As Artificial Intelligence (AI) becomes increasingly embedded in high-stakes domains such as healthcare, law, and public administration, automation bias (AB)—the tendency to over-rely on automated recommendations—has emerged as a critical challenge in human–AI collaboration. While previous reviews have examined AB in traditional computer-assisted decision-making, research on its implications in modern AI-driven work environments remains limited. To address this gap, this research systematically investigates how AB manifests in these settings and the cognitive mechanisms that influence it. Following PRISMA 2020 guidelines, we reviewed 35 peer-reviewed studies from SCOPUS, ScienceDirect, PubMed, and Google Scholar. The included literature, published between January 2015 and April 2025, spans fields such as cognitive psychology, human factors engineering, human–computer interaction, and neuroscience, providing an interdisciplinary foundation for our analysis. Traditional perspectives attribute AB to over-trust in automation or attentional constraints, resulting in users perceiving AI-generated outputs as reliable. However, our review presents a more nuanced view. While confirming some prior findings, it also sheds light on additional interacting factors such as, AI literacy, level of professional expertise, cognitive profile, developmental trust dynamics, task verification demands, and explanation complexity. Notably, although Explainable AI (XAI) and transparency mechanisms are designed to mitigate AB, overly technical, cognitively demanding, or even simplistic explanations may inadvertently reinforce misplaced trust, especially among less experienced professionals with low AI literacy. Taken together, these findings suggest that although explanations may increase perceived system acceptability, they are often insufficient to improve decision accuracy or mitigate AB. Instead, user engagement emerges as the most feasible and impactful point of intervention. As increased verification effort has been shown to reduce complacency toward AI mis-recommendations, we propose explanation design strategies that actively promote critical engagement and independent verification. These conclusions offer both theoretical and practical contributions to bias-aware AI development, underscoring that explanation usability is best supported by features such as understandability and adaptiveness.

Research in the area of robotics has made available numerous possibilities for further innovation in the education of children, especially in the rehabilitation of those with learning difficulties and/or intellectual disabilities. Despite the scientific evidence, there is still a strong scepticism against the use of robots in the fields of education and care of people. Here we present a study on the acceptance of robots by experienced practitioners (specialized in the treatment of intellectual disabilities) and university students in psychology and education sciences (as future professionals). The aim is to examine the factors, through the Unified Theory of Acceptance and Use of Technology (UTAUT) model, that may influence the decision to use a robot as an instrument in the practice. The overall results confirm the applicability of the model in the context of education and care of children, and suggest a positive attitude towards the use of the robot. The comparison highlights some scepticism among the practitioners, who perceive the robot as an expensive and limited tool, while students show a positive perception and a significantly higher willingness to use the robot. From this experience, we formulate the hypothesis that robots may be accepted if more integrated with standard rehabilitation protocols in a way that benefits can outweigh the costs.

Recent studies suggest that some children with autism prefer robots as tutors for improving their social interaction and communication abilities which are impaired due to their disorder. Indeed, research has focused on developing a very promising form of intervention named Robot-Assisted Therapy. This area of intervention poses many challenges, including the necessary flexibility and adaptability to real unconstrained therapeutic settings, which are different from the constrained lab settings where most of the technology is typically tested. Among the most common impairments of children with autism and intellectual disability is social attention, which includes difficulties in establishing the correct visual focus of attention. This article presents an investigation on the use of novel deep learning neural network architectures for automatically estimating if the child is focusing their visual attention on the robot during a therapy session, which is an indicator of their engagement. To study the application, the authors gathered data from a clinical experiment in an unconstrained setting, which provided low-resolution videos recorded by the robot camera during the child–robot interaction. Two deep learning approaches are implemented in several variants and compared with a standard algorithm for face detection to verify the feasibility of estimating the status of the child directly from the robot sensors without relying on bulky external settings, which can distress the child with autism. One of the proposed approaches demonstrated a very high accuracy and it can be used for off-line continuous assessment during the therapy or for autonomously adapting the intervention in future robots with better computational capabilities.

Robots are versatile devices that are promising tools for supporting teaching and learning in the classroom or at home. In fact, robots can be engaging and motivating, especially for young children. This paper presents an experimental study with 81 kindergarten children on memorizations of two tales narrated by a humanoid robot. The variables of the study are the content of the tales (knowledge or emotional) and the different social behaviour of the narrators: static human, static robot, expressive human, and expressive robot. Results suggest a positive effect of the expressive behaviour in robot storytelling, whose effectiveness is comparable to a human with the same behaviour and better when compared with a static inexpressive human. Higher efficacy is achieved by the robot in the tale with knowledge content, while the limited capability to express emotions made the robot less effective in the tale with emotional content.

(1) Background: in the last decade, various investigations into the field of robotics have created several opportunities for further innovation to be possible in student education. However, despite scientific evidence, there is still strong scepticism surrounding the use of robots in some social fields, such as personal care and education. (2) Methods: in this research, we present a new tool named the HANCON model, which was developed merging and extending the constructs of two solid and proven models—the Unified Theory of Acceptance and Use of Technology (UTAUT) model used to examine the factors that may influence the decision to use a telepresence robot as an instrument in educational practice, and the Post Acceptance Model used to evaluate acceptability after the actual use of a telepresence robot. The new tool is implemented and used to study the acceptance of a double telepresence robot by 112 pre-service teachers in an educational setting. (3) Results: the analysis of the experimental results predicts and demonstrate a positive attitude towards the use of telepresence robot in a school setting and confirm the applicability of the model in an educational context. (4) Conclusions: the constructs of the HANCON model could predict and explain the acceptance of social telepresence robots in social contexts.

Research and development in socially assistive robotics have produced several novel applications in the care of senior people. However, some are still unexplored such as their use as psychometric tools allowing for a quick and dependable evaluation of human users’ intellectual capacity. To fully exploit the application of a social robot as a psychometric tool, it is necessary to account for the users’ factors that might influence the interaction with a robot and the evaluation of user cognitive performance. To this end, we invited senior participants to use a prototype of a robot-led cognitive test and analyzed the influence of personality traits and user’s empathy on the cognitive performance and technology acceptance. Results show a positive influence of a personality trait, the “openness to experience”, on the human-robot interaction, and that other factors, such as anxiety, trust, and intention to use, are influencing technology acceptance and correlate the evaluation by psychometric tests.