The world’s population is aging, and there is a global drive toward developing a new care paradigm to support the older population. Robots have been seen as a unique solution to augment care because of their potential to support everyday living activities. We conducted a mixed-method scoping review of robots for older adults to establish the state of the science for robots supporting older adults. We explored the literature available on robots for older adults and identified key concepts and trends over 12 years (2010–2022). We included 205 studies following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The results revealed a growth in the range of studies evaluating robots for older adults. Key findings include characteristics of older adults involved in the research, the types of robots used, the tasks focused on, and the contexts for robot interaction and evaluation with older adults. This review provides valuable analysis and insights, offering research evidence for researchers, robot companies, and home care support personnel seeking to identify the potential of robots and advance design and application of robots to support older adults.

OBJECTIVES: The number of ethical frameworks designed to guide artificial intelligence (AI) use has grown substantially over the past decade, yet their real-world effect remains unclear. We aimed to synthesize existing evidence to analyze the practical impact of AI ethics frameworks (AIEFs) operationalized in healthcare. MATERIALS AND METHODS: We conducted a scoping review across 4 academic databases (Ovid MEDLINE, Ovid Embase, Scopus, and Web of Science), Google, and Google Scholar from January 2014 to January 2025. Eligible studies reported primary research on the qualitative or quantitative impacts of AIEFs implemented in healthcare. Data synthesis was conducted via narrative review. RESULTS: Of 1807 records identified, 16 studies met inclusion criteria. These comprised 5 preliminary initiatives testing guidelines in practice, 5 case studies, 5 implementation studies, and a comparative case study. AIEFs were implemented: (1) to develop new AI governance structures and guidelines, (2) as ethical review assessment systems for adopting clinical AI technologies, and (3) as ethical "audit" tools for identifying ethical risks. Impact was reported through qualitative improvements to process measures such as improved trust in AI. No studies demonstrated a direct link between AIEFs and health-related outcome measures such as patient safety. DISCUSSION: AIEFs led to changes in organizational or clinical processes, including increased compliance with ethical standards. When embedded in governance, AIEFs improved oversight and evaluation, but audits were constrained by their reliance on organizational cooperation. CONCLUSION: Despite the proliferation of AIEFs over the past decade, their implementation in healthcare remains limited and impact on health outcomes unmeasured or underreported.

Synthetic biology (SB) applies the engineering cycle of ‘design-build-test-learn’ to create new living systems. It is a multidisciplinary practice drawing on molecular biology, genetics, engineering, computing, biochemistry among other disciplines, linked by an underlying commitment to the design approach. The foundational premise of SB is seductively appealing: work with biological organisms as if their parts are or can be standardized, in order to understand how they work and engineer them to serve human purposes. SB thus offers a vision of navigating and controlling the otherwise-chaotic complexity of living biological systems.

As the population ages, there is an increasing need for socio-emotional support for older adults. Therapeutic pets have been used to meet this need, but there are limitations in the practicality of placing pets in older adults' living environments. More recently therapeutic robots have been proposed as a solution. However, there is limited research on the efficacy of deploying therapeutic robots to provide socio-emotional support to older adults. This study investigates the potential of the Paro robot seal to support older adults' needs while avoiding some of the limitations of current pet therapy approaches. Our results will provide insight about how robots can be designed and deployed to increase therapeutic efficacy.

Robots are being designed to assist older adults in their homes. However, we lack a clear understanding of the aspects of robot appearance older adults pay attention to and consider important for accepting a robot in their homes. The goal of this study was to systematically assess older adults' reactions to a specific robot's appearance in the context of home use. Independent living older adults interacted with the PR2 and were interviewed about its appearance. In general, there was expectation for a small sized robot that fits with the home and is easy to control. There was variability for desired human-likeness, although some human-like characteristics might be acceptable to most.

Operators and users interacting with computer environments often have to deal with multiple tasks at once, responding to each in series. Diagnostic automation, that is, automation that alerts users when and where to look, has been suggested to support the unique challenges of multiple task environments: activating tasks, switching between tasks, and tasks interfering with each other. Automation is not always reliable, however. Because of the common interaction with novel systems and the importance of training, the Simultaneous Task Environment Platform program—a multiple-task environment—was developed to understand the effects of experience on interaction with these automation-supported systems, as well as what strategies were developed. It was found that participants became more efficient with experience only when they interacted with higher reliability automation. Furthermore, the strategies participants developed focused on the differences between tasks and patterns across those tasks. Automated systems training should be sure to employ these findings.

A critical construct related to human-robot interaction (HRI) is autonomy, which varies widely across robot platforms. Levels of robot autonomy (LORA), ranging from teleoperation to fully autonomous systems, influence the way in which humans and robots may interact with one another. Thus, there is a need to understand HRI by identifying variables that influence - and are influenced by - robot autonomy. Our overarching goal is to develop a framework for levels of robot autonomy in HRI. To reach this goal, the framework draws links between HRI and human-automation interaction, a field with a long history of studying and understanding human-related variables. The construct of autonomy is reviewed and redefined within the context of HRI. Additionally, the framework proposes a process for determining a robot's autonomy level, by categorizing autonomy along a 10-point taxonomy. The framework is intended to be treated as guidelines to determine autonomy, categorize the LORA along a qualitative taxonomy, and consider which HRI variables (e.g., acceptance, situation awareness, reliability) may be influenced by the LORA.

Many older adults wish to remain in their own homes as they age [16]. However, challenges in performing home upkeep tasks threaten an older adult's ability to age in place. Even healthy independently living older adults experience challenges in maintaining their home [13]. Challenges with home tasks can be compensated through technology, such as home robots. However, for home robots to be adopted by older adult users, they must be designed to meet older adults' needs for assistance and the older users must be amenable to robot assistance for those needs. We conducted a needs assessment to (1) assess older adults' openness to assistance from robots; and (2) understand older adults' opinions about using an assistive robot to help around the home. We administered questionnaires and conducted structured group interviews with 21 independently living older adults (ages 65-93). The questionnaire data suggest that older adults prefer robot assistance for cleaning and fetching/organizing tasks overall. However their assistance preferences discriminated between tasks. The interview data provided insight as to why they hold such preferences. Older adults reported benefits of robot assistance (e.g., the robot compensating for limitations, saving them time and effort, completing undesirable tasks, and performing tasks at a high level of performance). Participants also reported concerns such as the robot damaging the environment, being unreliable at or incapable of doing a task, doing tasks the older adult would rather do, or taking up too much space/storage. These data, along with specific comments from participant interviews, provide the basis for preliminary recommendations for designing mobile manipulator robots to support aging in place.

Researchers have suggested that attention is a key moderating variable predicting performance with an input device [e.g., Greenstein & Arnaut, 1988] without directly assessing the attention demands of devices We hypothesized that the attentional demands of input devices would be intricately linked to whether the device matched the input requirements of the on-screen task. Further, matching task and device should be more important for attentionally reduced groups, such as older adults. Younger and older adults used either a direct (touch screen) or indirect (rotary encoder) input device to perform matched or mismatched input tasks under a spectrum of attention allocation conditions. Input devices required attention - more so for older adults, especially in a mismatch situation. In addition, task performance was influenced by the mach between task demands and input device characteristics. Though both groups benefited from a match between input device and task input requirements, older adults benefited more and this benefit increased as less attention was available. We offer an a priori method to choose an input device for a task by considering the overlap between device attributes and input requirements. These data have implications for design decisions concerning input device selection across age groups and task contexts.

Assistive technologies have the potential to support a variety of activities for a wide range of user capabilities. A critical design issue is the selection of an input device for the technology. Our research provides guidelines for the selection and design of input devices

In this investigation we evaluated the effect of two types of factors that affect human-automation interaction: those specific to the automation (Error Type: miss versus false alarm) and those specific to the human (domain experience, in this study automated farm equipment experience versus no experience). Participants performed a simulated harvesting task and used an obstacle avoidance automated decision aid. The type of unreliability of the automation had a major impact on behavioral reliance as a function of components of the avoidance decision task. The analysis of the effects of domain experience on automation use indicated that those with experience operating agricultural vehicles had different tendencies of reliance. Specifically, participants with experience operating agricultural vehicles were less likely to rely on automated alarms than those without experience. The results of this investigation have important implications for understanding how humans adjust their behavior according to the characteristics of an automated system

Two experiments examined the effects of general computer experience and age on library system search performance among novice library system users. Twenty younger adults (10 with high and 10 with low computer experience) and 20 older adults (10 with low and 10 with no computer experience) performed 10 search tasks of varying difficulty. Search success, syntax errors, database field specifications, keyword specifications, and use of Boolean operators were examined. Among younger novices, high computer experience was associated with slightly better performance than low computer experience. Among older novices, having some computer experience was associated with much better performance than no computer experience. Older computer users showed lower overall success rates, made more syntax and field specification errors, and demonstrated poorer understanding of Boolean logic and keyword matching algorithms than younger adults with similar computer experience. Implications for interface design and training interventions for novice on-line library system users are discussed.

Computer-based library systems are becoming pervasive throughout public and university libraries. The purpose of the present study was to survey the users of a representative system to assess the degree to which they used the system's functionality, their difficulties with the system, and their experiences learning the system. The majority of the 966 users of the system made limited use of the more advanced system commands and had difficulty understanding how the system works. Importantly, many of the users reported remotely accessing the system, which has implications for the development of training and help systems. The data from the survey are interpreted in the context of suggestions for design and training improvements for library on-line systems.