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The user experience (UX) is an emerging field in user research and design, and the development of UX evaluation methods presents a challenge for both researchers and practitioners. Different UX evaluation methods have been developed to extract accurate UX data. Among UX evaluation methods, the mixed-method approach of triangulation has gained importance. It provides more accurate and precise information about the user while interacting with the product. However, this approach requires skilled UX researchers and developers to integrate multiple devices, synchronize them, analyze the data, and ultimately produce an informed decision. In this paper, a method and system for measuring the overall UX over time using a triangulation method are proposed. The proposed platform incorporates observational and physiological measurements in addition to traditional ones. The platform reduces the subjective bias and validates the user’s perceptions, which are measured by different sensors through objectification of the subjective nature of the user in the UX assessment. The platform additionally offers plug-and-play support for different devices and powerful analytics for obtaining insight on the UX in terms of multiple participants.

The success of a software application is related to users’ willingness to keep using it. In this sense, evaluating User eXperience (UX) became an important part of the software development process. Researchers have been carrying out studies by employing various methods to evaluate the UX of software products. Some studies reported varied and even contradictory results when applying different UX evaluation methods, making it difficult for practitioners to identify which results to rely upon. However, these works did not evaluate the developers’ perspectives and their impacts on the decision process. Moreover, such studies focused on one-shot evaluations, which cannot assess whether the methods provide the same big picture of the experience (i.e., deteriorating, improving, or stable). This paper presents a longitudinal study in which 68 students evaluated the UX of an online judge system by employing AttrakDiff, UEQ, and Sentence Completion methods at three moments along a semester. This study reveals contrasting results between the methods, which affected developers’ decisions and interpretations. With this work, we intend to draw the HCI community’s attention to the contrast between different UX evaluation methods and the impact of their outcomes in the software development process.

The Internet has become one of the most important means to obtain health and medical information. It is often the first step in checking for basic information about a disease and its treatment. The search results are often useful to general users. Various search engines such as Google, Yahoo!, Bing, and Ask.com can play an important role in obtaining medical information for both medical professionals and lay people. However, the usability and effectiveness of various search engines for medical information have not been comprehensively compared and evaluated. To compare major Internet search engines in their usability of obtaining medical and health information. We applied usability testing as a software engineering technique and a standard industry practice to compare the four major search engines (Google, Yahoo!, Bing, and Ask.com) in obtaining health and medical information. For this purpose, we searched the keyword breast cancer in Google, Yahoo!, Bing, and Ask.com and saved the results of the top 200 links from each search engine. We combined nonredundant links from the four search engines and gave them to volunteer users in an alphabetical order. The volunteer users evaluated the websites and scored each website from 0 to 10 (lowest to highest) based on the usefulness of the content relevant to breast cancer. A medical expert identified six well-known websites related to breast cancer in advance as standards. We also used five keywords associated with breast cancer defined in the latest release of Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) and analyzed their occurrence in the websites. Each search engine provided rich information related to breast cancer in the search results. All six standard websites were among the top 30 in search results of all four search engines. Google had the best search validity (in terms of whether a website could be opened), followed by Bing, Ask.com, and Yahoo!. The search results highly overlapped between the search engines, and the overlap between any two search engines was about half or more. On the other hand, each search engine emphasized various types of content differently. In terms of user satisfaction analysis, volunteer users scored Bing the highest for its usefulness, followed by Yahoo!, Google, and Ask.com. Google, Yahoo!, Bing, and Ask.com are by and large effective search engines for helping lay users get health and medical information. Nevertheless, the current ranking methods have some pitfalls and there is room for improvement to help users get more accurate and useful information. We suggest that search engine users explore multiple search engines to search different types of health information and medical knowledge for their own needs and get a professional consultation if necessary.

We introduce an approach that supports researchers and practitioners to determine the quality of first-time user experience (FTUX) and long-term user experience (LTUX), as well as to identify critical issues with these two types of UX. The product we chose to study is a mobile fitness application. Mobile apps tend to have a much shorter service life than most other products; thus, the developers/designers need to pay great attention to both first-time and long-term user experience. This study is based on a multi-method approach. We employed the AttrakDiff questionnaire to assess users’ first impressions of the app, and the UX Curve method to evaluate how users’ experience of the app has changed over time. Besides the quantitative data, which helped to determine the quality of user experience, we also collected qualitative data during two interviews with participants, and focused on the issues that predominantly deteriorated user experience. A four-coordinate plane tool was designed later in the data analysis process that combined the two kinds of user experience data at the same time, which led to a qualitative positioning of the user experience status of a certain product. The model was further successfully adopted in the identification of user experience issues of an online fitness application.

Neural machine translation is increasingly being promoted and introduced in the field of translation, but research into its applicability for post-editing by human translators and its integration within existing translation tools is limited. In this study, we compare the quality of SMT and NMT output of the commercially-available interactive and adaptive translation environment Lilt, as well as the translation process of professional translators working with both versions of the tool, their preference for SMT vs. NMT for post-editing, and their attitude towards such an interactive and adaptive translation tool compared to their usual translation environments.

In this work, the results of usability and user experience (UX) evaluation of a serious video game for the cognitive stimulation and motor rehabilitation of the upper limb of the elderly are presented. The serious game includes features that allow (1) performing cooperative therapy exercises between two patients, (2) remote session configuration therapy, and (3) monitoring/analyzing the sessions’ results by the therapist. An evaluation of the game with 16 older adults provides evidence about how the tool is perceived by participants, who embraced it as stimulating, useful, usable and even fun, and which impacts in therapy compliance and acceptability by the elderly. In addition, through an in depth analysis of the participants’ performance and observed emotions, as well as their self-report, we determined which engagement attributes are present in the game. Finally, we also found evidence that suggests that the participants’ skill level and the game difficulty level rather than just a good performance on the game are key factors that influence their enjoyment and frustration.

The integration of generative artificial intelligence (AI) in e-commerce is constantly increasing and in different forms, while transforming content creation. Its impact on user experience remains underexplored. This study examines user perceptions of AI-generated e-commerce content, focusing on functionality, aesthetics, and security. A survey was conducted where 223 participants were requested to browse through the pages of an online store developed using ChatGPT and DALL·E and evaluate it, providing feedback through a constructed questionnaire. The collected data was subjected to descriptive statistical analysis, exploratory factor analysis (EFA), and comparative statistical tests to identify key user experience dimensions and possible demographic variances in satisfaction. Factor analysis extracted two main components influencing user experience: “Service Quality and Security” and “Design and Aesthetics”. Further analysis highlighted a slight variation in user evaluations between male and female participants. Although security-related questions were addressed with caution, the rest of the findings indicate that AI-generated content was well-received and highly rated. Clearly, generative AI is a valuable tool for businesses, AI developers, and anyone seeking to optimize AI-driven processes to enhance user engagement. It can be confidently concluded that it positively contributes to the development of a functional and aesthetically appealing e-commerce platform.

Agile software development prioritizes customer satisfaction through the continuous delivery of valuable software. However, integrating user experience (UX) evaluations into agile projects remains a significant challenge. Existing proposals address specific stages that apply UX evaluation methods but do not fully consider UX artifacts or UX events for integrating user experience into agile processes. To address this gap and support teams, we propose FRAMUX-EV, a framework for evaluating UX in agile software development using Scrum. FRAMUX-EV introduces seven UX artifacts: (1) UX evaluation methods, (2) UX design system, (3) UX personas, (4) UX responsibilities and roles, (5) UX evaluation repository, (6) UX backlog, and (7) UX sprint backlog; and four UX events: (1) pre-planning UX meeting, (2) pre-review UX meeting, (3) weekly UX meeting, and (4) weekly user meeting. The first version of the framework was developed using a seven-step methodology with a qualitative approach. A survey of 34 practitioners validated the usefulness and ease of integration of FRAMUX-EV components, yielding positive results. These findings suggest the potential of FRAMUX-EV as an interesting proposal for integrating UX into agile software development.

Many gesture recognition systems with innovative interfaces have emerged for smart home control. However, these systems tend to be energy-intensive, bulky, and expensive. There is also a lack of real-time demonstrations of gesture recognition and subsequent evaluation of the user experience. Photovoltaic light sensors are self-powered, battery-free, flexible, portable, and easily deployable on various surfaces throughout the home. They enable natural, intuitive, hover-based interaction, which could create a positive user experience. In this paper, we present the development and evaluation of a real-time, hover gesture recognition system that can control multiple smart home devices via a self-powered photovoltaic interface. Five popular supervised machine learning algorithms were evaluated using gesture data from 48 participants. The random forest classifier achieved high accuracies. However, a one-size-fits-all model performed poorly in real-time testing. User-specific random forest models performed well with 10 participants, showing no significant difference in offline and real-time performance and under normal indoor lighting conditions. This paper demonstrates the technical feasibility of using photovoltaic surfaces as self-powered interfaces for gestural interaction systems that are perceived to be useful and easy to use. It establishes a foundation for future work in hover-based interaction and sustainable sensing, enabling human–computer interaction researchers to explore further applications.