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
Additive manufacturing (AM), broadly known as 3D printing, is transforming how products are designed, produced, and serviced in public health. Recent advances on 3D printing in healthcare have led to lighter, stronger and safer products, reduced lead times and lower costs. However, literature refers that knowledge remains one of the greatest barriers to AM's wider adoption. So, how we leverage the potential of AM to drive innovation is a mandatory topic in science/technology curriculum.Our goal was to develop and implement an educational scenario regarding 3D printing that uses project-based learning to address these topics, strengthening the capacity of students in low secondary level and their schools to promote STEM learning with a focus on public health issues. The scenario supports 9th grade science and ICT teachers in exploring 3D printings and environments using updated scientific/technical evidence.Overall, three schools took part in the study and 202 students participated in the educational scenario. The learning experience supports youths in understanding and reaching high-level comprehension on how STEM may contribute to address these issues, contributing to evidence-based personal decision-making, and public policy. We believe it is relevant to understand if students and schools, when challenged, take a role in their community preparedness for major health problems.By implementing an educational scenario with a focus on 3D printing, and thus potentiate the use of this technology, we intend to help raise awareness on the public health theme. © 2025 Elsevier B.V., All rights reserved.

Abstract

Abstract
Online reviews are a crucial asset for e-commerce platforms as they provide consumers with valuable insights into products. It is important to note that these reviews are subjective and may contain biases. Therefore, it is essential to approach them with a critical eye. Despite this, online reviews remain a valuable tool for consumers when making purchasing decisions. This study focuses on developing web-based mini-games that target cognitive biases. The games are specifically designed to enhance the perception of e-commerce online reviews. A pilot study involving 85 participants was conducted to explore the potential of integrating these cognitive bias games into web platforms. The findings indicate promising avenues for leveraging these games to enhance cognitive personalization and improve the quality of e-commerce online reviews.