Filtered by themeUsability33 papersclear ✕

Transforming User Experience (UX) through Artificial Intelligence (AI) in Interactive MediaYingchia Liu, Yang Xu, Ranze Song · 2024This paper discusses the application and advantages of artificial intelligence technology in digital media interactive product design. Firstly, the development background of artificial intelligence technology and its promoting effect on design innovation is introduced, and the application of new technologies, such as generative adversarial networks in art creation and design personalization, is definitely analyzed. It then explores in detail the ability of AI to break traditional constraints in interactive design, innovate design, and optimize user experience, especially in digital media. Finally, the case study of Microsoft Cortana shows the method of classifying and processing user queries by machine learning system and its experimental results. The research of this paper provides the theoretical basis and empirical support for the application of artificial intelligence technology in the future interactive design of digital media and has important academic and practical significance.

UX of AI & PersonalizationResearch Papers

A Systematic Review on Human and ComputerRESHNA NANDIPI · 2024As technology continues to advance at an unprecedented pace, the interaction between humans and computers has become an integral part of our daily lives. This study provides a comprehensive review of the evolving landscape of human- computer interaction (HCI) research, focusing on the key concepts, methodologies, and advancements in this interdisciplinary field. The review begins by presenting an overview of the historical evolution of HCI, tracing its roots from early command-line interfaces to the current era of intuitive touchscreens and voice recognition systems. The fundamental principles of HCI, including usability, accessibility, and user- centered design, are examined in detail, highlighting their significance in enhancing the overall user experience. Moreover, the review explores various interaction modalities that have emerged over the years, such as graphical user interfaces, haptic feedback, augmented reality, and virtual reality. It examines the strengths, limitations, and potential applications of these modalities, shedding light on the future possibilities they hold for human-computer interaction. Furthermore, the review delves into the emerging trends in HCI research, including natural language processing, gesture recognition, machine learning, and affective computing. These advancements have paved the way for more personalized and adaptive interfaces, enabling computers to understand and respond to human emotions and intentions, thereby fostering deeper levels of engagement and satisfaction. The study also addresses the challenges and ethical considerations associated with human-computer interaction, such as privacy concerns, data security, and algorithmic biases. It emphasizes the importance of designing inclusive and ethical systems that respect users' rights and values.

UX of AI & PersonalizationResearch Papers

Predicting the usability of mobile applications using AI tools: the rise of large user interface models, opportunities, and challengesIndustry (EDI40) · 2024This article proposes the so-called large user interface models (LUIMs) to enable the generation of user interfaces and prediction of usability using artificial intelligence in the context of mobile applications. To this end, we synergized an integrated framework for the effective testing of the usability of mobile applications following a selective review of the most influential models of mobile usability testing. Next, we identified and analysed 13 recent AI tools that generate user interfaces for mobile apps, and systematically tested these tools to identify their AI capabilities. Our striking findings demonstrate that current generative UI tools fail to address mobile usability attributes, such as efficiency, learnability, effectiveness, satisfaction, and memorability. Our large UI models’ architecture proposes to leverage the capabilities of large language models, large vision models, and large code models to overcome the challenges of AI-driven UI/UX design and front-end implementations. This fascinating UI eco-system must be augmented with sufficient UI data and multi-sensory input regarding user behaviour to train the models. We anticipate LUIMs to create ample opportunities, like expedited frontend software development, enhanced personalised user experience, and wider accessibility of smart technologies. However, the research challenges hindering the UI generation and usability prediction of mobile apps include the seamless integration of complex generative AI models, semantic understanding of non-uniform visual designs, scarcity of UX datasets, and modelling of realistic user interactions. 672 Abdallah Namoun et al. / Procedia Computer Science 238 (2024) 671–682

UX of AI & PersonalizationResearch Papers

Application of Artificial Intelligence in Interactive UI DesignSha Liang · 2024The purpose of this study is to deeply explore the influence of artificial intelligence (AI) application in interactive User Interface (UI) design, and evaluate its effect in conceptual design, prototype design and final design through experiments and analysis. By introducing generative design tools, machine learning algorithms and sentiment analysis technology, this study evaluates the role of AI in improving design efficiency, personalizing user experience, increasing creativity and enhancing emotional resonance of users. In the evaluation of design effect, it is found that the effect score is significantly improved after the introduction of AI in each design stage. The design scheme generated by the designer through AI is more in line with the needs of users, the design accuracy is improved, and the creativity is also increased compared with the traditional design. In terms of personalized user experience, we use machine learning algorithm to adjust interface elements according to user behavior to provide a more personalized user experience. The experimental results show that personalized design can significantly improve user satisfaction and interaction efficiency. Through the emotional analysis technology, the language and emotional tone of the design are adjusted, and the emotional resonance of users is improved. Designers can better express their emotions and make the design more in line with the emotional needs of users. On the whole, this study provides an in-depth empirical study on the application of AI in UI design, and provides beneficial enlightenment for future intelligent design and user experience research.

UX of AI & PersonalizationResearch Papers

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

Machine Learning Algorithms for Improved Product Design User ExperienceXueli Wang, Bo Hu · 2024With the rapid advancement of technology and the increasing demand for user-centric products, the integration of machine learning techniques has become imperative. This paper explores the transformative potential of integrating Particle Swarm Optimization (PSO), Deep Reinforcement Learning (DRL), and other machine learning algorithms such as neural networks, decision trees, and support vector machines into product design processes. Our novel hybrid framework leverages PSO’s global search capabilities and DRL’s adaptive learning to optimize product designs in a manner that traditional methods cannot achieve. By employing predictive modeling, clustering, and recommendation systems, designers can gain valuable insights into user needs and preferences, facilitating the creation of more intuitive and personalized products. We demonstrate that this integrated approach significantly improves design efficiency and user satisfaction. Key findings include a 25% reduction in design iteration time and a 30% increase in user satisfaction scores compared to traditional optimization methods. Additionally, our methodology provides a flexible and scalable solution adaptable to various product design contexts, showcasing its broad applicability and effectiveness. The incorporation of real-time feedback mechanisms allows for continuous refinement and adaptation of product designs to meet evolving user expectations. This study contributes to the field by presenting a comprehensive, multi-technique optimization framework that bridges existing gaps and sets a new standard for user-centric product design optimization. Ultimately, this research underscores the significance of embracing machine learning as a powerful tool for revolutionizing the product design landscape and delivering superior user experiences. INDEX TERMS Machine learning algorithms, product design, user experience enhancement, user data analysis, predictive modeling.

UX of AI & PersonalizationResearch Papers

Bibliometric and Visualized Analysis of User Experience Design Research: From 1999 to 2019Rui Li · 2022With increasing research attention to user experience (UX), UX design (UXD) has gained concomitant interest. This study systematically reviews UXD research using a bibliometrical knowledge map. We collected and reviewed 14,825 documents to analyze UXD research from five perspectives: keyword trends, reference co-citation, author co-citation, categories, and author institutes. (a) UXD, as a research term, broadens the boundaries of experience and design and brings them together. (b) Publications in UXD focus on user, business, and technology orientation. (c) Author co-citation analysis reveals “invisible college networks” among UXD scholars. (d) Computer Science and Engineering are the most significant majors in UXD, although interdisciplinary research is common. (e) Research from the United States dominates. This review recognizes research gaps and future trends for conceptualizing and assessing UXD skills. The findings can benefit researchers, curriculum designers, and instructors.

UX of AI & PersonalizationResearch Papers

Measuring and Improving User Experience Through Artificial Intelligence-Aided Design[Unknown · 2020This paper aims to propose a methodology for measuring user experience (UX) by using artificial intelligence-aided design (AIAD) technology in mobile application design. Unlike the traditional assistance design tools, AIAD focuses on the rational use of artificial intelligence (AI) technology to measure and improve UX since conventional data collection methods (such as user interview and user observation) for user behavior data are inefficient and time-consuming. We propose to obtain user behavior data from logs of mobile application. In order to protect the privacy of users, only a few dimensions of information is used in the process of browsing and operating mobile application. The goal of the proposed methodology is to make the deep neural network model simulate the user's experience in the process of operating a mobile application as much as possible. We design and use projected pages of application to train neural networks for specific tasks. These projected pages consist of the click information of all users in the process of completing a certain task. Thus, features of user behavior can be aggregated and mapped in the connection layers and the hidden layers. Finally, the optimized design is executed on the social communication application to verify the efficiency of the proposed methodology.

UX of AI & PersonalizationResearch Papers

Combinatorial Optimization of Graphical User Interface DesignsAntti Oulasvirt · 2020| The graphical user interface (GUI) has become the prime means for interacting with computing systems. It lever- ages human perceptual and motor capabilities for elementary tasks such as command exploration and invocation, informa- tion search, and multitasking. For designing a GUI, numerous interconnected decisions must be made such that the out- come strikes a balance between human factors and technical objectives. Normally, design choices are specified manually and coded within the software by professional designers and developers. This article surveys combinatorial optimization as a flexible and powerful tool for computational generation and adaptation of GUIs. As recently as 15 years ago, applications were limited to keyboards and widget layouts. The obstacle has been the mathematical definition of design tasks, on the one hand, and the lack of objective functions that capture essential aspects of human behavior, on the other. This article presents definitions of layout design problems as integer programming tasks, a coherent formalism that permits identification of problem types, analysis of their complexity, and exploitation of known algorithmic solutions. It then surveys advances in Manuscript received April 8, 2019; revised October 9, 2019 and January 12, 2020; accepted January 17, 2020. This work was supported in part by the European Research Council (ERC) through the European Union’s Horizon 2020 Research and Innovation Program under Grant 637991 and in part by the Academy of Finland projects Bayesian Artefact Design (BAD) and Human Automata. (Corresponding author: Antti Oulasvirta.) Antti Oulasvirta is with the Department of Communications and Networking, School of Electrical Engineering, Aalto University, 02150 Espoo, Finland, and also with the Finnish Center for Artificial Intelligence (FCAI), 02015 Espoo, Finland (e-mail: antti.oulasvirta@aalto.fi). Niraj Ramesh Dayama and Morteza Shiripour are with the Department of Communications and Networking, School of Electrical Engineering, Aalto University, 02150 Espoo, Finland. Maximilian John and Andreas Karrenbauer are with the Max Planck Institute for Informatics, 66123 Saarbrücken, Germany. Digital Object Identifier 10.1109/JPROC.2020.2969687 formulating evaluative functions for common design-goal foci such as user performance and experience. The convergence of these two advances has expanded the range of solvable problems. Approaches to practical deployment are outlined with a wide spectrum of applica

UX of AI & PersonalizationResearch Papers

Retroactive Transfer Phenomena inAlternating User Interfaces · 2020We investigated retroactive transfer when users alternate be­ tween different interfaces. Retroactive transfer is the influence of a newly learned interface on users’ performance with a pre­ viously learned interface. In an interview study, participants described their experiences when alternating between different interfaces, e.g. different operating systems, devices or tech­ niques. Negative retroactive transfer related to text entry was the most frequently reported incident. We then reported a lab­ oratory experiment that investigated the impact of similarity between two abstract keyboard layouts, and the number of alternations between them, on retroactive interference. Results indicated that even small changes in the interference interface produced a significant performance drop for the entire previ­ ously learned interface. The amplitude of this performance drop decreases with the number of alternations. We suggest that retroactive transfer should receive more attention in HCI, as the ubiquitous nature of interactions across applications and systems requires users to increasingly alternate between similar interfaces. Author Keywords skill transfer; retroactive interference; keyboard layout CCS Concepts •Human-centered computing → User studies;

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 Long-Term Evaluation of Adaptive Interface Design for Mobile Transit InformationAlexander Haig · 2020Personalization of user experience has a long history of success in the HCI community. More recently the community has focused on adaptive user interfaces, supported by machine learning, that reduce interaction efforts and improves user experience by collaps- ing transactions and pre-filtering results. However, generally, these more recent results have only been demonstrated in the laboratory environment. In this paper, we share the case of a deployed mobile transit app that adapts based on users’ previous usage. We examine the impact of adaptation, both good and bad, and user abandon- ment rates. We conducted an 18-month assessment where 2,616 participants (with and without vision impairments) were recruited and participated in an A/B study. Finally, we draw some insights on some unusual effects that appear over the long term. CCS CONCEPTS • Human-centered computing →Empirical studies in inter- action design.

UX of AI & PersonalizationResearch Papers

Self-adaptation of Workflow Business Software to the User's Requirements and BehaviorUser Interface, User Experience et al. · 2020The main goal of the presented paper is to propose a method for adaptation of the user interface of workflow software to increase its efficiency, reduce the number of errors, and improve its UX. The authors assumed that the system adaptation will be achieved by application to intelligent methods for modeling user as well as system. In order to do this, a special tool for data gathering has been designed and in the next steps of the research, this tool will be also implemented in a real environment. A unique value of the paper is that after many years of theoretical research, the first attempt to implement a practical solution for self-adapting and the personalized interface for workflow systems was done. Janusz Sobecki et al. / Procedia Computer Science 176 (2020) 3506–3513 3507 adaptation [3]. This AI approach for user interface adaptation may be also enhanced with the application of user interface ontologies [4]. 1.1. Related Works In user interface design we should start with proper user model [1] however, we should always remember that ever-increasing number of users, especially of web-based systems, also brings the increase of differences among their users and interaction styles [3]. The user differences may reflex their demographical, psychological as well as sociological user characteristics, which have an influence on the users' information needs and interaction habits. The consequence of these differences is causing difficulties in modeling these users in the standard way [1], so since many years more sophisticated solutions have been proposed, for example, one enhanced with the application of ontologies [5], which was further applied in SOA systems development [4] and [6]. The information systems design and development have been enhanced by ontologies on at least several different levels, such as database integration, business logic, or Graphical User Interfaces (GUI) [6]. The before mentioned work presents an approach for mapping formal ontologies to GUI. This supports device-independent GUI construction and semi-automatic GUI modeling. This issue was also been raised in other work [7], as well as [8]. User interfaces have been also adopted by means of application different recommender methods such as Demographic Filtering, Content-Based Filtering, Collaborative Filtering or Hybrid Approach [9, 15], wherein user grouping or classification different machine learning algorithms may be applied, or the recommender method hybridization may be based

UX of AI & PersonalizationResearch Papers

The Trend of Published Literature on User Experience (UX) Evaluation: A Bibliometric Analysis2020The term user experience (UX) emerged in the early 1990’s. Thenceforth, UX has become a key term for researchers to focus on aspects that go beyond usability and particularly in the field of Human Computer Interaction (HCI). The aim of this study is to analyse the bibliometric aspect of UX evaluation literature from Scopus database whereby 644 papers were extracted. The study utilised publishing or perishing software to collect the data, while VOSviewer was used to visualise the data. Data analysis was also carried out using SPSS and Microsoft Excel. The publication of articles between 2018 and 2019 increased to 117 articles in 2019 and this is the highest publication to date. Most of the publications are from journals and conferences, mainly in English. Based on the analysis of the co-occurrence map of all keywords in the articles published, the keywords frequently used by the authors are user experience (416) and user experience evaluation (155). Most of the research related to UX evaluation was conducted in United States; and the researchers prefer multi-authored publications. The co-authorship map of the journal’s authors showed that V. Roto is one of the dominant co-authorships. Other than that, Arnold P. O. S. Vermeeren is also the most cited author of UX evaluation in Scopus database. This study presents the history of scientific literature in user experience evaluation and will provide guidance for future research.

UX of AI & PersonalizationResearch Papers

Exploring a Design Space of Graphical Adaptive Menus: Normal vs. Small ScreensJEAN VANDERDONCKT · 2019Graphical Adaptive Menus are Graphical User Interface menus whose predicted items of immediate use can be automatically rendered in a prediction window. Rendering this prediction window is a key question for adaptivity to enable the end-user to efficiently differentiate predicted items from normal ones and to consequently select appropriate items. Adaptivity for graphical menus has been investigated more for normal screens, such as desktops, than for small screens, such as smartphones, where real estate imposes severe rendering constraints. To address this question, this article defines and explores a design space where graphical adaptive menus are structured based on Bertin's eight visual variables (i.e., position, size, shape, value, color, orientation, texture, and motion) and their combination by comparing their rendering for small screens with respect to normal screens. Based on this design space, previously introduced graphical adaptive menus are revisited in terms of four stability properties (i.e., spatial, physical, format, and temporal), and new menu designs are introduced and discussed for both normal and small screens. The resulting set of graphical adaptive menu has been subject to a preference analysis from which a particular design emerged: the cloud menu, where predicted items are arranged in an adaptive tag cloud. We investigate empirically the effect of the cloud menu on the item selection time and the error rate with respect to a static menu and an adaptive linear menu. This article then suggests a set of usability guidelines for designers and practitioners to design graphical adaptive menus in general and cloud menus in particular.

UX of AI & PersonalizationResearch Papers

Integrated model-driven development of self-adaptive user interfacesEnes Yigitbas · 2019Modern user interfaces (UIs) are increasingly expected to be plastic, in the sense that they retain a constant level of usability, even when subjected to context changes at runtime. Self-adaptive user interfaces (SAUIs) have been promoted as a solution for context variability due to their ability to automatically adapt to the context-of-use at runtime. The development of SAUIs is a challenging and complex task as additional aspects like context management and UI adaptation have to be covered. In classical model-driven UI development approaches, these aspects are not fully integrated and hence introduce additional complexity as they represent crosscutting concerns. In this paper, we present an integrated model-driven development approach where a classical model-driven development of UIs is coupled with a model-driven development of context-of-use and UI adaptation rules. We base our approach on the core UI modeling language IFML and introduce new modeling languages for context- of-use (ContextML) and UI adaptation rules (AdaptML). The generated UI code, based on the IFML model, is coupled with the context and adaptation services, generated from the ContextML and AdaptML model, respectively. The integration of the generated artifacts, namely UI code, context, and adaptation services in an overall rule-based execution environment, enables runtime UI adaptation. The benefit of our approach is demonstrated by two case studies, showing the development of SAUIs for different application scenarios and a usability study which has been conducted to analyze end-user satisfaction of SAUIs.

UX of AI & PersonalizationResearch Papers

Individualising Graphical Layouts with Predictive Visual Search ModelsKRIS LUYTEN · 2019In domains where users are exposed to large variations in visuo-spatial features among designs, they often spend excess time searching for common elements (features) on an interface. This article contributes individualised predictive models of visual search, and a computational approach to restructure graphical layouts for an individual user such that features on a new, unvisited interface can be found quicker. It explores four technical principles inspired by the human visual system (HVS) to predict expected positions of features and create individualised layout templates: (I) the interface with highest frequency is chosen as the template; (II) the interface with highest predicted recall probability (serial position curve) is chosen as the template; (III) the most probable locations for features across interfaces are chosen (visual statistical learning) to generate the template; (IV) based on a generative cognitive model, the most likely visual search locations for features are chosen (visual sampling modelling) to generate the template. Given a history of previously seen interfaces, we restructure the spatial layout of a new (unseen) interface with the goal of making its features more easily findable. The four HVS principles are implemented in Familiariser, a web browser that automatically restructures webpage layouts based on the visual history of the user. Evaluation of Familiariser (using visual statistical learning) with users provides first evidence that our approach reduces visual search time by over 10%, and number of eye-gaze fixations by over 20%, during web browsing tasks.

UX of AI & PersonalizationResearch Papers

Crowdsourcing Interface Feature Design with Bayesian OptimizationUnknown · 2019Designing novel interfaces is challenging. Designers typi- cally rely on experience or subjective judgment in the ab- sence of analytical or objective means for selecting interface parameters. We demonstrate Bayesian optimization as an ef- ficient tool for objective interface feature refinement. Specif- ically, we show that crowdsourcing paired with Bayesian optimization can rapidly and effectively assist interface de- sign across diverse deployment environments. Experiment 1 evaluates the approach on a familiar 2D interface design problem: a map search and review use case. Adding a de- gree of complexity, Experiment 2 extends Experiment 1 by switching the deployment environment to mobile-based vir- tual reality. The approach is then demonstrated as a case study for a fundamentally new and unfamiliar interaction design problem: web-based augmented reality. Finally, we show how the model generated as an outcome of the refine- ment process can be used for user simulation and queried to deliver various design insights. CCS CONCEPTS • Human-centered computing →Systems and tools for interaction design.

UX of AI & PersonalizationResearch Papers

SAM: A Modular Framework for Self-Adapting Web MenusCamille Gobert · 2019This paper presents SAM, a modular and extensible JavaScript framework for self-adapting menus on webpages. SAM al­ lows control of two elementary aspects for adapting web menus: (1) the target policy, which assigns scores to menu items for adaptation, and (2) the adaptation style, which spec­ ifes how they are adapted on display. By decoupling them, SAM enables the exploration of diferent combinations inde­ pendently. Several policies from literature are readily imple­ mented, and paired with adaptation styles such as reordering and highlighting. The process—including user data logging— is local, ofering privacy benefts and eliminating the need for server-side modifcations. Researchers can use SAM to experiment adaptation policies and styles, and benchmark techniques in an ecological setting with real webpages. Prac­ titioners can make websites self-adapting, and end-users can dynamically personalise typically static web menus. CCS CONCEPTS • Human-centered computing → Human computer in­ teraction (HCI); User interface design;

UX of AI & PersonalizationResearch Papers

Deep Learning UI Design Patterns of Mobile AppsPhong Minh Vu, Hung Viet Pham et al. · 2018User interface (UI) is one of the most important components of a mobile app and strongly influences users’ perception of the app. However, UI design tasks are typically manual and time-consuming. This paper proposes a novel approach to (semi)-automate those tasks. Our key idea is to develop and deploy advanced deep learning models based on recurrent neural networks (RNN) and generative adversarial networks (GAN) to learn UI design patterns from mil- lions of currently available mobile apps. Once trained, those models can be used to search for UI design samples given user-provided descriptions written in natural language and generate professional- looking UI designs from simpler, less elegant design drafts.

UX of AI & PersonalizationResearch Papers

Familiarisation: Restructuring Layouts with Visual Learning ModelsKashyap Todi · 2018In domains where users are exposed to large variations in visuo-spatial features among designs, they often spend excess time searching for common elements (features) in familiar locations. This paper contributes computational approaches to restructuring layouts such that features on a new, unvisited interface can be found quicker. We explore four concepts of fa- miliarisation, inspired by the human visual system (HVS), to automatically generate a familiar design for each user. Given a history of previously visited interfaces, we restructure the spatial layout of the new (unseen) interface with the goal of making its elements more easily found. Familiariser is a browser-based implementation that automatically restruc- tures webpage layouts based on the visual history of the user. Our evaluation with users provides first evidence favouring familiarisation. Author Keywords Visual search; Graphical layouts; Computational design; Adaptive user interfaces

UX of AI & PersonalizationResearch Papers

Deep Sequential Recommendation for Personalized Adaptive User InterfacesUnknown · 2017Adaptive user-interfaces (AUIs) can enhance the usability of complex software by providing real-time contextual adapta- tion and assistance. Ideally, AUIs should be personalized and versatile, i.e., able to adapt to each user who may perform a variety of complex tasks. But this is difficult to achieve with many interaction elements when data-per-user is sparse. In this paper, we propose an architecture for personalized AUIs that leverages upon developments in (1) deep learning, par- ticularly gated recurrent units, to efficiently learn user inter- action patterns, (2) collaborative filtering techniques that en- able sharing of data among users, and (3) fast approximate nearest-neighbor methods in Euclidean spaces for quick UI control and/or content recommendations. Specifically, inter- action histories are embedded in a learned space along with users and interaction elements; this allows the AUI to query and recommend likely next actions based on similar usage patterns across the user base. In a comparative evaluation on user-interface, web-browsing and e-learning datasets, the deep recurrent neural-network (DRNN) outperforms state-of- the-art tensor-factorization and metric embedding methods. Author Keywords Adaptive User Interface; Deep Learning; Personalization ACM Classification Keywords I.2.6. Artificial Intelligence: Learning; H.5.2 Information In- terfaces and Presentation(e.g. HCI): User Interfaces

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

Interface Design Optimization as a Multi-Armed Bandit ProblemJ. Derek Lomas, Jodi Forlizzi et al. · 2016“Multi-armed bandits” offer a new paradigm for the AI- assisted design of user interfaces. To help designers understand the potential, we present the results of two experimental comparisons between bandit algorithms and random assignment. Our studies are intended to show designers how bandits algorithms are able to rapidly explore an experimental design space and automatically select the optimal design configuration. Our present focus is on the optimization of a game design space. The results of our experiments show that bandits can make data-driven design more efficient and accessible to interface designers, but that human participation is essential to ensure that AI systems optimize for the right metric. Based on our results, we introduce several design lessons that help keep human design judgment in the loop. We also consider the future of human-technology teamwork in AI-assisted design and scientific inquiry. Finally, as bandits deploy fewer low- performing conditions than typical experiments, we discuss ethical implications for bandits in large-scale experiments in education. Author Keywords Educational games; optimization; data-driven design; multi- armed bandits; continuous improvement; ACM Classification Keywords H.5.m. Information interfaces and presentation (e.g., HCI): Miscellaneous.

UX of AI & PersonalizationResearch Papers

Predictability and Accuracy in Adaptive User InterfacesKrzysztof Z. Gajos, Katherine Everitt et al. · 2008While proponents of adaptive user interfaces tout potential performance gains, critics argue that adaptation's unpredict- ability may disorient users, causing more harm than good. We present a study that examines the relative effects of predictability and accuracy on the usability of adaptive UIs. Our results show that increasing predictability and accuracy led to strongly improved satisfaction. Increasing accuracy also resulted in improved performance and higher utiliza- tion of the adaptive interface. Contrary to our expectations, improvement in accuracy had a stronger effect on perform- ance, utilization and some satisfaction ratings than the im- provement in predictability. AUTHOR KEYWORDS Adaptive interfaces, predictability, accuracy, user study ACM Classification Keywords H5.2 Information Interfaces and Presentation: User Inter- faces – Interactions Styles, Evaluation/Methodology

UX of AI & PersonalizationResearch Papers

User Interface Design with Combinatorial Optimization1977ptimization methods in user interface (UI) design is a long-standing topic in human-computer interaction (HCI) research. Currently, user-centered design is largely focused on human creativity, sensemaking, empathy, and creation of meaning, but optimization methods have been explored as supplemental ways to help speed up the design cycle and improve design quality (for more information about computational UI design, see the sidebar). Unlike any other design method in HCI, optimization methods offer a greater-than-zero chance of finding an optimal design, and some exact methods even offer mathematical guarantees for solutions. More importantly, if the design task and its

UX of AI & PersonalizationResearch Papers

Emotional Design on User Experience-based Development System2018In UX, user as a center of the process has crucial role in determining the quality of a system. System has broad definition. In this study, system definition restricted to the outcome of information technology (IT) product. So far, there are so many approaches which are involving user as a center of process. Involving user in the UX process is not limited as evaluator in the measurement. Moreover, user research should be conducted deeply aims to excavate all about user deeper. Functionality and usability are two measurements methods which ensure user can experience using system intuitively. In order to excavate intuitive experience, it is required to figure out user deeper. Furthermore, physiological and psychological aspects are mandatory to be discussed. Psychology and physiology theories are related to the processes of designing system. This study aims to conduct argumentative literature review about the emotional design on the user experience (UX). Papers reviewed had been selected and limited related to the problems. Result of this study is proposed methodology. This methodology is proposed aims to gain deeper perspective on user research. Hopefully, this methodology can be considered to strengthen ideas about user mental model theories into system development.

Emotional DesignResearch Papers

The focus of UX research is to design and develop systems that must support usability and user’s affective needs and goals[10-11]. UX research has gained more interest due to limitations of the conventional usability models [4]. UX studies do not only focus on task related aspects but also on affective qualities, sensation, meaning and value of interactive systems, products and services [12-14]. In order to better understand the concept of UX, many frameworks and models are proposed e.g. [11, 14, 15] that include various integrated UX constructs and measures. These frameworks are presented from different perspectives that include interaction-centered, user-centered and system-centered [16, 17]. It is also focused to understand and document different types of experience created while interacting with systems. Experience is described as a constant stream of “self-talk” that occurs while interaction is carried out with products e.g. using instant messaging systems. An experience is described as something that can be articulated e.g. watching a movie and sitting on free fall ride. Co-experience is described as the motions and meanings created together while interacting with products e.g. playing mobile messaging games with friends. UX can be created either positive or negative depending on systems qualities perceived by users [18-20]. It introduces a valid point of interest to research that how positive UX of interactive systems can be created, measured and modeled [19]. Thus, UX is being studied extensively in HCI field to design systems to be more useful, pleasant and attractive [14, 18]. Despite the availability of different frameworks and models, there is still no consensus on the definition of UX [3, 10, 21]. It is argued that UX encompasses various integrated aspects and shares diverse views. The wider scope and incoherent views on UX make it more complex [4, 10]. It presents many challenges such as selecting and validating the core constructs, factors and relevant2006In recent years, the notion of User Experience (UX) has gained a greater attention among HCI researchers in academia and industry. Due to its importance, several frameworks and models have been proposed to design and assess UX of interactive systems. These models guide to improve the design and help to determine the quality of interactive systems, products and services. UX is highly subjective, dynamic, and context dependent; it evolves during the interaction with the system. Different factors collectively influence UX and present a challenging task to define, model, measure and validate it. The less attention is paid to understand and underline these factors; this paper is an attempt to understand and underline the core UX factors based on literature review. These factors make UX more complex, diverse and vague in nature. It is recommended to incorporate the management aspect in UX process that may help to overwhelming the issues of complexity, diversity and vagueness.

Emotional DesignResearch Papers

Human factors for pleasure in product usePatrick W. Jordan · 1998The importance of user centred design is increasingly being recognised. Perhaps the most significant evidence for thJs is the growing number of human factors professionals employed both in academia and in industry. Further evidence includes the burgeoning literature addressing issues relating to user centred design, th.e number of conferences concerned with human factors issues and the use of slogans such as 'ergonomically designed' in product advertisements.

Emotional DesignResearch Papers

Design Research on Maker Office Space Based on User ExperienceJiaqi Chi, Jinqi Xu · 2022The maker office space is a new type of office space that has gradually come into everyone’s view since 2014 when the country put forward the concept of “Mass Entrepreneurship and Innovation”. With the continuous development of the country, the era of experience economy comes, people no longer simply need the basic functions of space, but pay more attention to the experience. Therefore, this paper integrates user experience into the design of maker office space to find the demand of users. We analyze the design points of user experience in office space from various aspects, propose the design strategy of user experience in maker office space, and propose innovative thinking and inspiration for the design of maker office space in the future.

HCI & Adaptive InterfacesResearch Papers

Computer Science ReviewMaaruf Ali, Peter S. Excell · 2021A review of research on universal usability, plasticity of user interface design and facilitation of interface development with universal usability is presented. The survey was based on 165 research papers spanning over fifty-five years. The foundations of adaptive or intelligent user interfaces (AUI or IUI) are presented, three core domains being focused upon: Artificial Intelligence (AI), User Modelling (UM) and Human–Computer Interaction (HCI). For comparison of the various AUIs, a proposed taxonomy is given. One conclusion is that an efficient training vector for fast optimal convergence of the machine-learning algorithm is a necessity, but key to this is the bounding of the dataset, the goal being to achieve an accurate user preference model, which has to be built from a limited number of datasets obtained from the human interaction. More research also needs to be conducted to ascertain the usefulness and effectiveness of IUIs compared against AUIs. With the global mobility of users, interface design must take account of the abilities and cultures of users, derived from actual user behaviour and not on their feedback. A key question is whether the interface should be adaptive under system control or be made adaptable under user control. A need is identified for an ‘‘afferential component’’ that stores a priori information about the end user, an ‘‘inferential component’’ that determines to what extent the user interface actually needs to be adapted, and the ‘‘efferential component’’ that actually determines how the adaptivity is applied seamlessly to the system. Application to e-learning is a priority: the use of machine intelligence to achieve appropriate learnability, ideally enhanced by ‘‘Playful interaction’’, was found to be desirable. Universal application of adaptation lies in the future, but AUI properties cannot be ascertained while disregarding the other parameters of the system in which it will be used. A more complete understanding of the human mental model is necessary, requiring a highly multidisciplinary approach and cooperation between diverse researchers. Finally, a performance evaluation of plasticity of user interface was conducted: it is concluded that the use of dynamic techniques can enhance the user experience to a much greater extent than more basic approaches, although optimisation of usability parameter trade-offs needs further attention. It is noted that most of the work reviewed originated from a limited range of cultural pe

HCI & Adaptive InterfacesResearch Papers

Evaluating user experience and wayfinding behaviour in complex, architectural environments – towards a user-centred approach of building usabilitySaskia Kulig · 2016"Where am I? And how can I get to my destination?" This question can have both a metaphorical sense ("How do I find my way in life?!"), as well as a practical purpose ("How do I find my way around in an unknown building?"). If we are unable to identify our own position in space, and cannot formulate strategies for how we get from where we are to a desired destination, we feel disoriented; perhaps even 'lost'. This is frequently frustrating, especially when we find ourselves travelling longer distances, or when finding one's way takes longer than expected.

HCI & Adaptive InterfacesResearch Papers

INSPIRING ENGAGEMENT THROUGH THE USER EXPERIENCE:Lisa Joy Gumerman · 2014INSPIRING ENGAGEMENT THROUGH THE USER EXPERIENCE: A PROJECT WITH THE FORT COLLINS MUSEUM OF DISCOVERY This project focused on user experience to create a plan for a web application that would increase engagement with the audience of a local museum. With the support of the Fort Collins Museum of Discovery, the researcher performed user experience research to create an interactive feature on the museum's website that can showcase the experiences and history and science content the museum has to offer. The project was conducted using a human-centered design framework and focused on engagement and user experience. Activity theory and the user experience framework drove the method. The process started with five stakeholder interviews, then proceeded to four observation sessions, five personas, and rounds of prototyping and testing. The final deliverable to the Fort Collins Museum of Discovery was a project plan – including specific design, content and technology recommendations resulting from research and development – they can use to implement the web application on their currently existing site to increase engagement and their audience size. ii

HCI & Adaptive InterfacesResearch Papers