Abstract
The current surge in the deployment of web applications underscores the need to consider users' individual preferences in order to enhance their experience. In response to this, an innovative approach is emerging that focuses on the detailed analysis of interaction data captured by web browsers. These data, which includes metrics such as the number of mouse clicks, keystrokes, and navigation patterns, offer insights into user behavior and preferences. By leveraging this information, developers can achieve a higher degree of personalization in web applications, particularly in the context of interactive elements such as online games. This paper presents the WebTraceSense project, which aims to pioneer this approach by developing a framework that encompasses a backend and frontend, advanced visualization modules, a DevOps cycle, and the integration of AI and statistical methods. The backend of this framework will be responsible for securely collecting, storing, and processing vast amounts of interaction data from various websites. The frontend will provide a user-friendly interface that allows developers to easily access and utilize the platform's capabilities. One of the key components of this framework is the visualization modules, which will enable developers to monitor, analyze, and interpret user interactions in real time, facilitating more informed decisions about user interface design and functionality. Furthermore, the WebTraceSense framework incorporates a DevOps cycle to ensure continuous integration and delivery, thereby promoting agile development practices and enhancing the overall efficiency of the development process. Moreover, the integration of AI methods and statistical techniques will be a cornerstone of this framework. By applying machine learning algorithms and statistical analysis, the platform will not only personalize user experiences based on historical interaction data but also infer new user behaviors and predict future preferences. In order to validate the proposed components, a case study was conducted which demonstrated the usefulness of the WebTraceSense framework in the creation of visualizations based on an existing dataset.

Abstract
Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) in adults can present challenges in learning and work environments, by impacting focus, organization, social interaction, and self-esteem. The aim of this study is the potential of Human-Computer Interaction (HCI) in the empowerment of adults with ADHD and ASD. Specific difficulties faced in educational and professional settings were found through qualitative interviews with six participants. HCI seems to offer a pathway towards a more inclusive future, as educational technology solutions built on HCI principles can create better and alternative learning environments with fewer distractions and gamification for increased engagement. Assistive technologies can address challenges related to focus and organization (like task management apps, time tracking tools). Additionally, features promoting social interaction and communication can empower individuals with ASD. Technologies arising nowadays like Augmented and Virtual Reality (AR/VR) can create interactive learning experiences. Through the use of Human-Computer Interaction principles, more inclusive learning and work environments that empower individuals with ADHD and ASD can originate, while improving engagement and efficiency for all. © 2025 Elsevier B.V., All rights reserved.

Abstract
The current surge in the development of web applications highlights the necessity of incorporating user-specific preferences into the design process. An innovative approach to improving these applications involves the analysis of interaction data recorded by browsers, such as the number of mouse clicks and keystrokes. The data thus obtained provides valuable insight into user behavior, enabling effective personalization of web applications. The WebTraceSense project proposes the development of a web platform designed to facilitate the customization of the visualization of interaction data from websites. The platform will include a dynamic visualization component, which will support the identification of user behaviors, and a DevOps cycle, which will help streamline software cycle processes. This article presents a case study for the examination of user interaction logs from a visual data analysis tool, utilizing the functionalities of the WebTraceSense platform to facilitate the identification of behavioral trace patterns. © 2025 Elsevier B.V., All rights reserved.

Abstract
This research investigates the use of artificial intelligence algorithms to identify behavioural patterns in computer use, with the aim of detecting trends that help to flag cases of depression by analysing the human-computer interaction records of these users, thereby increasing the quality of the data for early detection of these situations. Following design science methodology, a case study will be conducted using an existing mental health screening questionnaire, integrating an artificial intelligence layer to map mouse and keyboard interactions, followed by machine learning analysis of the records. The results of the machine learning assisted questionnaires will be compared with the results of the questionnaires without the mapping. If there is a significant difference, this model could be useful for making predictions about emotional states, contributing to the field of artificial intelligence and helping to prevent depression, which is the focus of the research, although the aim is to look at mental health in a global way. © 2025 Elsevier B.V., All rights reserved.

Abstract
Autism Spectrum Disorder (ASD) affects individuals in diverse ways, making personalized therapeutic approaches crucial. In this context, we propose a personalized mobile application designed for music-based therapy tailored to people with ASD. This adaptive piano app can be customized to suit the individual abilities of each user. The paper is structured as follows: The introduction provides context on autism and the importance of personalized therapy. The background section reviews related studies on music-based therapy. The methodology section introduces "Professor Piano,"our adaptive and adaptable music therapy application. The results and discussion section explores the challenges encountered during development and presents the findings from a heuristic evaluation conducted by experts. Finally, the conclusion summarizes the main insights and implications of the study. © 2025 Elsevier B.V., All rights reserved.

Abstract
The use of gamified crowdsourcing mechanisms through serious games and games with a purpose (GWAPs) has emerged as an effective motivational strategy for enhancing performance in human intelligence tasks (HITs). In this systematic literature review, we examine the underlying characteristics of competitive and cooperative player-oriented GWAPs and how they can be leveraged to optimize crowdsourcing performance in completing batches of HITs. By exploring gamified crowdsourcing elements in GWAPs, we can evaluate the impact of these two types of player behaviors (i.e., competition and cooperation) on motivation and performance. We reviewed 27 publications and grouped them into five categories: player orientation, game elements and motivation, crowd work optimization, gamified knowledge collection, and comparative studies and best practices. Our research pinpoints the significance of intuitive task instructions, alignment of game elements with player motivations, and the role of competitive and cooperative dynamics in enhancing engagement and performance.