As public libraries adopt social robots to enhance visitor interactions, understanding factors driving user acceptance is crucial. Based on the Technology Acceptance Model (TAM), present study investigates how perceived usefulness (PU), perceived ease of use (PEOU), social influences, and perceived enjoyment shape the acceptance of social robots in public libraries. 65 participants interacted in a field study held in two public libraries with the Pepper robot for book presentations. Following a qualitative approach, the influence of TAM constructs on the user experience (UX) with Pepper was explored. Results show participants valued pragmatic properties such as efficiency and task fulfillment, underscoring PU. However, transparency issues impaired usability. Social influences shaped users‘ attitudes, and perceived enjoyment emerged as an important factor, with mixed responses to the robot’s emotional engagement. Insights were used to derive 20 recommendations for improving Human-Robot Interaction (HRI) in public libraries.

The integration of AI-powered drone monitoring with thermal imaging cameras enhances wildlife protection by enabling autonomous animal detection and classification. This paper presents a system based on YOLOv8 that processes thermal data from drones, allowing single-operator usage and eliminating the need for an on-site video analyst. Through collaboration with wildlife conservation experts, we identified key requirements for real-time data processing and developed a user-centric smartphone application for instant data visualization. This study advances current methodologies by demonstrating the practical application of AI and drone technology in wildlife conservation.

The development of social robots for public spaces is challenging as they need to be accessible and socially acceptable for diverse user groups. Although current standards and guidelines contain numerous requirements, there is still a lack of comprehensive analysis and a compendium of these accessibility requirements, especially for social robots. Therefore, the aim of this paper is to derive a set of requirements and recommendations based on existing standards and guidelines. This is intended to promote the development of socially sustainable, accessible robots in public spaces and ensure that the specific needs of different user groups are taken into account. For this purpose, we conducted a systematic review of 31 International, European, and German standards and guidelines relevant to the design of socially sustainable robotics in public spaces. The review focused on aspects of accessibility, usability, design, safety requirements, and risk management in relation to robots, considering the heterogeneity of use by different target groups. In particular, we emphasized the equality requirements essential for the development of socially sustainable robots. We then categorized these requirements and recommendations according to different user needs using text mining techniques. Our analysis led to the creation of an illustrative catalog of 83 equality requirements and recommendations. These requirements and recommendations are intended to guide the design process, development, implementation, and use of sustainable and equality-oriented social robots in public spaces. This catalog provides comprehensive insights into promoting accessibility and social sustainability in the design of social robotics, addressing the challenges and requirements in developing such systems, and ensuring that these systems meet the needs of a heterogeneous user group.

Smart home systems contain plenty of features that enhance wellbeing in everyday life through artificial intelligence (AI). However, many users feel insecure because they do not understand the AI’s functionality and do not feel they are in control of it. Combining technical, psychological and philosophical views on AI, we rethink smart homes as interactive systems where users can partake in an intelligent agent’s learning. Parallel to the goals of explainable AI (XAI), we explored the possibility of user involvement in supervised learning of the smart home to have a first approach to improve acceptance, support subjective understanding and increase perceived control. In this work, we conducted two studies: In an online pre-study, we asked participants about their attitude towards teaching AI via a questionnaire. In the main study, we performed a Wizard of Oz laboratory experiment with human participants, where participants spent time in a prototypical smart home and taught activity recognition to the intelligent agent through supervised learning based on the user’s behaviour. We found that involvement in the AI’s learning phase enhanced the users’ feeling of control, perceived understanding and perceived usefulness of AI in general. The participants reported positive attitudes towards training a smart home AI and found the process understandable and controllable. We suggest that involving the user in the learning phase could lead to better personalisation and increased understanding and control by users of intelligent agents for smart home automation.