ABSTRACT Introduction As aging populations grow and caregiving systems strain, social robots are increasingly proposed to assist in older‐adult care. Yet, acceptance among caregivers, particularly in underrepresented regions such as Brazil, remains poorly understood. Objectives This exploratory study investigates how Brazilian caregivers perceive the utility, advantages, disadvantages, and overall acceptance of social robots in older‐adult care, accounting for demographic background, workload conditions, and prior familiarity. Method An exploratory cross‐sectional survey was administered to 94 Brazilian caregivers (formal and informal) after exposure to a realistic video demonstration of the Robios social robot. The questionnaire assessed preferences, attitudes, and acceptance across 10 structured items and 3 multiple‐choice blocks. Exploratory statistical analyses used Spearman correlations, Mann–Whitney U tests, chi‐square tests, and the Jonckheere–Terpstra trend test, with Holm‐adjusted p values for multiple comparisons. Results Caregivers prioritised functionalities related to safety monitoring and medication reminders, while expressing concerns about maintenance, technical failures, and online security. Perceived workload, rather than objective hours or caregiving experience, was positively associated with two acceptance indicators. Prior awareness of social robots was not clearly associated with acceptance in this sample. No significant differences emerged between formal and informal caregivers. Evaluation Unlike prior studies concentrated in high‐income, more institutionalised contexts, this research provides an initial systematic analysis of caregiver acceptance in Brazil's middle‐income, familistic, and predominantly informal care ecosystem. The findings challenge assumptions about demographic predictors and emphasise situational, perceptual, and cultural drivers, highlighting a collaborative caregiver‐assistant view of social robots in this context. Conclusion In this Brazilian setting, acceptance of social robots appears to be shaped less by background variables and more by perceived usefulness and relief under demanding conditions. These exploratory results inform user‐centred design and policy strategies for deploying socially adaptive robotic systems in eldercare and motivate future confirmatory studies in diverse cultural settings.

This empirical study involved volunteers who played a game featuring NPCs specially developed for the research. The research investigated the influence and behaviour of NPC appearance on some factors regarding the players. Results suggest that NPC appearance can significantly affect the player’s emotions, immersion, perception, and decision-making. Poorly dressed NPCs negatively influenced the player’s emotional state, while well-dressed NPCs generated higher trustworthiness and positively influenced the player’s decision-making. This study may help develop more efficient strategies for creating characters that influence the player’s decision-making in video games and encourage the development of new research in the field of human-agent interaction and game development.

The behaviour of a person in a given situation can be explained understanding his personality traits. In this sense, the identification of these traits can be a great value to achieve personalised social influence. Although there are several models of persuasion, few of them take into account the person's personality traits. For this reason, this work describes a persuasion study that takes into account a person's personality. We develop a storytelling decision-making scenario, where the participant receives influencing messages to follow a pattern of behaviour determined by a persuasive agent (an autonomous social robot with assertive behaviour). From the study, we find evidence that the model used within the proposed scenario managed to make participants more engaged in the activity. We found pieces of evidence that the levels of assertiveness of a person can influence their attitude and perception by an agent. Also, we identify that persuasion strategy which uses persuasive arguments are more efficient than strategies that have no arguments. Finally, our proposed persuasion strategies have achieved a good level of successful influence.

Social robots are being used to create better educational scenarios, thereby fostering children»s learning. In the work presented here, we describe an autonomous social robot that was designed to enhance children»s handwriting skills. Exploiting the benefits of the learning-by-teaching method, the system provides a scenario in which a child acts as a teacher and corrects the handwriting difficulties of the robotic agent. To explore the children»s perception towards this social robot and the effect on their learning, we have conducted a multi-session study with children that compared two contrasting competencies in the robot: 'learning' vs 'non-learning' and presented as two conditions in the study. The results suggest that the children learned more in the learning condition compared with the non-learning condition and their learning gains seem to be affected by their perception of the robot. The results did not lead to any significant differences in the children»s perception of the robot in the first two weeks of interaction. However, by the end of the 4th week, the results changed. The children in the learning condition gave significantly higher writing ability and overall performance scores to the robot compared with the non-learning condition. In addition, the change in the robot»s learning capabilities did not show to affect their perceived intelligence, likability and friendliness towards it.

The determination of the three-dimensional (3D) structure of biological macromolecules at different configurations can be very important for understanding biological processes at the molecular level. The detection of individual particles from electron microscopy (EM) micrographs turns into a major labor-intensive bottleneck, when the number of particles needed starts to exceed a few tens of thousand molecular images. Multi-classifier systems have been widely investigated as tools for performing complex classifying tasks. In this work, we investigate the adequacy of using multi-classifier systems to detect particles on electron microscopy micrographs. In order to do so, we compare the performance of five algorithms for generating individual classifiers and three other ones for multi-classifier algorithms. Such results are also compared with others found in the literature. In terms of results, the multi-classifier systems generated show larger accuracy (correct classification) and lower false positive and negative rates.