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AbstractThis Systematic Literature Review explores the evolving landscape of consumer behaviour towards DVAs using the TCCM (Theory-Context-Characteristics-Methodology) framework. Through a comprehensive analysis of 64 research papers sourced from the Scopus database, indexed in the ABDC (Australian Business Deans Council) category list, and published until December 2023, we uncover pivotal themes and significant knowledge gaps in the current body of research. The findings show that most studies on consumer-DVA interactions are empirical in nature, predominantly focused on the shopping and hospitality sectors, and grounded in the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) frameworks. Applying the TCCM framework, this paper uncovers fertile ground for future research, highlighting opportunities for further exploration and development. Building on the identified research gaps and our insights, we present integrated frameworks on consumer behaviour towards DVAs. Additionally, we provide actionable recommendations for DVA researchers, developers, and marketers, highlighting critical areas for future research, such as integrating sentiment analysis and emotional recognition capabilities. We also propose tailored strategies, including customizable DVA personalities and engaging voice experiences, to enhance consumer engagement and brand loyalty. The paper concludes with a summary of limitations and key findings. [ABSTRACT FROM AUTHOR]

AbstractSituational awareness and decision support tools such as procedures and alarm systems are vital for effective interaction among control room operators, especially in safety-critical situations. In safety-critical environments such as process plants, there remains a gap in evaluating specific tools during actual operations, or ”work-as-done.” Additionally, the underlying factors that might impact operators’ cognitive states and performance concerning safety have not been thoroughly explored. The need for such an evaluation is further bolstered by current interaction configurations where operators are more passive than active, thus reducing their cognitive performance. Therefore, this experimental study addresses the highlighted evaluation gap by introducing and comparing three human system interfaces/decision support tools in four human-in-the-loop configurations. The supports include two alarm design formats (prioritized vs. non-prioritized) and three procedure representation formats (paper, screen-based digitized, and an AI-based support system built with an integrated Bayesian network and reinforcement learning model). Ninety-two people (n = 92) participated voluntarily in the test. They were divided equally into four groups. Each group tested three safety-related events in a simulated formaldehyde production facility. Individuals belonging to the group with prioritized alarms and utilized paper procedures rated procedural support slightly higher on average than others in different groups. Unlike the other groups, their assessment of alarm prioritization support remained consistent across all scenarios. Further analysis of the impact of the setup on cognitive states and actual performance will be performed. [ABSTRACT FROM AUTHOR]

AbstractThe paper explores the synergy between wearable data and augmented Artificial Intelligence (AI) through findings from two interconnected studies. The first study (study 1) focuses on medical professionals’ perceptions of wearable data and AI, and the second study (Study 2) extends it focuses on how differences in the level of granularity in the data presented affect the professionals’ understanding, interpretation, and trust in AI recommendations. This system allows medical professionals to view AI-generated recommendations for sleep and activity improvement and explanations of the underlying rationale. While each study has distinct research questions, Study 2 builds upon Study 1's foundation. Both studies employed scenario-based interviews. Thematic analysis of Study 1 identified trust as a crucial factor in the acceptance of wearable data and AI, influencing Study 2's exploration of factors affecting trust, such as explainability, data granularity, representativeness, and user interaction. Study 2 highlighted varying perspectives on information sufficiency and data sharing from the AI system linked to professionals’ roles and tasks. The work offers insights into data granularity’s impact on engagement with AI recommendations. [ABSTRACT FROM AUTHOR]

In the context of intelligent systems for human-vehicle collaboration, the fusion of information space, physical space, and user cognitive space has become a trend. This paper aims to address the challenges posed by information perception gaps and cognitive limitations experienced by drivers by leveraging Augmented Reality Head-up Displays (ie, AR-HUD) to compensate for perceptual deficiencies and enhance driver cognition. We introduce the innovative concept of the Human-Machine Team Situation Awareness (ie, TSA) loop model. Firstly, we analyze the cognitive characteristics of drivers and the spatiotemporal information elements within hazardous scenarios. Subsequently, AR-HUDs are employed to provide drivers with perceptual compensation and cognitive enhancement. Furthermore, we design AR-HUD interfaces for two representative scenarios. The results demonstrate that, with the support of AR-HUDs, the integration of dynamic interface elements proves to be more effective in compensating for perceptual deficiencies, and the inclusion of predictive information contributes to improved driving performance. Notably, in emergency situations, AR-HUDs play a crucial role in providing decision-enhancing information to drivers. The proposed theoretical framework offers opportunities for expanding the theoretical approaches and application domains of AR-HUDs. [ABSTRACT FROM AUTHOR]

AbstractIntelligent environments are rapidly gaining ground, propelled by a rich sensor infrastructure, the Internet of Things, sophisticated reasoning capabilities, and Artificial Intelligence. In this complex technological landscape, crafting usable intelligent environments and assessing the user experience (UX) demands a thorough understanding of the concepts involved and the parameters that need to be studied. This paper carries out a review of usability and UX evaluation methods and frameworks, elaborating on fundamental concepts and presenting in detail approaches and methods reported in the literature. It additionally examines evaluation approaches in adaptive and ubiquitous computing systems, which are closely associated with intelligent environments, and presents UX challenges and evaluation frameworks in intelligent environments. Finally, the findings are synthesized and consolidated to produce a comprehensive overview of the field and the challenges that lie ahead. [ABSTRACT FROM AUTHOR]

AbstractIn complex industrial and chemical process control rooms, effective decision-making is crucial for safety and efficiency. The experiments in this paper evaluate the impact and applications of an AI-based decision support system integrated into an improved human-machine interface, using dynamic influence diagrams, a hidden Markov model, and deep reinforcement learning. The enhanced support system aims to reduce operator workload, improve situational awareness, and provide different intervention strategies to the operator adapted to the current state of both the system and human performance. Such a system can be particularly useful in cases of information overload when many alarms and inputs are presented all within the same time window, or for junior operators during training. A comprehensive cross-data analysis was conducted, involving 47 participants and a diverse range of data sources such as smartwatch metrics, eye-tracking data, process logs, and responses from questionnaires. The results indicate interesting insights regarding the effectiveness of the approach in aiding decision-making, decreasing perceived workload, and increasing situational awareness for the scenarios considered. Additionally, the results provide insights to compare differences between styles of information gathering when using the system by individual participants. These findings are particularly relevant when predicting the overall performance of the individual participant and their capacity to successfully handle a plant upset and the alarms connected to it using process and human-machine interaction logs in real-time which resulted in a 95.8% prediction accuracy using hidden Markov model. These predictions enable the development of more effective intervention strategies. [ABSTRACT FROM AUTHOR]

AbstractResearch in explainable AI (XAI) aims to provide insights into the decision-making process of opaque AI models. To date, most XAI methods offer one-off and static explanations, which cannot cater to the diverse backgrounds and understanding levels of users. With this paper, we investigate if free-form conversations can enhance users’ comprehension of static explanations in image classification, improve acceptance and trust in the explanation methods, and facilitate human-AI collaboration. We conduct a human-subject experiment with 120 participants. Half serve as the experimental group and engage in a conversation with a human expert regarding the static explanations, while the other half are in the control group and read the materials regarding static explanations independently. We measure the participants’ objective and self-reported comprehension, acceptance, and trust of static explanations. Results show that conversations significantly improve participants’ comprehension, acceptance , trust, and collaboration with static explanations, while reading the explanations independently does not have these effects and even decreases users’ acceptance of explanations. Our findings highlight the importance of customized model explanations in the format of free-form conversations and provide insights for the future design of conversational explanations. [ABSTRACT FROM AUTHOR]

AbstractWith the rapid advancement of technology, assistive robotic entities have arisen as indispensable instruments within diverse Human-Robot Interaction (HRI)-based health-care applications. By integrating Artificial Intelligence (AI) into these assistive robotic entities, they gain the capacity to autonomously perceive, engage in sophisticated reasoning, and execute actions within highly dynamic and complex environments. In light of these impressive achievements, this paper highlights a three-stage exploratory overview of the literature on the remarkable advancement of assistive robotics in HRI-based health-care applications. The first stage initiates an assessment of assistive robotics spanning historical epochs from ancient to modern times. Following this, the second stage comprehensively explores assistive robotics investigations in the realm of HRI-based health-care with its four sub-fields including rehabilitation, geriatric-care, pediatric-care, and nursing. Finally, the third stage entails a thorough analysis of the common challenges encountered in these pertinent investigations and provides a set of recommendations. This comprehensive paper not only provides an abundance of studies for each concept, method, and application in HRI, but it also presents their theoretical foundations, strengths, gaps, critical challenges, and recommendations. The results of the conducted exploratory overview shed light on the noteworthy prominence of assistive robotic entities within the HRI-based health-care field. The acquired findings emphasize the positive impact of such entities on human health, affirming their pivotal role in contributing to the advancement and effectiveness of health-care interventions. Furthermore, this paper provides an opportunity for scholars and researchers actively engaged in the pertinent field to obtain comprehensive additional insights, serving as a guiding resource for their academic endeavors. [ABSTRACT FROM AUTHOR]

AbstractThe user experience and usability of Wearable Health Devices (WHD-UXU) play a pivotal role in determining user acceptance. However, there is a scarcity of comprehensive research systematically addressing the current focus and future research directions of WHD-UXU using bibliometric analysis. Therefore, to unveil the latest research progress and the evolutionary trajectory of WHD-UXU research hotspots over the past decade, this study leverages 365 papers related to WHD-UXU gathered from the Web of Science (WOS) database. Employing bibliometric methods and integrating tools like VOSviewer and CiteSpace, a knowledge map is formulated, encompassing aspects such as keyword co-occurrence, references co-citation, and research collaboration networks. The research findings highlight that current WHD-UXU research hotspots predominantly revolve around (1) the study of usability in wearable health devices, (2) research on mobile health of wearable health devices, (3) the study of health monitoring in wearable health devices, (4) research on rehabilitation treatment of wearable health devices, and (5) the study of user acceptance in wearable health devices. Future research is anticipated to focus on areas such as human-computer interaction, affective computing, artificial intelligence, human-machine trust, user self-disclosure, privacy algorithms, and system design evaluation. Finally, the study addresses future research directions and challenges in this field. [ABSTRACT FROM AUTHOR]

Abstract The paper reports on two empirical studies that provide the first examination into how the presentation of an AI teammate’s identity, responsibility, and capability impacts humans’ perception surrounding AI teammate adoption before interacting as teammates. Study 1’s results indicated that AI teammates are accepted when they share equal responsibility on a task with humans, but other perceptions such as job security generally decline the more responsibility AI teammates have. Study 1 also revealed that identifying an AI as a tool instead of a teammate can have small benefits to human perceptions of job security and adoption. Study 2 revealed that the negative impacts of increasing responsibility can be mitigated by presenting AI teammates’ capabilities as being endorsed by coworkers and one’s own past experience. This paper discusses how to use these results to best balance the presentation of AI teammates’ capabilities and responsibilities, as well as identifying AI as teammates. [ABSTRACT FROM AUTHOR]

This document, titled "AI Fairness in Action: A Human-Computer Perspective on AI Fairness in Organizations and Society," discusses the increasing use of artificial intelligence (AI) systems in decision-making processes within organizations and society. The authors highlight the importance of fairness in AI decision-making and the negative consequences of unfair practices. They argue that while technical improvements to AI models are commonly pursued to enhance fairness, there is a need to also consider the subjective nature of fairness perceptions. The document introduces several papers that explore different aspects of AI fairness, including perceptions of fairness, biases in AI system creators, counterfactual thinking in ethical dilemmas, perceptions of advice seekers, socio-algorithmic construction of fairness, and procedural justice in online platforms. The authors conclude that AI fairness is influenced by the interaction between AI and human end-users, and organizations and societies must approach AI adoption and implementation in a way that is perceived as fair. [Extracted from the article]

User eXperience (UX) has been used to achieve improvements in digital information systems based on how people perceive them. In particular, this paper establishes a framework that employs methods for eye and mouse tracking, keyboard input, self-assessment questionnaire and artificial intelligence algorithms to evaluate user experience and categorize users in terms of performance profiles. The results obtained with this framework are artifacts that can be used to support customizations of the User Interface (UI) on the websites. Moreover, the established framework is generic and flexible and can be applied to any information system, such as the case study shown in the website of the Federal Revenue of Brazil (RFB). The main objectives of this paper are as follows: (i) to set out a powerful UX framework based on three tracking techniques – the AIT2-UX; (ii) to provide the T2-UXT to collect, collate, process and visualize data obtained from users' interactions (iii) to use and compare machine learning algorithms with the classification of user performance profiles; (iv) to use the artifacts generated by the framework to manually customize the UI with the website. [ABSTRACT FROM AUTHOR]

Traditional health systems mostly rely on rules created by experts to offer adaptive interventions to patients. However, with recent advances in artificial intelligence (AI) and machine learning (ML) techniques, health-related systems are becoming more sophisticated with higher accuracy in providing more personalized interventions or treatments to individual patients. In this paper, we present an extensive literature review to explore the current trends in ML-based adaptive systems for health and well-being. We conduct a systematic search for articles published between January 2011 and April 2022 and selected 87 articles that met our inclusion criteria for review. The selected articles target 18 health and wellness domains including disease management, assistive healthcare, medical diagnosis, mental health, physical activity, dietary management, health monitoring, substance use, smoking cessation, homeopathy remedy finding, patient privacy, mobile health (mHealth) apps finder, clinician knowledge representation for neonatal emergency care, dental and oral health, medication management, disease surveillance, medical specialty recommendation, and health awareness. Our review focuses on five key areas across the target domains: data collection strategies, model development process, ML techniques utilized, model evaluation techniques, as well as adaptive or personalization strategies for health and wellness interventions. We also identified various technical and methodological challenges including data volume constraints, data quality issues, data diversity or variability issues, infrastructure-related issues, and suitability of interventions which offer directions for future work in this area. Finally, we offer recommendations for tackling these challenges, leveraging on technological advances such as multimodality, Cloud technology, online learning, edge computing, automatic re-calibration, Bluetooth auto-reconnection, feedback pipeline, federated learning, explainable AI, and co-creation of health and wellness interventions. [ABSTRACT FROM AUTHOR]

As AI-enhanced technologies become common in a variety of domains, there is an increasing need to define and examine the trust that users have in such technologies. Given the progress in the development of AI, a correspondingly sophisticated understanding of trust in the technology is required. This paper addresses this need by explaining the role of trust in the intention to use AI technologies. Study 1 examined the role of trust in the use of AI voice assistants based on survey responses from college students. A path analysis confirmed that trust had a significant effect the on intention to use AI, which operated through perceived usefulness and participants' attitude toward voice assistants. In Study 2, using data from a representative sample of the U.S. population, different dimensions of trust were examined using exploratory factor analysis, which yielded two dimensions: human-like trust and functionality trust. The results of the path analyses from Study 1 were replicated in Study 2, confirming the indirect effect of trust and the effects of perceived usefulness, ease of use, and attitude on intention to use. Further, both dimensions of trust shared a similar pattern of effects within the model, with functionality-related trust exhibiting a greater total impact on usage intention than human-like trust. Overall, the role of trust in the acceptance of AI technologies was significant across both studies. This research contributes to the advancement and application of the TAM in AI-related applications and offers a multidimensional measure of trust that can be utilized in the future study of trustworthy AI. [ABSTRACT FROM AUTHOR]

Trust plays an essential role in the interaction between humans and artificial intelligence (AI). To promote trust in AI, information about the AI's performance should be communicated well to the users. Accordingly, this paper investigates how information about AI performance should be presented, focusing on message framing and the ownership of decisions. A 2 (ownership: no ownership vs. ownership) × 3 (message framing: no information vs. negative information vs. positive information) between-subjects experiment was conducted (N = 120). Participants were asked to choose items to help them survive in the desert, supported by an AI decision. The results showed that participants without decision ownership perceived higher trust than those with decision ownership. Also, trust was perceived to be higher when participants were not given performance information than when they were. The results indicate the importance of carefully communicating with AI. The implications of this study are discussed. [ABSTRACT FROM AUTHOR]

Intelligent assistants have proliferated across various settings spurring research and ideas. However, there is a lack of clarity on how they can be defined and identified. This definitional and terminological incoherence manifests in narrow and misinformed understanding of technology and thus, poses threats to theoretical and empirical endeavors in AI. To overcome these challenges, this paper presents a definitional framework that defines intelligent assistants and distinguishes them from other technologies. This framework is informed by tracing the historical evolution of this technology in the past 70 years which saw the rise of three constitutive features in machines: AI, interaction, and assistance. A review of technology's definitions published since 2010 and associated terminologies was also conducted to unpack their conceptual limitations and support the framework. A new definition is then presented which contends that a technology can only be characterized as an intelligent assistant if it is AI-enabled, interactive, and assistive. [ABSTRACT FROM AUTHOR]