As technology advances and the demand for STEM workers increases, libraries are evolving to meet the changing needs of their communities. One way they do this is by providing robotics education opportunities for children. However, there is limited research on robotics education in public libraries, and there is a lack of adequate evaluation methods for assessing the impact of informal STEM learning. This pilot study aimed to investigate the effectiveness of conducting summer robotics activities in UK public libraries and the questionnaire design used to assess children's interest in robotics, STEM and STEM careers. Over three weeks, eight robotics activities were conducted in Leeds Libraries, with eighty-one children aged between 6 and 13 from the HealthyHolidays Programme in Leeds participating. The results of the paired samples t-test showed that the children's interest in robotics and related careers significantly increased from the pre-test to the post-test. However, the robotics activity had little influence on children's STEM and STEM related career. This study contributes to the existing literature by highlighting the need for further research on robotics education in public libraries and the development of better questionnaires that can be well comprehended by primary students and better evaluate the impact of the informal learning experience. Nonetheless, this study shows that libraries can play a vital role in fostering technology literacy in communities.

Despite the importance of context in Recommender Systems (RSs) more generally, and its clear applicability in the food domain, most existing research focuses on single contextual factors, and only considers simple extrinsic factors such as location and time. No RSs research has systematically explored the impact of multiple dynamic factors, or investigated the effect of emotion in determining people's eating, recipe rating and nutritional intake behaviour. To bridge these gaps, we conducted a comprehensive large-scale (n=397) crowdsourced experimental study to uncover the intricate relationship between various simulated contextual factors and users' subsequent recipe rating and implied nutritional intake behaviour. We further aimed to explore how these contextual factors can be incorporated to improve recommendation performance. Four distinct types of contextual factors were investigated: seasonal, emotional, busyness and physical activity, encompassing a total of seven elements. Our findings show that people's eating preferences and the likelihood of them choosing to eat healthy recipes vary depending on the simulated context they find themselves in. Moreover, we demonstrate how these contextual features can be used to significantly improve recipe rating prediction performance. Our research has implications for the future development of food RSs, and shows that emotion-aware systems could lead to better healthy food recommendations.

There is an increasing interest in considering, measuring, and implementing trust in human-robot interaction (HRI). New avenues in this field include identifying social means for robots to influence trust, and identifying social aspects of trust such as a perceptions of robots’ integrity, sincerity or even benevolence. However, questions remain regarding robots’ authenticity in obtaining trust through social means and their capacity to increase such experiences through social interaction with users. We propose that the dyadic model of HRI misses a key complexity: a robot’s trustworthiness may be contingent on the user’s relationship with, and opinion of, the individual or organisation deploying the robot (termed here, Deployer). We present a case study in three parts on researching HRI and a LEGO ® Serious ® Play focus group on care robotics to indicate how Users’ trust towards the Deployer can affect trust towards robots and robotic research. Our Social Triad model (User, Robot, Deployer) offers novel avenues for exploring trust in a social context.

Hit song prediction, one of the emerging fields in music information retrieval (MIR), remains a considerable challenge. Being able to understand what makes a given song a hit is clearly beneficial to the whole music industry. Previous approaches to hit song prediction have focused on using audio features of a record. This study aims to improve the prediction result of the top 10 hits among Billboard Hot 100 songs using more alternative metadata, including song audio features provided by Spotify, song lyrics, and novel metadata-based features (title topic, popularity continuity and genre class). Five machine learning approaches are applied, including: k-nearest neighbours, Naive Bayes, Random Forest, Logistic Regression and Multilayer Perceptron. Our results show that Random Forest (RF) and Logistic Regression (LR) with all features (including novel features, song audio features and lyrics features) outperforms other models, achieving 89.1% and 87.2% accuracy, and 0.91 and 0.93 AUC, respectively. Our findings also demonstrate the utility of our novel music metadata features, which contributed most to the models' discriminative performance.

AI and robots have the potential to transform Higher Education (HE) but pose many ethical and implementation challenges. To ensure the widest debate about our choices for the future of HE with these technologies, engaging ways to present the issues are needed and this article is part of an exploration of the potential of fictional narratives to do so. Specifically, the purpose of this article is to enrich understanding of quality in such fiction-based research, through analysing responses to a collection of fictions from a group of expert readers. A starting point was synthesising previous attempts to articulate notions of quality. The discussions with the readers suggest that the key qualities were substantive contribution, credibility, resonance, ambiguity and aesthetics; rich rigour and sincerity need also to be considered. Fiction has a place in educational research because it enables one to imagine vividly different possibilities, presents issues in an open-ended way, and is engaging.

Despite the increasing interest in trust in human-robot interaction (HRI), there is still relatively little exploration of trust as a social construct in HRI. We propose that integration of useful models of human-human trust from psychology, highlight a potentially overlooked aspect of trust in HRI: a robot's apparent trustworthiness may indirectly relate to the user's relationship with, and opinion of, the individual or organisation deploying the robot. Our Social Triad for HRI model (User, Robot, Deployer), identifies areas for consideration in co-creating trustworthy robotics.

As robots are introduced to more and more complex scenarios, the issues of trust become more complex as various groups, peoples, and entities begin to interact with a deployed robot. This short paper explores a few scenarios in which the trust of the robot may come into conflict between one (or more) entities or groups that the robot is required to deal with. We also present a scenario concerning the idea of repairing trust through a possible apology.

Disabled people are often involved in robotics research as potential users of technologies which address specific needs. However, their more generalised lived expertise is not usually included when planning the overall design trajectory of robots for health and social care purposes. This risks losing valuable insight into the lived experience of disabled people, and impinges on their right to be involved in the shaping of their future care. This project draws upon the expertise of an interdisciplinary team to explore methodologies for involving people with disabilities in the early design of care robots in a way that enables incorporation of their broader values, experiences and expectations. We developed a comparative set of focus group workshops using Community Philosophy, LEGO® Serious Play® and Design Thinking to explore how people with a range of different physical impairments used these techniques to envision a “useful robot”. The outputs were then workshopped with a group of roboticists and designers to explore how they interacted with the thematic map produced. Through this process, we aimed to understand how people living with disability think robots might improve their lives and consider new ways of bringing the fullness of lived experience into earlier stages of robot design. Secondary aims were to assess whether and how co-creative methodologies might produce actionable information for designers (or why not), and to deepen the exchange of social scientific and technical knowledge about feasible trajectories for robotics in health-social care. Our analysis indicated that using these methods in a sequential process of workshops with disabled people and incorporating engineers and other stakeholders at the Design Thinking stage could potentially produce technologically actionable results to inform follow-on proposals.

This paper discusses Responsible (Research and) Innovation (RRI) within a UKRI project funded through the Trustworthy Autonomous Systems Hub, Imagining Robotic Care: Identifying conflict and confluence in stakeholder imaginaries of autonomous care systems. We used LEGO® Serious Play® as an RRI methodology for focus group workshops exploring sociotechnical imaginaries about how robots should (or should not) be incorporated into the existing UK health-social care system held by care system stakeholders, users and general publics. We outline the workshops' protocol and some emerging insights from early data collection, including the ways that LSP aids in the surfacing of tacit knowledge, allowing participants to develop their own scenarios and definitions of 'robot' and 'care'. We further discuss the implications of LSP as a method for upstream stakeholder engagement in general and how this may contribute to embedding RRI in robotics research on a larger scale.

In the last ten years, there have been great efforts to increase automation in the health-social care ecosystem, including the use of robotics to provide practical and social care. However, development of these robots often does not include potential users until late in the design process, so they may not adequately address user expectations or needs. This pilot introduces LEGO Serious Play workshops as design tools to support individuals' articulation of the potential benefits and consequences of robot care systems. The narratives elicited address key themes in robotics for care, indicating the workshops' potential use early in design.

Collaborative robots offer opportunities to increase the sustainability of work and workforces by increasing productivity, quality, and efficiency, whilst removing workers from hazardous, repetitive, and strenuous tasks. They also offer opportunities for increasing accessibility to work, supporting those who may otherwise be disadvantaged through age, ability, gender, or other characteristics. However, to maximise the benefits, employers must overcome negative attitudes toward, and a lack of confidence in, the technology, and must take steps to reduce errors arising from misuse. This study explores how dynamic graphical signage could be employed to address these issues in a manufacturing task. Forty employees from one UK manufacturing company participated in a field experiment to complete a precision pick-and-place task working in conjunction with a collaborative robotic arm. Twenty-one participants completed the task with the support of dynamic graphical signage that provided information about the robot and the activity, while the rest completed the same task with no signage. The presence of the signage improved the completion time of the task as well as reducing negative attitudes towards the robots. Furthermore, participants provided with no signage had worse outcome expectancies as a function of their response time. Our results indicate that the provision of instructional information conveyed through appropriate graphical signage can improve task efficiency and user wellbeing, contributing to greater workforce sustainability. The findings will be of interest for companies introducing collaborative robots as well as those wanting to improve their workforce wellbeing and technology acceptance.

Technology is transforming societies worldwide. A major innovation is the emergence of robotics and autonomous systems (RAS), which have the potential to revolutionize cities for both people and nature. Nonetheless, the opportunities and challenges associated with RAS for urban ecosystems have yet to be considered systematically. Here, we report the findings of an online horizon scan involving 170 expert participants from 35 countries. We conclude that RAS are likely to transform land use, transport systems and human–nature interactions. The prioritized opportunities were primarily centred on the deployment of RAS for the monitoring and management of biodiversity and ecosystems. Fewer challenges were prioritized. Those that were emphasized concerns surrounding waste from unrecovered RAS, and the quality and interpretation of RAS-collected data. Although the future impacts of RAS for urban ecosystems are difficult to predict, examining potentially important developments early is essential if we are to avoid detrimental consequences but fully realize the benefits. The future challenges and potential opportunities of robotics and autonomous systems in urban ecosystems, and how they may impact biodiversity, are explored and prioritized via a global horizon scan of 170 experts.

This paper describes the concepts that have emerged within Quando, an end user toolset, used for creating digital interactives through browser based code generation for museums and theatre. End user focused concepts and recommendations have been identified with Research through Design, including the ability to create new visual blocks as part of the toolset.

There is an increasing interest in considering, implementing, and measuring trust in human-robot interaction (HRI). Typically, this centres on influencing user trust within the framing of HRI as a dyadic interaction between robot and user. We propose this misses a key complexity: a robot's trustworthiness may also be contingent on the user's relationship with, and opinion of, the individual or organisation deploying the robot. Our new HRI triad model (User, Robot, Deployer), offers novel predictions for considering and measuring trust more completely.

As robots become more prevalent, particularly in complex public and domestic settings, they will be increasingly challenged by dynamic situations that could result in performance errors. Such errors can have a harmful impact on a user’s trust and confidence in the technology, potentially reducing use and preventing full realization of its benefits. A potential countermeasure, based on social psychological concepts of trust, is for robots to demonstrate self-awareness and ownership of their mistakes to mitigate the impact of errors and increase users’ affinity towards the robot. We describe an experiment examining 326 people’s perceptions of a mobile guide robot that employs synthetic social behaviours to elicit trust in its use after error. We find that a robot that identifies its mistake, and communicates its intention to rectify the situation, is considered by observers to be more capable than one that simply apologizes for its mistake. However, the latter is considered more likeable and, uniquely, increases people’s intention to use the robot. These outcomes highlight that the complex and multifaceted nature of trust in human–robot interaction may extend beyond established approaches considering robots’ capability in performance and indicate that social cognitive models are valuable in developing trustworthy synthetic social agents.

This paper provides a speculative, conceptual and literature-based review of the relationship between disability and new technologies with a specific focus on inclusive education for disabled people. The first section critically explores disability and new technologies in a time of Industry 4.0. We lay out some concerns that we have, especially in relation to disabled people’s peripheral positionality, when it comes to these new developments. The second section focuses on the area of inclusive education. Inclusion and education are oftentimes in conflict with one another. We tease out these conflicts and argue that we cannot decouple the promise of new technologies from the challenges of inclusive education, because, in spite of the potential for technological mediation to broaden access to education, there remains deep-rooted problems with exclusion. The third section of our paper explores affirmative possibilities in relation to the interactions between disability and new technologies. We draw on the theoretical fields of Science and Technology Studies; Critical Disability Studies; Assistive and Inclusive Technologies; Collaborative Robotics, Maker and DIY Cultures and identify a number of key considerations that relate directly to the revaluing of inclusive education. We conclude our paper by identifying what we view as pressing and immediate concerns for inclusive educators when considering the merging of disability and technology, accessibility and learning design.

There have been multiple calls for integrating topics related to fairness, accountability, transparency, ethics (FATE) and social justice into Data Science curricula, but little exploration of how this might work in practice. This paper presents the findings of a collaborative auto-ethnography (CAE) engaged in by a MSc Data Science teaching team based at University of Sheffield (UK) Information School where FATE/Critical Data Studies (CDS) topics have been a core part of the curriculum since 2015/16. In this paper, we adopt the CAE approach to reflect on our experiences of working at the intersection of disciplines, and our progress and future plans for integrating FATE/CDS into the curriculum. We identify a series of challenges for deeper FATE/CDS integration related to our own competencies and the wider socio-material context of Higher Education in the UK. We conclude with recommendations for ourselves and the wider FATE/CDS orientated Data Science community.

This paper outlines a social robot platform for providing health information. In comparison with previous findings for accessing information online, the use of a social robot may affect which factors users consider important when evaluating the trustworthiness of health information provided.