Walking is an essential aspect of daily life, while walking with companions offers numerous benefits. Recently developed mobile robots, through their ability to navigate challenging terrains, open new possibilities for outdoor walking companionship. Yet, little is known about how such companions shape the human walking experience. In this study, nine participants walked outdoors with a robot and later reflected on their walking experience in semi-structured interviews. Thematic analysis showed that the robot influenced how participants related to it, how they managed proximity, and how their attention, control, and social presence were affected. Building on these insights, we identify five key dimensions of human–robot walking: attunement, awareness mediation, proxemics, social perception, and playful curiosity. These dimensions capture how walking with robots transforms this ordinary activity into a co-experienced practice and additionally offer concrete design implications for designing and creating more meaningful, comfortable, and socially attuned human-robot walking interactions.

Autonomous systems such as robots are permeating our daily lives increasingly every day, which are now adorned with social elements, bringing them closer to synthetic companions. While used in fields like well-being, education, guidance, and entertainment, companion robots also hold great potential for outdoor uses, particularly accompanying people in the wild with numerous potential benefits. However, current studies lack a comprehensive understanding of the possible uses, functions, and behavior of companion robots outdoors. To explore this area, we have run a co-design study consisting of 5 design workshops with 30 participants, including interaction designers, product development experts, engineers, robotics experts, and frequent forest goers. The study resulted in nine valuable design themes, transferred into five design concepts, which were then interpreted into a comprehensive design space that can be leveraged by designers and researchers in creating companion robots for the wild.

Walking is an integral part of daily human lives which also has a great influence on happiness and wellbeing. Walking with a dog companion is one of the most popular forms of recreational walking that has similar benefits. The recent development of mobile zoomorphic robots, especially robot dogs has opened up new opportunities in the landscape of walking with companions. This led us to investigate how such robots can accompany humans in walking, and how the mobility and behavior of robots in a daily-life walking scenario affect humans’ walking experience. We interviewed nine participants who took a 15–20-minute walk with a companion robot around a university campus, to understand how diverse walking behaviors of a companion robot influence their perceived experiences. We have generated four key themes through thematic analysis. They imply that affective relationalities between humans and robots can build intimacy and empathy, whereas personal space and physical proximity need to be thought carefully to ensure interaction comfort and spontaneity. Additionally, the robot influenced people’s self-reflection and social values refraining them from enjoying an unknown experience, while ambiguity in communication led to less confidence and trust.

This study investigated the predictive ability of ten different machine learning (ML) models for diabetes using a dataset that was not evenly distributed. Additionally, the study evaluated the effectiveness of two oversampling and undersampling methods, namely the Synthetic Minority Oversampling Technique (SMOTE) and the Near-Miss algorithm. Explainable Artificial Intelligence (XAI) techniques were employed to enhance the interpretability of the model's predictions. The results indicate that the extreme gradient boosting (XGB) model combined with SMOTE oversampling technique exhibited the highest accuracy and an F1-score of 99% and 1.00 respectively. Furthermore, the utilization of XAI methods increased the dependability of the model's decision-making process, rendering it more appropriate for clinical use. These results imply that integrating XAI with ML and oversampling techniques can enhance the early detection and management of diabetes, leading to better diagnosis and intervention.

Second language learning has become very important because of globalization and as a result, many online language learning platforms have gained popularity. Despite their popularity and convenience, they still lack the human factor and meaningful interaction. Robot-assisted language learning (RALL) is a concept where social robots are employed to assist in language learning, adding meaningful and human-like interactions to the process. In the case of online learning platforms, a similar approach can be taken using virtual robots. Virtual robots are similar to social robots as they can have a visual appearance, communication capabilities as well as human-like features. This research aims to understand the potential users’, i.e., university students’ perceptions, and expectations of a virtual robot as a language learning companion. We are focusing on three major aspects of its design: appearance, interaction and rewarding behavior. This is a qualitative and explorative study, which employs a human-centered design (HCD) approach by conducting a co-design workshop with five groups of university-level language students (n = 25) and a theme interview with seven design students. This article presents the first phase of the HCD process. The participants were asked questions about the appearance, behavior, movements, motivational factors, sound and rewarding features of the potential virtual language companion robot. The findings show that the idea of having an interactive virtual robot to assist online language learning was accepted and appreciated by all the participants but their expectations about the robot’s design varied. The potential users preferred a robot-like appearance rather than a human-like one for the virtual language learning companion, however, different robot-like appearances were mentioned in terms of their body parts, hands, head, shapes etc. Human-like gestures and movements were appreciated by the participants. Finally, seven design implications were formulated to support the further design of a virtual robot that can act as a virtual language learning companion as part of an online learning platform for university students.