Abstract
Histology is a preclinical subject transversal in medical, dental, and veterinary curricula. Classical teaching approaches in histology are often undermined by lower motivation and engagement of students, which may be addressed by innovative learning environments. Herein, we developed a serious game approach and compared it with a classical teaching style. The students' feedback was evaluated by questionnaires, and their performance on quizzes and exam's scores were assessed. The serious game (Histopoly) consisted of a game-based web application for the teacher/game master, a digital gaming application used by the students as a controller, and a projected digital board game. The board featured rows for the four fundamental tissues (epithelial, connective, muscular, and nervous) paired with question tiles and additional tiles with more demanding activities (e.g., drawing, presenting slides, and making a syllabus). Participants included all veterinary students enrolled in the first year. Paired laboratory sessions were split with four sections (n = 94 students) playing Histopoly at the end of all sessions and two sections (n = 28 students) completing small evaluations every three weeks at the beginning of sessions. According to the questionnaires, students that played the serious game were more motivated, engaged, and more interconnected with classmates. The activity was considered fun, and students enjoyed the classes more. No differences in the final examination scores were found, but the percentage of correct answers provided throughout the serious game was significantly higher. Overall, these findings argue for the inclusion of serious games in modern histology teaching to promote student engagement in learning.

Abstract
The potential of Augmented Reality (AR) has been harnessed to create immersive game settings, present layers of relevant information in museums, streamline procedures in healthcare and industry, and captivate consumers through innovative marketing strategies. Certain artifacts lend themselves well to representation in AR, especially those requiring a seamless fusion of the information layer with physical space. This integration underscores the suitability of information design artifacts for AR implementation. This study aims to delineate the distinctive attributes of AR in remediating information design, effectively catering to the user's informational needs. To this end, we analyzed the Google Translate app, examining it through the analytical lens of body schema and haptic engagement. The findings reveal that AR manifests as a performative, personalized, crafted image that fosters involvement through agency. The performative nature of the image directs attention, while individual images collectively form a collection. It is recommended that AR design be centered around achieving harmony among body, media, and space.

Abstract
Driving simulators are essential tools for training, education, research, and scientific experimentation. However, the diversity and quality of virtual environments in simulations is limited by the specialized human resources availability for authoring the content, leading to repetitive scenarios and low complexity of real-world scenes. This work introduces a pipeline that can process text-based narratives outlining driving experiments to procedurally generate dynamic traffic simulation scenarios. The solution uses Retrieval-Augmented Generation alongside local open-source Large Language Models to analyse unstructured textual information and produce a knowledge graph that encapsulates the world scene described in the experiment. Additionally, a context-based formal grammar is generated through inverse procedural modelling, reflecting the game mechanics related to the interactions among the world entities in the virtual environment supported by CARLA driving simulator. The proposed pipeline aims to simplify the generation of virtual environments for traffic simulation based on descriptions from scientific experiment, even for users without expertise in computer graphics. © 2025 by SCITEPRESS–Science and Technology Publications, Lda.

Abstract
The virtual reconstitution of Castelo de Vide, Portugal, within the FRONTOWNS project, highlights the challenges and successes of multidisciplinary collaboration in heritage preservation through 3D modeling. The goal was to reconstruct the town's urban evolution, focusing on its role as a border settlement from the 13th to 16th centuries. The project combined archaeological evidence, historical sources, and digital technologies like photogrammetry and 3D scanning. Co -creation workshops aligned diverse knowledge, leading to creative solutions that balanced historical accuracy and technical feasibility. Despite budget constraints, it produced a high-quality digital reconstitution with insights for future virtual heritage projects.

Abstract
This paper aims to evaluate how different combinations of multisensory stimuli affect the vividness of users' mental imagery in the context of virtual tourism. To this end, a between-subjects experimental study was conducted with 94 participants, who were allocated to either a positive or a negative immersive virtual environment. The positive environment contained only pleasant multisensory stimuli, whereas the negative contained only unpleasant stimuli. For each of the virtual experiences, a multisensory treasure hunt was developed, where each object found corresponded to a planned combination of stimuli (positive or negative, accordingly). The results showed that positive stimuli involving a higher number of sensory modalities resulted in higher reported vividness. In contrast, when the same multisensory modalities were delivered with negative stimuli, vividness levels decreased - an effect we attribute to potential cognitive overload. Nevertheless, some reduced negative combinations (audiovisual with smell and audiovisual with haptics) remained effective, indicating that olfactory and haptic cues play an important role in shaping users' vividness of mental imagery, even in negative contexts.

Abstract
Creating content for digital video game is an expensive segment of the development process, and many techniques have been explored to automate it. Much of the generated content is graphical, ranging from textures and sprites to typographical elements and user interfaces. Numerous techniques have been explored to automate the generation of these assets, with recent advancements incorporating artificial intelligence methodologies, such as deep learning generative models. This study comprehensively surveys the literature from 2016 onward, focusing on using machine learning to generate image-based assets for video game development, reviewing the deep learning approaches employed, and analyzing the specific challenges found. Specifically, the deep learning approaches employed, the problems addressed within the domain, and the metrics used for evaluating the results. The study demonstrates a knowledge gap in generative methods for some types of video game assets. In addition, applicability and effectiveness of the most used evaluation metrics in the literature are studied. As future research prospects, with the increase in popularity of generative AI, the adoption of such techniques will be seen in automation processes.