the criteria of coherence or linkage to a model of behaviour. At the centre of a proposed new framework is a ‘behaviour system’ involving three essential conditions: capability, opportunity, and motivation (what we term the ‘COM-B system’). This forms the hub of a ‘behaviour change wheel’ (BCW) around which are positioned the nine intervention functions aimed at addressing deficits in one or more of these conditions; around this are placed seven categories of policy that could enable those interventions to occur. The BCW was used reliably to characterise interventions within the English Department of Health’s 2010 tobacco control strategy and the National Institute of Health and Clinical Excellence’s guidance on reducing obesity. Conclusions: Interventions and policies to change behaviour can be usefully characterised by means of a BCW comprising: a ‘behaviour system’ at the hub, encircled by intervention functions and then by policy categories. Research is needed to establish how far the BCW can lead to more efficient design of effective interventions. Background Improving the implementation of evidence-based prac- tice and public health depends on behaviour change. Thus, behaviour change interventions are fundamental to

Emotional DesignResearch Papers

Design and Emotion: Time for a New Direction?Terence Love · 2004This paper describes research investigating why the extensive effort in Design and Emotion research and publications has not had more significant effect in improving design theory and practice in spite of the large amounts of funding expended, the number of research projects and research centers and the large number of publications. The analyses point to two foundational research issues that appear to have been overlooked and which point to why Design and Emotion research culture is failing to make radical and significant impacts on design theory, design research, design practice and design education across all the major fields of design in the art and design, technical design and other design sectors. The paper concludes with a brief outline of the changes likely to make the Design and Emotion field more effective in improving design outcomes, design practice, design research and design theory. Key words: Design and Emotion, theory failure, design practice failure, cognitive neuroscience.

Emotional DesignResearch Papers