Explore the vast range of titles available.

This research examines whether a machine, specifically artificial intelligence (AI), can be creative by comparing design solutions for a practical competition – a light fixture for a pediatric waiting room – among AI, collaboration efforts and a human designer. Amazon Mechanical Turk and Prolific workers observed the design solutions throughout the design process, from sketches ( $ S $ ) to three-dimensional renderings ( $ 3D $ ) to fully developed models in virtual waiting rooms ( $ VR $ ). Using the well-established Creative Product Semantic Scale (CPSS), the workers rated each design solution in three distinctive stages – $ S $ , $ 3D $ and $ VR $ – on three criteria – novelty (freshness or newness), resolution (relevance and logic) and style (craftsmanship and desirability). Despite some demographic discrepancies, the workers expressed general senses of happiness and calmness, resonating with the competition’s requirements. Statistical results of CPSS ratings revealed that while AI excelled in style for $ 3D $ , the human designer outperformed in novelty for both $ S $ and $ VR $ . Collaboration efforts surprisingly finished last. Such findings challenge current assumptions of AI’s creative ability in design research and highlight the need to be agile in the age of disruptive technologies. This research also offers guidance for product and interior designers and educators on thoughtfully integrating AI into the design process.

As Generative AI (GenAI) becomes increasingly integrated into creative work, creative professionals face an emerging dilemma: while GenAI offers powerful capabilities through prompt-based interactions, its automated processes also introduce uncertainties and risks. Drawing on semi-structured interviews with 18 creative professionals, this study examines their experiences and perceptions of integrating GenAI into creative practice. Findings revealed that GenAI's perceived adaptability, creativity, and scalability enhanced the sense of agency in the creative process. Conversely, limitations in transparency, customizability, and feedback mechanisms undermined human agency. These perceptions were influenced by users' conceptualization of GenAI, as either a simple tool or a collaborative partner, and by the folk theories they constructed through iterative communications with the technology. This research advances our understanding of human-machine collaboration in creative fields and provides design implications for developing GenAI tools that more effectively support creative professionals.

Online customer feedback management (CFM) is becoming increasingly important for businesses. Providing timely and effective responses to guest reviews can be challenging, especially as the volume of reviews grows. This paper explores the response process and the potential for artificial intelligence (AI) augmentation in response formulation. We propose an orchestration concept for human–AI collaboration in co-writing within the hospitality industry, supported by a novel NLP-based solution that combines the strengths of both human and AI. Although complete automation of the response process remains out of reach, our findings offer practical implications for improving response speed and quality through human–AI collaboration. Additionally, we formulate policy recommendations for businesses and regulators in CFM. Our study provides transferable design knowledge for developing future CFM products.

We aimed to develop and evaluate Explainable Artificial Intelligence (XAI) for fetal ultrasound using actionable concepts as feedback to end-users, using a prospective cross-center, multi-level approach. We developed, implemented, and tested a deep-learning model for fetal growth scans using both retrospective and prospective data. We used a modified Progressive Concept Bottleneck Model with pre-established clinical concepts as explanations (feedback on image optimization and presence of anatomical landmarks) as well as segmentations (outlining anatomical landmarks). The model was evaluated prospectively by assessing the following: the model’s ability to assess standard plane quality, the correctness of explanations, the clinical usefulness of explanations, and the model’s ability to discriminate between different levels of expertise among clinicians. We used 9352 annotated images for model development and 100 videos for prospective evaluation. Overall classification accuracy was 96.3%. The model’s performance in assessing standard plane quality was on par with that of clinicians. Agreement between model segmentations and explanations provided by expert clinicians was found in 83.3% and 74.2% of cases, respectively. A panel of clinicians evaluated segmentations as useful in 72.4% of cases and explanations as useful in 75.0% of cases. Finally, the model reliably discriminated between the performances of clinicians with different levels of experience (p- values < 0.01 for all measures) Our study has successfully developed an Explainable AI model for real-time feedback to clinicians performing fetal growth scans. This work contributes to the existing literature by addressing the gap in the clinical validation of Explainable AI models within fetal medicine, emphasizing the importance of multi-level, cross-institutional, and prospective evaluation with clinician end-users. The prospective clinical validation uncovered challenges and opportunities that could not have been anticipated if we had only focused on retrospective development and validation, such as leveraging AI to gauge operator competence in fetal ultrasound.

Reinforcement Learning (RL) provides effective results with an agent learning from a stand-alone reward function. However, it presents unique challenges with large amounts of environment states and action spaces, as well as in the determination of rewards. Imitation Learning (IL) offers a promising solution for those challenges using a teacher. In IL, the learning process can take advantage of human-sourced assistance and/or control over the agent and environment. A human teacher and an agent learner are considered in this study. The teacher takes part in the agent’s training towards dealing with the environment, tackling a specific objective, and achieving a predefined goal. This paper proposes a novel approach combining IL with different types of RL methods, namely, state-action-reward-state-action (SARSA) and Asynchronous Advantage Actor–Critic Agents (A3C), to overcome the problems of both stand-alone systems. How to effectively leverage the teacher’s feedback—be it direct binary or indirect detailed—for the agent learner to learn sequential decision-making policies is addressed. The results of this study on various OpenAI-Gym environments show that this algorithmic method can be incorporated with different combinations, and significantly decreases both human endeavors and tedious exploration process.

Objective This study aims to advance the On Scene Injury Severity Prediction (OSISP), an Artificial Intelligence (AI)-based model, as a Clinical Decision Support System (CDSS) that supports Emergency Medical Service (EMS) personnel during on-scene assessment of adult trauma patients. The objectives are to explore the integration of OSISP with the prehospital trauma workflow and to refine the User Interface (UI) that communicates the predictions. Methods Workflow integration was studied in a workshop by analysis of a customer journey map created by personnel with experience of working in the EMS setting (n = 8). Literature reviews were conducted to identify key factors enabling efficient human–AI collaboration and implementation options. Identified UI components derived from workshop and literature review findings were then evaluated and selected to refine the OSISP UI. Results The workshop derived that OSISP is a service to be used on portable IT platforms as a second opinion, support for prioritization, and support during patient assessment. The literature reviews identified key content, characteristics, and goals of communicating predictions to users. The refined UI consisted of eight information components (prediction, entered predictors, missing predictors, and model details), and four functions (notification, exploration mode, and filtering of top three entered and missing predictors), to communicate the OSISP prediction. Conclusions The refined OSISP UI has potential to integrate well into the clinical workflow during patient assessment, as well as enhance human–AI collaboration through customizable information when communicating predictions. However, usability testing of the OSISP UI is needed to ensure clinical utility.

Artificial intelligence (AI) can transform health care practices with its increasing ability to translate the uncertainty and complexity in data into actionable—though imperfect—clinical decisions or suggestions. In the evolving relationship between humans and AI, trust is the one mechanism that shapes clinicians’ use and adoption of AI. Trust is a psychological mechanism to deal with the uncertainty between what is known and unknown. Several research studies have highlighted the need for improving AI-based systems and enhancing their capabilities to help clinicians. However, assessing the magnitude and impact of human trust on AI technology demands substantial attention. Will a clinician trust an AI-based system? What are the factors that influence human trust in AI? Can trust in AI be optimized to improve decision-making processes? In this paper, we focus on clinicians as the primary users of AI systems in health care and present factors shaping trust between clinicians and AI. We highlight critical challenges related to trust that should be considered during the development of any AI system for clinical use.

PurposeArtificial intelligence (AI) has gained significant momentum in recent years. Among AI-infused systems, one prominent application is context-aware systems. Although the fusion of AI and context awareness has given birth to personalized and timely AI-powered context-aware systems, several challenges still remain. Given the “black box” nature of AI, the authors propose that human–AI collaboration is essential for AI-powered context-aware services to eliminate uncertainty and evolve. To this end, this study aims to advance a research agenda for facilitators and outcomes of human–AI collaboration in AI-powered context-aware services.Design/methodology/approachSynthesizing the extant literature on AI and context awareness, the authors advance a theoretical framework that not only differentiates among the three phases of AI-powered context-aware services (i.e. context acquisition, context interpretation and context application) but also outlines plausible research directions for each stage.FindingsThe authors delve into the role of human–AI collaboration and derive future research questions from two directions, namely, the effects of AI-powered context-aware services design on human–AI collaboration and the impact of human–AI collaboration.Originality/valueThis study contributes to the extant literature by identifying knowledge gaps in human–AI collaboration for AI-powered context-aware services and putting forth research directions accordingly. In turn, their proposed framework yields actionable guidance for AI-powered context-aware service designers and practitioners.

The field of artificial intelligence (AI) is advancing quickly, and systems can increasingly perform a multitude of tasks that previously required human intelligence. Information systems can facilitate collaboration between humans and AI systems such that their individual capabilities complement each other. However, there is a lack of consolidated design guidelines for information systems facilitating the collaboration between humans and AI systems. This work examines how agent transparency affects trust and task outcomes in the context of human-AI collaboration. Drawing on the 3-Gap framework, we study agent transparency as a means to reduce the information asymmetry between humans and the AI. Following the Design Science Research paradigm, we formulate testable propositions, derive design requirements, and synthesize design principles. We instantiate two design principles as design features of an information system utilized in the hospitality industry. Further, we conduct two case studies to evaluate the effects of agent transparency: We find that trust increases when the AI system provides information on its reasoning, while trust decreases when the AI system provides information on sources of uncertainty. Additionally, we observe that agent transparency improves task outcomes as it enhances the accuracy of judgemental forecast adjustments.

[Purpose/Significance] Through interdisciplinary comparison, the core connotation, common core and field differentiation of the Agent concept are revealed, the Agent-related concepts and theories contained in library science are revealed, and the innovative value of AI Agent driven by large language models to the core services of libraries is analyzed to promote the transformation of knowledge services to intelligent and collaborative paradigms. Understanding the interdisciplinary nature of Agents will help library science, information science and other related disciplines to better design and apply AI technologies and achieve the core mission of connecting humans and knowledge in a more efficient, accurate and humane way. It will also enable library science to more accurately integrate the essence of the six major disciplines and transform the traditional three-subject relationship of readers, librarians and systems into a new collaborative paradigm. [Method/Process] Using interdisciplinary literature analysis, the definition, theoretical evolution and application characteristics of Agent in six major disciplines of philosophy, economics, law, biology, sociology and computer science are sorted out, the concepts and theories related to Agent contained in library science are explored, and the commonalities and differentiation of Agent in the five-dimensional characteristics of autonomy, perception, purpose, adaptability and interactivity of each discipline are compared. The theoretical essence of the six major disciplines is mapped to the three-dimensional subject in the practical field of library science, and the Agent role coordination mechanism of readers, librarians and systems is analyzed. Readers are entities with intentions and autonomous consciousness, and they will actively initiate information search behaviors based on information needs such as learning knowledge and solving problems and will also adjust their strategies according to environmental changes such as technical tools and social culture, reflecting agent-like adaptability. Librarians serve as service intermediaries and information gatekeepers, connecting readers with resources through technical services such as classification and cataloging, and helping users clarify their information needs and improve their information literacy through reader services such as reference consultation. The library's information systems will also simulate human agent capabilities through algorithms or technologies. Automated search engines or crawlers will collect data according to preset rules, and personalized information recommendations will be made based on user behavior, driving the library's management and services towards automation and intelligence. [Results/Conclusions] The commonality of interdisciplinary agents revolves around the realization of goals by autonomous actors in the environment. The five-dimensional characteristics constitute an interdisciplinary consensus, and the differences are due to the core issues of the disciplines. The essence of a library is a multi-agent system. The reader agent integrates philosophical intentionality and economic game strategy, reflecting demand-driven adaptive retrieval. The librarian agent inherits the legal agency rights and responsibilities and the sociological structural initiative, becoming a professional intermediary between resources and users. The system agent draws on the biological evolutionary adaptation and computer perception closed loop, and advances to an intelligent base for autonomous optimization. AI Agent is a technical enhancement of the inherent agent characteristics of library science. It realizes automation, personalization and intelligent service upgrades through large language models, realizes intention understanding, tool calling, and multi-agent collaboration, and drives the three-element subject from passive response to active collaboration. The three-element agent framework for library science is created, which clarifies the collaborative agent roles of readers, librarians, and systems, and reveals the deep logic of AI Agent driven by large language models and library science. An interdisciplinary comparative study of the Agent concepts reveals that its essence is a practical vehicle for achieving autonomous decision-making in a specific environment. Philosophy gives it depth of consciousness, economics models strategic games, law defines the boundaries of rights and responsibilities, biology reveals evolutionary logic, sociology anchors structural interactions, and computer science ultimately achieves a closed-loop technology. Library science constructs a ternary collaborative intelligent ecosystem that transforms abstract autonomy into a practical paradigm of knowledge connection through the dynamic collaboration of readers, librarians, and systems.