Optimal performance of collaborative tasks requires consideration of the interactions between intelligent agents and their human counterparts. The functionality and success of these agents lie in their ability to maintain user trust; with too much or too little trust leading to over-reliance and under-utilisation, respectively. This problem highlights the need for an appropriate trust calibration methodology with an ability to vary user trust and decision making in-task. An online experiment was run to investigate whether stimulus difficulty and the implementation of agent features by a collaborative recommender system interact to influence user perception, trust and decision making. Agent features are changes to the Human-Agent interface and interaction style, and include presentation of a disclaimer message, a request for more information from the user and no additional feature. Signal detection theory is utilised to interpret decision making, with this applied to assess decision making on the task, as well as with the collaborative agent. The results demonstrate that decision change occurs more for hard stimuli, with participants choosing to change their initial decision across all features to follow the agent recommendation. Furthermore, agent features can be utilised to mediate user decision making and trust in-task, though the direction and extent of this influence is dependent on the implemented feature and difficulty of the task. The results emphasise the complexity of user trust in Human-Agent collaboration, highlighting the importance of considering task context in the wider perspective of trust calibration.

Optimal performance of collaborative tasks requires consideration of the interactions between socially intelligent agents, such as social robots, and their human counterparts. The functionality and success of these systems lie in their ability to establish and maintain user trust; with too much or too little trust leading to over-reliance and under-utilisation, respectively. This problem highlights the need for an appropriate trust calibration methodology, with the work in this paper focusing on the first step: investigating user trust as a behavioural prior. Two pilot studies (Study 1 and 2) are presented, the results of which inform the design of Study 3. Study 3 investigates whether trust can determine user decision making and task outcome during a human-agent collaborative task. Results demonstrate that trust can be behaviourally assessed in this context using an adapted version of the Trust Game. Further, an initial behavioural measure of trust can significantly predict task outcome. Finally, assistance type and task difficulty interact to impact user performance. Notably, participants were able to improve their performance on the hard task when paired with correct assistance, with this improvement comparable to performance on the easy task with no assistance. Future work will focus on investigating factors that influence user trust during human-agent collaborative tasks and providing a domain-independent model of trust calibration.

In this paper, we present the design of a "CS0" Computational Thinking course at an Australian regional university, that is also offered to non-enrolled students via the Open Universities Australia network. Unlike many CS0 courses, this targets a predominantly mature age and part-time demographic, although high school leavers are also included. Our design attempts to recognise that computational thinking experiences are increasingly incorporated into school experiences, both through curricula and outreach. Consequently, a CS0 course no longer has the sole purpose of introducing students to computing. It also serves a bridging role, giving a compressed form of outreach and school experiences to adult learners who might have missed them. It also brings brings opportunities to compress the introduction of programming, so that more time can be spent in challenges that stretch students' experience, and in demonstrating its application to areas such as robotics and social AI.

Building trust in robots through social interactions has a major impact on user experience and adoption of robot technologies. The role of trust in such interactions is associated with the persuasive influence a robot has on humans. A persuasive attempt may decrease trusting attitudes towards robots if it leads to persuasive backfiring, which refers to the creation of an attitude change in a direction opposite to the one intended by the intervention. In order to explore persuasive backfiring in the context of Human-Robot Interaction, this research study tests the interaction between emotion and logic as elements present both in the attitudes to be influenced, and in the persuasive appeal delivered by a robot. Results indicate a significant backfiring effect when emotions are used to influence attitudes that are based on logic. This observation has practical design implications for persuasive robots, especially in high-stakes fields such as Psychotherapy and Urban Search and Rescue.

Deploying social robots applications in public spaces for conducting in the wild studies is a significant challenge but critical to the advancement of social robotics. Real world environments are complex, dynamic, and uncertain. Human-Robot interactions can be unstructured and unanticipated. In addition, when the robot is intended to be a shared public resource, management issues such as user access and user privacy arise, leading to design choices that can impact on users' trust and the adoption of the designed system. In this paper we propose a user registration and login system for a social robot and report on people's preferences when registering their personal details with the robot to access services. This study is the first iteration of a larger body of work investigating potential use cases for the Pepper social robot at a government managed centre for startups and innovation. We prototyped and deployed a system for user registration with the robot, which gives users control over registering and accessing services with either face recognition technology or a QR code. The QR code played a critical role in increasing the number of users adopting the technology. We discuss the need to develop social robot applications that responsibly adhere to privacy principles, are inclusive, and cater for a broad spectrum of people.

Research in robotics and human-robot interaction is becoming more and more mature. Additionally, more affordable social robots are being released commercially. Thus, industry is currently demanding ideas for viable commercial applications to situate social robots in public spaces and enhance customers experience. However, present literature in human-robot interaction does not provide a clear set of guidelines and a methodology to (i) identify commercial applications for robotic platforms able to position the users» needs at the centre of the discussion and (ii) ensure the creation of a positive user experience. With this paper we propose to fill this gap by providing a methodology for the design of robotic applications including these desired features, suitable for integration by researchers, industry, business and government organisations. As we will show in this paper, we successfully employed this methodology for an exploratory field study involving the trial implementation of a commercially available, social humanoid robot at an airport.

Robots interacting with humans in public spaces often need to collect users' private information in order to provide the required services. Current privacy legislation in major jurisdictions requires organisations to disclose information about their data collection process and obtain user's consent prior to collecting privacy sensitive information. In this study, we consider a privacy-sensitive design of a data collection system for face identification. We deployed a face enrolment system on a humanoid robot with human-like gesturing and speech. We compared it with an equivalent system, in terms of capability and interactive process, on a screen-based interactive kiosk. In our previous contribution, we investigated the effects that embodiment has on users' privacy considerations. We found that an embodied humanoid robot is capable of collecting more private information from users in comparison to a disembodied interactive kiosk. However, this effect was statistically significant only when the two compared systems were using a transparent interface, i.e. an interface communicating to users the privacy policies for data processing and storage. Thus, in this work, we aim to further investigate the effects of transparency on users' privacy considerations and their experience with robot applications. We found that when comparing a non-transparent vs. transparent interface within the same system (i.e. on an embodied robot or on a disembodied kiosk) transparency does not lead to significant effects on users' privacy considerations. However, we found that transparency leads to a significantly better user experience for both systems. Therefore, our overall analyses suggest that both the interactive robot and the interactive kiosk are capable of enhancing the user experience by providing transparent information to users, which is required by privacy legislation. However, an interactive kiosk providing transparent information elicits significantly more privacy concerns in users as compared to the robot supplying the very same transparent information. This exploratory study provides conclusions that provide valuable insights for designing robot applications dealing with users privacy and it discusses the related legal implications, concluding with recommendations for privacy policymakers.

When robots and human users collaborate, trust is essential for user acceptance and engagement. In this paper, we investigated two factors thought to influence user trust towards a robot: preference elicitation (a combination of user involvement and explanation) and embodiment. We set our experiment in the application domain of a restaurant recommender system, assessing trust via user decision making and perceived source credibility. Previous research in this area uses simulated environments and recommender systems that present the user with the best choice from a pool of options. This experiment builds on past work in two ways: first, we strengthened the ecological validity of our experimental paradigm by incorporating perceived risk during decision making; and second, we used a system that recommends a nonoptimal choice to the user. While no effect of embodiment is found for trust, the inclusion of preference elicitation features significantly increases user trust towards the robot recommender system. These findings have implications for marketing and health promotion in relation to Human-Robot Interaction and call for further investigation into the development and maintenance of trust between robot and user.

Nowadays, robots are gradually appearing in public spaces such as libraries, train stations, airports and shopping centres. Only a limited percentage of research literature explores robot applications in public spaces. Studying robot applications in the wild is particularly important for designing commercially viable applications able to meet a specific goal. Therefore, in this paper we conduct an experiment to test a robot application in a shopping centre, aiming to provide results relevant for today's technological capability and market. We compared the performance of a robot and a human in promoting food samples in a shopping centre, a well known commercial application, and then analysed the effects of the type of engagement used to achieve this goal. Our results show that the robot is able to engage customers similarly to a human as expected. However unexpectedly, while an actively engaging human was able to perform better than a passively engaging human, we found the opposite effect for the robot. In this paper we investigate this phenomenon, with possible explanation ready to be explored and tested in subsequent research.

Pointing is a typical means of directing a human's attention to a specific object or event. Robot pointing behaviours that direct the attention of humans are critical for human-robot interaction, communication and collaboration. In this paper, we describe an experiment undertaken to investigate human comprehension of a humanoid robot's pointing behaviour. We programmed a NAO robot to point to markers on a large screen and asked untrained human subjects to identify the target of the robots pointing gesture. We found that humans are able to identify robot pointing gestures. Human subjects achieved higher levels of comprehension when the robot pointed at objects closer to the gesturing arm and when they stood behind the robot. In addition, we found that subjects performance improved with each assessment task. These new results can be used to guide the design of effective robot pointing behaviours that enable more effective robot to human communication and improve human-robot collaborative performance.