This work presents a platform that facilitates sustainability learning through waste management principles - reducing and reusing waste from everyday items. The platform features (a) an AI assistant that helps users express and develop their ideas about sustainable practices, and (b) a commentary interface to support asynchronous collaboration. The AI agent provides context-aware suggestions while encouraging users to modify and personalize these recommendations, creating a collaborative approach to sustainability ideation. A user study was conducted, and the effectiveness of the approach was evaluated. Results demonstrated increased sustainability awareness among participants after using the platform, with varying patterns of improvement across different sustainability approaches. In particular, users who actively modified AI suggestions produced higher-quality contributions with more specific actionable recommendations compared to those who directly copied AI's responses. The study also reveals insights into how AI assistance affects content quality. These findings contribute to understanding how AI can be effectively integrated into sustainability education platforms to enhance learning outcomes.

ABSTRACT The emergence of AI‐driven technologies often necessitates the collection of private user information to deliver personalised services and enhance the overall user experience. Given the recurring incidents of data breaches, awareness of privacy risks and concerns about disclosing personal information to AI‐driven applications has significantly increased. Privacy concerns have become a critical issue, heavily influencing users' intentions to interact with such systems. To appropriately investigate the antecedents and consequences of disclosing private information, this study examines the influence of social presence (humanlike vs. non‐humanlike media) on privacy concerns and information disclosure across different types of data sensitivities (including retail, financial, and medical data). An online survey (N = 282) and a lab experiment (N = 70) were conducted, incorporating multiple experimental tasks under various conditions. The results reveal that both social presence and data sensitivity significantly impact privacy concerns and information disclosure. Additionally, a privacy paradox is observed: while participants express concern about privacy, their attitudinal and behavioural intentions shift, indicating a willingness to trade sensitive information for enhanced services. The findings also show that individual personality traits strongly influence one's intention to disclose personal information when interacting with humanlike media. Furthermore, when investigating privacy concerns, it is essential to move beyond task‐driven assessments. Instead, identifying the specific types of private information involved and adopting a data‐driven perspective provides a more accurate understanding of privacy‐related behaviours.

The use of humanoid robots has surged in recent decades. However, the nonverbal features in shaping robot personalities remain underexplored. This study investigates how nonverbal cues (including textual and gestural elements) can generate a spectrum of robot personality traits (introvert, ambivert, and extrovert) and evaluates their impact on users’ cognitive perceptions. Textual manipulations involved three iterations, adjusting word count, information structure, and visual effects. Gestural designs underwent two iterations, altering movement frequency, speed, and size. Multiple empirical studies were conducted to assess the development of robot personality traits and their effects. The results confirm the effectiveness of these nonverbal approaches in characterizing diverse robot personalities and significantly influencing users’ cognitive framing. This research provides valuable design guidelines for leveraging a humanoid robot’s nonverbal features to create a variety of personality traits. Our findings emphasize the importance of considering a spectrum of robot personalities rather than focusing solely on extreme traits.

This study examines the impact of Explainable AI (XAI) on users’ cognitive and affective responses, with a particular emphasis on cross-cultural differences. Utilizing the Situation Awareness-Based Agent Transparency model, our XAI mechanisms varied in terms of transparency levels and explanation types. Survey studies conducted in the United States (N = 1200) and Taiwan (N = 600) assessed the cultural influences on XAI perception. Our findings identified significant cultural differences, with Western cultures demonstrating an increased awareness of data privacy and exhibiting a pronounced reluctance to trust AI services. The results further revealed that Eastern cultures emphasized rational analysis in evaluating privacy risk, whereas Western cultures were more inclined to employ emotional responses to assess privacy concerns. Regarding the effectiveness of XAI mechanisms, both the low system transparency with an example-based method and high system transparency with a feature-based method yielded similar positive outcomes. Additionally, while the U.S. group exhibited little variance between conditions, Taiwanese participants demonstrated heightened sensitivity to differences in XAI approaches.

Responsible waste management is important for our sustainable living. However, there are still many challenges ahead, such as properly sort the wastes. In this paper, we conducted a Theory of Planned Behavior (TPB)-based study to understand the impact of waste management literacy on people's intentions. Our results indicate that increasing the literacy level can positively influence people's intentions to actively sort their wastes. Additionally, we also proposed a mobile application that includes an intelligent Waste Detect Engine (WDE) and a Waste management Knowledge Agent (WMKA) to facilitate people learn. In a subsequent user study, we observed a significant improvement in users' waste sorting knowledge with the assistance of the proposed educational technology.

The present study collects a large amount of HRI-related research studies and analyzes the research trends from 2010 to 2021. Through the topic modeling technique, our developed ML model is able to retrieve the dominant research factors. The preliminary results reveal five important topics, handover, privacy, robot tutor, skin de deformation, and trust. Our results show the research in the HRI domain can be divided into two general directions, namely technical and human aspects regarding the use of robotic applications. At this point, we are increasing the research pool to collect more research studies and advance our ML model to strengthen the robustness of the results.

Social media allows any individual to disseminate information without third-party restrictions, making it difficult to verify the authenticity of a source. The proliferation of fake news has severely affected people’s intentions and behaviors in trusting online sources. Applying AI approaches for fake news detection on social media is the focus of recent research, most of which, however, focuses on enhancing AI performance. This study proposes XFlag, an innovative explainable AI (XAI) framework which uses long short-term memory (LSTM) model to identify fake news articles, layer-wise relevance propagation (LRP) algorithm to explain the fake news detection model based on LSTM, and situation awareness-based agent transparency (SAT) model to increase transparency in human-AI interaction. The developed XFlag framework has been empirically validated. The findings suggest the use of XFlag supports users in understanding system goals (perception), justifying system decisions (comprehension), and predicting system uncertainty (projection), with little cost of perceived cognitive workload.

The reported study compares groups of 120 participants each, from the United States (U.S.), Taiwan (TW), and Turkey (TK), interacting with versions of an automated path planner that vary in transparency and degree of automation. The nationalities were selected in accordance with the theory of cultural syndromes as representatives of Dignity (U.S.), Face (TW), and Honor (TK) cultures, and were predicted to differ in readiness to trust automation, degree of transparency required to use automation, and willingness to use systems with high degrees of automation. Three experimental conditions were tested. In the first, highlight, path conflicts were highlighted leaving rerouting to the participant. In the second, replanner made requests for permission to reroute when a path conflict was detected. The third combined condition increased transparency of the replanner by combining highlighting with rerouting to make the conflict on which decision was based visible to the user. A novel framework relating transparency, stages of automation, and trust in automation is proposed in which transparency plays a primary role in decisions to use automation but is supplemented by trust where there is insufficient information otherwise. Hypothesized cultural effects and framework predictions were confirmed.

Trust in automation has become a topic of intensive study since the late 1990s and is of increasing importance with the advent of intelligent interacting systems. While the earliest trust experiments involved human interventions to correct failures/errors in automated control systems, a majority of subsequent studies have investigated information acquisition and analysis decision aiding tasks such as target detection for which automation reliability is more easily manipulated. Despite the high level of international dependence on automation in industry, almost all current studies have employed Western samples primarily from the U.S. The present study addresses these gaps by running a large sample experiment in three (U.S., Taiwan, and Turkey) diverse cultures using a “trust sensitive task” consisting of both automated control and target detection subtasks. This article presents results for the target detection subtask for which reliability and task load were manipulated. The current experiments allow us to determine whether reported effects are universal or specific to Western culture, vary in baseline or magnitude, or differ across cultures. Results generally confirm consistent effects of manipulations across the three cultures as well as cultural differences in initial trust and variation in effects of manipulations consistent with 10 cultural hypotheses based on Hofstede's Cultural Dimensions and Leung and Cohen's theory of Cultural Syndromes. These results provide critical implications and insights for correct trust calibration and to enhance human trust in intelligent automation systems across cultures. Additionally, our results would be useful in designing intelligent systems for users of different cultures. Our article presents the following contributions: First, to the best of our knowledge, this is the first set of studies that deal with cultural factors across all the cultural syndromes identified in the literature by comparing trust in the Honor, Face, Dignity cultures. Second, this is the first set of studies that uses a validated cross-cultural trust measure for measuring trust in automation. Third, our experiments are the first to study the dynamics of trust across cultures.

The use of autonomous systems has been rapidly increasing in recent decades. To improve human-automation interaction, trust has been closely studied. Research shows trust is critical in the development of appropriate reliance on automation. To examine how trust mediates the human-automation relationships across cultures, the present study investigated the influences of cultural factors on trust in automation. Theoretically guided empirical studies were conducted in the U.S., Taiwan and Turkey to examine how cultural dynamics affect various aspects of trust in automation. The results found significant cultural differences in human trust attitude in automation.

Robotic swarms are distributed systems whose members interact via local control laws to achieve a variety of behaviors, such as flocking. In many practical applications, human operators may need to change the current behavior of a swarm from the goal that the swarm was going towards into a new goal due to dynamic changes in mission objectives. There are two related but distinct capabilities needed to supervise a robotic swarm. The first is comprehension of the swarm's state and the second is prediction of the effects of human inputs on the swarm's behavior. Both of them are very challenging. Prior work in the literature has shown that inserting the human input as soon as possible to divert the swarm from its original goal towards the new goal does not always result in optimal performance (measured by some criterion such as the total time required by the swarm to reach the second goal). This phenomenon has been called Neglect Benevolence, conveying the idea that in many cases it is preferable to neglect the swarm for some time before inserting human input. In this paper, we study how humans can develop an understanding of swarm dynamics so they can predict the effects of the timing of their input on the state and performance of the swarm. We developed the swarm configuration shape-changing Neglect Benevolence Task as a Human Swarm Interaction (HSI) reference task allowing comparison between human and optimal input timing performance in control of swarms. Our results show that humans can learn to approximate optimal timing and that displays which make consensus variables perceptually accessible can enhance performance.

A wide class of multirobot control tasks involves operator interactions with individual robots. Where the robots' actions are independent, as for example in some foraging tasks, the operator can interact with robots sequentially in a round robin fashion. If the need for interaction can be detected by the robot through self-reflection, the robot could communicate its need for interaction to the operator. The resulting human-robot system would form a queuing system in which the operator is the server and the queue of robots requesting interaction, the jobs. As a queuing system, performance could be optimized using standard techniques, providing the operator's attention could be appropriately directed. An earlier study found that Human-Robot Interaction (HRI) performance was improved by communicating requests for interaction to the operator, however, a first-in-first-out (FIFO) aid showing a single request at a time led to poorer performance than one showing the entire (Open) queue. The current experiment compared Open-queue and FIFO conditions from the first experiment with a Priority-queue using a shortest job first (SJF) discipline known to maximize throughput. Performance in the Priority-queue condition was statistically indistinguishable from the best performance for all measures except those for missed victims where it was intermediate between FIFO (best) and Open-queue. Both of the other conditions produced poorest performance on some measures. The results suggest that operator attention can be effectively scheduled allowing the use of scheduling algorithms to improve the efficiency of HRI.

The present study investigates the effect of imperfect automation in a human multi-robot controlled environment with different principles for scheduling an operator's attention in a foraging task. The experiment compared a SJF-queue (shortest job first) presenting a single alarm at a time with an Open-queue which showed all current alarms. Two levels of automation reliability, high (90%) and low (50%), were examined in the study. Performance for the queue mechanisms was equivalent confirming that operator attention can be effectively directed to improve performance. Additionally, the higher reliability condition raised an operator's success rate for resolving robot failures and assisted the operator in allocating attention to emergent events in a timely manner. Although the more frequent alerts contributed to better performance operators experienced increased levels of workload.

In this paper, we present an asynchronous display method, coined image queue, which allows operators to search through a large amount of data gathered by autonomous robot teams. We discuss and investigate the advantages of an asynchronous display for foraging tasks with emphasis on Urban Search and Rescue. The image queue approach mines video data to present the operator with a relevant and comprehensive view of the environment in order to identify targets of interest such as injured victims. It fills the gap for comprehensive and scalable displays to obtain a network-centric perspective for UGVs. We compared the image queue to a traditional synchronous display with live video feeds and found that the image queue reduces errors and operator's workload. Furthermore, it disentangles target detection from concurrent system operations and enables a call center approach to target detection. With such an approach we can scale up to very large multi-robot systems gathering huge amounts of data that is then distributed to multiple operators.

Using classical methods the time course of workload over a work session is rarely observed. Subjective measures of necessity are for the entire session rather than instantaneous. While this distinction makes little difference to researchers wanting to compare task difficulty, it is important to designers of systems that wish to schedule operator attention because they must predict workload. The present study provides an experimental setting in which intervals of continuous work and uninterrupted rest can be precisely controlled. No differences in probability of error or latency of response were found for the length of break/rest intervals or their location in a sequence of intervals. The local utilization within a break/work interval (ratio of work/(work + rest)), however, was significant, suggesting that utilization effects are strictly local and that simple algorithms could be used to optimize human-system performance for such tasks.

Technology for multirobot systems has advanced to the point where we can consider their use in a variety of important domains, including urban search and rescue. A key to the practical usefulness of multirobot systems is the ability to have a large number of robots effectively controlled by small numbers of operators. In this paper, two modalities for controlling a team of 24 robots in a foraging task in an urban search and rescue environment are compared. In both modalities, multiple operators must monitor video streams from the robots to detect and mark victims on a map as well as teleoperating robots that cannot get themselves out of difficult situations. In the first modality, the operators must also provide waypoints for the robots to explore, using both video and a partially completed map to choose appropriate waypoints. In the second modality, the robots autonomously plan their paths, allowing operators to focus on monitoring the video, but without being able to interpret video streams to guide exploration. Experimental results show that significantly better overall performance is achieved with autonomous path planning, although the reduction in operator workload is not significant.

We are developing a theory for human control of robot teams based on considering how control varies across different task allocations. Our current work focuses on domains such as foraging in which robots perform largely independent tasks. The present study addresses the interaction between automation and organization of human teams in controlling large robot teams performing an Urban Search and Rescue (USAR) task. We identify three subtasks: perceptual search-visual search for victims, assistance-teleoperation to assist robot, and navigation-path planning and coordination. For the studies reported here, navigation was selected for automation because it involves weak dependencies among robots making it more complex and because it was shown in an earlier experiment to be the most difficult. Two possible ways to organize operators were identified as assignment of robots to particular operators or as a shared pool in which operators service robots from the population as needed. The experiment compares two member teams of operators controlling teams of 12 robots each in the assigned robots conditions or sharing control of 24 robots in the shared pool conditions using either waypoint control or autonomous path planning. We identify three self organizing team strategies in the shared pool condition: joint control operators share full authority over robots, mixed control in which one operator takes primary control while the other acts as an assistant, and split control in which operators divide the robots with each controlling a subteam. Automating path planning improved system performance. Effects of team organization favored operator teams who shared authority for the pool of robots.

We are developing an architecture for controlling robot teams based on considering how control difficulty for different tasks grows with increases in team size. Our analysis suggests that assignments of persons to commander (single commands to entire robot team), operator (commands to individual robots), and coordinator (control of interdependent robots) roles can lead to the most efficient organization. The ability to assign tasks within or between operators makes scheduling these interactions an important factor in team performance. Two possible ways to organize operators are through Individual Assignments of robots or as a Call Center in which operators service robots from the population as needed. In recent experiments we have found that participants performing an Urban Search And Rescue (USAR) foraging task using waypoint control were at or over their limits when controlling 12 robots. The present study uses the same robots, environment, and level of autonomy but with teams of two operators assigned to control 24 robots. These operators controlled teams of 12 robots in the Individual Assignment condition. In the Call Center condition operators shared control of the 24 robots. For this task and level of robot autonomy Individual Assignment participants performed marginally better searching larger regions but without finding more victims.