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Article UX Framework Including Imbalanced UX Dataset Reduction Method for Analyzing Interaction Trends of Agent SystemsAgent Systems. Sensors · 2023The performance of game AI can significantly impact the purchase decisions of users. User experience (UX) technology can evaluate user satisfaction with game AI by analyzing user interaction input through a user interface (UI). Although traditional UX-based game agent systems use a UX evaluation to identify the common interaction trends of multiple users, there is a limit to evaluating UX data, i.e., creating a UX evaluation and identifying the interaction trend for each individual user. The loss of UX data features for each user should be minimized and reflected to provide a personalized game agent system for each user. This paper proposes a UX framework for game agent systems in which a UX data reduction method is applied to improve the interaction for each user. The proposed UX framework maintains non-trend data features in the UX dataset where overfitting occurs to provide a personalized game agent system for each user, achieved by minimizing the loss of UX data features for each user. The proposed UX framework is applied to a game called “Freestyle” to verify its performance. By using the proposed UX framework, the imbalanced UX dataset of the Freestyle game minimizes overfitting and becomes a UX dataset that reflects the interaction trend of each user. The UX dataset generated from the proposed UX framework is used to provide customized game agents of each user to enhanced interaction. Furthermore, the proposed UX framework is expected to contribute to the research on UX-based personalized services.

UX of AI & PersonalizationResearch Papers

Algorithms for the Development of Adaptive Web Interfaces: A Systematic Literature ReviewMichael Rodrigues da Silva · 2022The User Interface (UI) is a fundamental part of web interfaces, being considered a fundamental aspect of user satisfaction when using a web application. The UI that offers a good User Experience (UX) is the one that makes the interaction as simple and efficient as possible for the user to achieve the previously desired goals. In this way, web interfaces that have adaptive interfaces tend to offer a better UX, as they seek to adapt to the needs and desires of users. Therefore, this paper carries out a Systematic Literature Review (SLR) to identify the most used algorithms for the development of Adaptive Web Interfaces (AWI). The results indicate that the use of Machine Learning (ML) techniques, both to improve the performance of some task and to induce results, are the most popular ways to develop AWI. During the execution of SLR, 294 articles were analyzed using automated and manual searches, and inclusion, exclusion and quality criteria were applied to them. The research findings allow us to state that the topic is quite relevant to the academic community, with an average publication of 20 papers each year.1

UX of AI & PersonalizationResearch Papers

Customer Experiences in the Age of Artificial IntelligenceNisreen Ameen, Ali Tarhini et al. · 2020Artificial intelligence (AI) is revolutionising the way customers interact with brands. There is a lack of empirical research into AI-enabled customer experiences. Hence, this study aims to analyse how the integration of AI in shopping can lead to an improved AI-enabled customer experience. We propose a theoretical model drawing on the trust-commitment theory and service quality model. An online survey was distributed to customers who have used an AI- enabled service offered by a beauty brand. A total of 434 responses were analysed using partial least squares-structural equation modelling. The findings indicate the significant role of trust and perceived sacrifice as factors mediating the effects of perceived convenience, personalisation and AI-enabled service quality. The findings also reveal the significant effect of relationship commitment on AI-enabled customer experience. This study contributes to the existing literature by revealing the mediating effects of trust and perceived sacrifice and the direct effect of relationship commitment on AI-enabled customer experience. In addition, the study has practical implications for retailers deploying AI in services offered to their customers.

UX of AI & PersonalizationResearch Papers

Context- and Data-driven Satisfaction Analysis of User Interface Adaptations Based on Instant User FeedbackUsability testing · 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 (platform, user, and environment) changes at runtime. Adaptive UIs have been promoted as a solution for context variability due to their ability to automatically adapt to the context-of-use at runtime. However, evaluating end-user satisfaction of adaptive UIs is a challenging task, because the UI and the context-of-use are both constantly changing. Thus, an acceptance analysis of UI adaptation features should consider the context-of-use when adaptations are triggered. Classical usability evaluation methods like usability tests mostly focus on a posteriori analysis techniques and do not fully exploit the potential of collecting implicit and explicit user feedback at runtime. To address this challenge, we present an on-the-fly usability testing solution that combines continuous context monitoring together with collection of instant user feedback to assess end-user satisfaction of UI adaptation features. The solution was applied to a mobile Android mail application, which served as basis for a usability study with 23 participants. A data-driven end-user satisfaction analysis based on the collected context information and user feedback was conducted. The main results show that most of the triggered UI adaptation features were positively rated.

UX of AI & PersonalizationResearch Papers

Model-based adaptive user interface based on context and user experience evaluationJamil Hussain, Anees Ul Hassan, Hafiz Syed Muhammad Bilal, Rahman Ali, Muhammad Afzal, Shujaat Hussain, Jaehun Bang,Oresti Banos, Sungyoung Lee · 2016Personalized services have greater impact on user experience to effect the level of user satisfaction. Many approaches provide personalized services in the form of an adaptive user interface. The focus of these approaches is lim- ited to specific domains rather than a generalized approach applicable to every domain. In this paper, we proposed a domain and device-independent model-based adaptive user interfacing methodology. Unlike state-of-the-art approaches, the proposed methodology is dependent on the evaluation of user context and user experience (UX). The proposed methodologyisimplementedasanadaptiveUI/UXauthoring (A-UI/UX-A) tool; a system capable of adapting user inter- face based on the utilization of contextual factors, such as user disabilities, environmental factors (e.g. light level, noise level, and location) and device use, at runtime using the adap- tation rules devised for rendering the adapted interface. To validate effectiveness of the proposed A-UI/UX-A tool and methodology, user-centric and statistical evaluation methods are used. The results show that the proposed methodology B Sungyoung Lee [sylee@oslab.khu.ac.kr](mailto:sylee@oslab.khu.ac.kr) Jamil Hussain [jamil@oslab.khu.ac.kr](mailto:jamil@oslab.khu.ac.kr) 1 Department of Computer Science & Engineering, Kyung Hee University (Global Campus), 1732 Deokyoungdae-ro, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Republic of Korea 2 Quaid-e-Azam College of Commerce, University of Peshawar, Peshawar, Pakistan 3 College of Electronics and Information Engineering, Sejong University, Seoul, South Korea 4 Telemedicine Group, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Postbox 217, 7500 AE Enschede, The Netherlands outperforms the existing approaches in adapting user inter- faces by utilizing the users context and experience.

UX of AI & PersonalizationResearch Papers

Library and Information Science ResearchMaja Kuhar, Tanja Merˇcun · 2022In the field of digital libraries, research on user experience is sparse, especially in terms of emotions, impressions, and stimulation evoked by interaction with the digital library. Given the many different methods and tools available for assessing user experience, two questionnaires and eye-tracking were explored to see how each can contribute to understanding user experience in digital libraries. An eye-tracking study with 30 participants was carried out using two digital libraries. Analysis of the questionnaires revealed that overall, one digital library was experienced more positively, with the difference most evident in the hedonic quality. The eye-tracking data revealed different gaze patterns in the two digital libraries, with significant difference in measure values particularly on the search boxes. When eye-tracking was combined with the questionnaire data, several corre­ lations were found, indicating that the search box position and the intuitiveness of the homepage had an impact on the user experience.

Emotional DesignResearch Papers

Emotional Design in Multimedia Learning: How Emotional Intelligence Moderates Learning OutcomesJeya Amanth · 2016This study is designed as a preliminary study to explore the effects of emotional intelligence (EI) on achievement, perceived intrinsic motivation and perceived satisfaction when expose to an emotional designed Multimedia Learning Environment (MLE) that was designed to induce either positive, neutral or negative emotions. All three designs had similar content and narration but differed in visual element such as colour, font size, font style and images. Based on the findings, it was reported that students performed better in the design used to induce negative emotion (NegD design) followed by the positive (PosD) and Neutral (NeuD). There is no significant difference in levels of emotional intelligence towards these learning outcomes; however, students with Low EI performed better overall. EI only qualified perceived satisfaction when using a MLE designed to induce emotions and it was found that students with Low EI preferred the design that induces positive emotions. In addition, High EI students favored designs with emotionality (positive or negative) compared to neutral design. Index Terms—Emotional Design, Engineering Education, Emotional Intelligence, Multimedia- Based Learning, Aesthetics.

Emotional DesignResearch Papers