Human Collaboration Research Articles

Abstract 4138722: Large Language Model based multi-agent Transcatheter Aortic Valve Implantation team to augment multidisciplinary meetings - proof of concept.

Introduction: Multidisciplinary team (MDT) discussions are integral to Transcatheter Aortic Valve Implantation (TAVI) decision making. Large language model (LLM) ubiquity and low-code no-code platforms have enabled clinician lead solution development. Specialised chatbots or ‘agents’ have evolved into multi-agent systems that can personify human collaboration. We assess the performance of an artificial intelligence (AI) multi-agent TAVI MDT. Methods: Four de-identified TAVI cases from two metropolitan Australian hospitals were assessed by a mock human TAVI MDT (h-MDT) and an AI multi-agent TAVI MDT (ai-MDT). The ai-MDT was created with Agentflow within Flowise AITM and had a hierarchical multi-agent architecture suited to complex reasoning required for TAVI MDT simulation (figure). LLM limitations necessitated the ai-MDT rely on imaging reports rather than clinical images. The h-MDT and ai-MDT consisted of similar team members. Outputs from the h-MDT and ai-MDT was adjudicated by a panel of four blinded TAVI doctors that determined if output was human vs AI and assigned a SMIC score (4-12, 4=good, 12=poor) that assessed structure, missing information, incorrect information and clinical utility. Time durations for h-MDT and ai-MDT were recorded. Results: Adjudicators differentiated human vs. AI output 100% of the time and ai-MDT output had better SMIC scores than h-MDT (U-stat 213, p=0.0011). ai-MDT outperformed h-MDT in the domains of structure, missing information and clinical utility but was not statistically different in the incorrect information domain (U-stat 132, p=0.88). The average time for each case in h-MDT was 15 minutes and 45 seconds compared to 97 seconds for ai-MDT. Conclusion: This demonstrates the potential of using LLM based multi-agent systems as a clinical adjunct in highly specialized multidisciplinary clinical meetings. AI responses were superior for structure, clinical utility and missing information and non-inferior for incorrect information compared to humans, which highlights that hallucinations remain an issue with generative AI. Time was saved but image interpretation still requires human input, for now. Cognitive AI continues to require human supervision for implementation.

Navigating the AI/ML-Driven Future of HRM: Balancing Technological Innovation with Human Collaboration

In the age of rapid technological advancement, the integration of Artificial Intelligence (AI) and Machine Learning (ML) into Human Resource Management (HRM) practices is reshaping the landscape of modern workplaces. This article explores the multifaceted dynamics of this integration, exploring how AI/ML technologies are revolutionizing HRM while emphasizing the critical importance of human collaboration in this evolving paradigm. Through an in-depth analysis of current trends, challenges, and opportunities, this paper elucidates the myriad benefits that AI/ML offer to HRM, ranging from enhanced decision-making to streamlined processes. However, it also underscores the indispensable role of human expertise, empathy, and ethical considerations in harnessing the full potential of AI/ML in HRM. Drawing upon a rich body of existing literature and insightful case studies, this article provides nuanced insights into the future trajectory of HRM, advocating for a balanced approach where AI/ML seamlessly integrates with human capabilities to foster a collaborative environment. Ultimately, this exploration serves as a guiding beacon for organizations navigating the AI/ML-driven future of HRM, emphasizing the imperative of aligning technological innovation with human-centric values to achieve organizational success and employee well-being in the digital age. Keywords: AI, ML, Human Resource Management, Collaboration, Technology, Future of Work

Challenges of Preparing Students for International Entrepreneurship in the era of Artificial Intelligence

International entrepreneurship competencies in the era of rapidly developing artificial intelligence focus more than earlier on stepping out of one’s comfort zone, asking the right questions, assessing the reliability of information sources, and developing cross-cultural teamwork readiness. The purpose of this research is to prepare students for collaborative learning situations where generative artificial intelligence tools have become widely available. The main research question is: What are the challenges in different types of entrepreneurial team-based action learning projects in the context of artificial intelligence? Learning concepts based on collaborative learning and improving the international networking readiness of students are presented. Practical implications of time zone differences, trust building and conflict management in international teams are analysed based on the experience of X-Culture projects. We compare the opportunities and challenges of applying generative artificial intelligence such as ChatGPT-4, Microsoft Co-pilot and Google Gemini Advanced when developing learners’ own international business ideas to learning from international teamwork to assist foreign start-ups in entering new markets. Gaps in skills to apply human collaboration for critically assessing artificial intelligence summary answers and identifying possible hallucinations when comparing new potential foreign markets were revealed. Artificial intelligence applications can be used to form global student teams based on the similarity of entrepreneurial self-development visions of team members. Still, experimenting with prompts in these applications can also enhance readiness to compare the pros and cons of different international entrepreneurship concepts and intelligent questioning skills for coaching and consulting. Students can chat with AI in a role-playing manner that helps them understand how to manage cultural differences in international entrepreneurship teams. Online action learning driven by tasks of start-ups is an essential tool in the entrepreneurial learning approach that prepares students to understand the value and limitations of generative artificial intelligence tools for international entrepreneurship. This paper contributes to understanding the challenges in action learning of students for international entrepreneurship and ways to overcome these challenges in the era when generative artificial intelligence tools are changing learning and international entrepreneurship practices.

The impact of leadership behaviors on organizational innovative performance and learning in AI-driven Industry 5.0 environments

Purpose Our aim was to elucidate how leaders’ behaviors may impact innovation and organizational learning in a fast-changing, human-centric and sustainability responsive AI-driven Industry 5.0 environment. Design/methodology/approach An unsystematic narrative review of relevant literature was conducted focusing on the influence of leadership behaviors on innovation and learning in Industry 5.0 environment. Findings We found that leadership behaviors that align with Industry 5.0 demands and values must emphasize collaboration, empathy, and continuous learning. The translation of leaders’ actions into desired outcomes requires a psychologically safe work environment, ensuing team cohesion, empowering team members, promoting a learning culture, engendering trust, and vision and goal alignment. Research limitations/implications Being aware of leadership qualities required in Industry 5.0 environment, characterized by machine–human collaboration, sustainable innovations, and continuous learning, enables organizations to focus their recruitment efforts on leaders’ characteristics that align with this environment. It also helps them design suitable leaders’ training and development programs. This study requires further expansion and empirical testing to validate the proposed model. Originality/value There is a plethora of studies on leadership in various contexts; however, there is very little research on the type of leadership that maybe effective in the fast changing and AI-driven Industry 5.0 environment. The findings of this paper shed light on such a leadership.

Fragmentology: A Geniza Manifesto

Abstract: By using the metaphor of ceramic sherds as manuscript fragments, this article proposes a fresh look at the way we think about geniza fragments. Starting with a sketch of the history of the various Cairo Geniza databases, the author discusses the problems of rendering dating in digital terms. Then, the essay turns to the dispersal of manuscript collections and the problem of partage—dividing archaeological finds among the institutions involved in excavating them—in the context of current museum collections. The author uses this overview as a springboard to discuss how machine learning can help to join fragments but at the same time introduces inherent bias. The essay concludes by highlighting the paradox that fragmentation enables digital de-siloing and human collaboration.

Online dual robot–human collaboration trajectory generation by convex optimization

For dynamic collision-free trajectory planning in dual-robot and human collaborative tasks, this paper develops an online dual-robot Mutual Collision Avoidance (MCA) scheme based on convex optimization. A novel convex optimization formulation model, named Disciplined Convex programming by Shifting reference paths (DCS), is proposed for solving the single-robot trajectory optimization problem. Furthermore, a new dual-robot trajectory convex optimization algorithm is presented for online adjustment of the dual-robot trajectories according to the collaborative task priority. The overall pipeline, named DCS-MCA, generates collision-free and time-optimal dual-robot trajectories, while prioritizing the task accessibility of the high-priority robot. Simulation experiments demonstrate that DCS exhibits comparable performance to the current state-of-the-art single-robot motion planner, while the DCS-MCA outperforms common algorithms by up to 30% in time optimality for dual-robot collaborative tasks. The feasibility and dynamic performance of the proposed approach are further validated in a real collaborative cell, illustrating its suitability for collaborative dual-robot tasks in moderately dynamic environments.

AI and Human Collaboration: The Future of HR in the Fourth Industrial Revolution

This study investigates the interplay of Adoption of Advanced HR Technologies, Workforce Management Effectiveness, Employee Engagement and Productivity, and Employee Performance within PT Danareksa (Persero), a prominent state-owned enterprise in Indonesia's financial services sector. Using a quantitative approach and Structural Equation Modeling (SEM) with Smart PLS, data from 80 employees were analyzed to explore the direct and indirect relationships among these variables. The findings reveal significant direct effects of Adoption of Advanced HR Technologies and Workforce Management Effectiveness on Employee Engagement and Productivity, highlighting their role in enhancing employee engagement and productivity levels. Moreover, both Adoption of Advanced HR Technologies and Workforce Management Effectiveness indirectly influence Employee Performance through their impacts on Employee Engagement and Productivity. These results underscore the critical importance of strategic investments in HR technologies and effective workforce management practices in optimizing organizational performance and competitiveness. This research contributes to understanding how HR innovations and management practices can foster a conducive work environment that drives employee performance and organizational success in the financial services industry.

Enhancing anomaly detection: A comprehensive approach with MTBO feature selection and TVETBO[sbnd]Optimized Quad-LSTM classification

The rapid growth of computer networks has heightened system vulnerabilities, challenging traditional Machine Learning (ML) techniques in network anomaly detection due to the abundance of data, imbalanced attack classes, and large-scale datasets. This paper introduces a novel approach leveraging the AWID3 dataset, utilizing advanced preprocessing techniques such as data splitting and normalization. We propose the Mountaineering Team-Based Optimization (MTBO) algorithm for robust feature selection, inspired by social behavior and human collaboration. Additionally, we deploy a Quad-LSTM framework, integrating ConvLSTM, CNN-LSTM, E3D-LSTM, and SAConvLSTM models, fine-tuned by the Technical and Vocational Education and Training-Based Optimizer (TVETBO). Our approach achieves a remarkable 99.91 % accuracy, significantly surpassing existing models, thus enhancing network security by effectively addressing the complexities of anomaly detection.

A Conception of Collaborative Decision Support Systems: Approach and Platform Architecture

The paper describes a general conception of collaborative decision support systems, in which teams providing decision support a) are formed flexibly in accordance with the problem and b) consist of both human experts and intelligent agents implementing AI methods and techniques. An analysis of the key problems of creating collaborative decision support systems based on the collaboration of humans and AI is carried out, the following problems are highlighted: ensuring interoperability (mutual understanding) between heterogeneous team members, reconciling differing positions of participants, ensuring trust between participants, ensuring the effectiveness of joint actions planning and maintaining a balance between predefined workflows and self-organization. Principles for constructing such systems have been formed, offering solutions to the identified problems. In particular, it is proposed to employ an ontology-oriented representation of information about the problem (in the form of multi-aspect ontology), a set of methods for monitoring team activities, reputation scheme, elements of explainable AI, as well as mechanisms of limited self-organization. The proposed concept forms the basis of a software platform for the development of collaborative decision support systems, the main architectural provisions of which are also presented in the paper. The use of the platform is illustrated by an example from the field of rational management of road infrastructure and the creation of a collaborative DSS for the development of measures to reduce road accidents.

AI-deation: When the Teacher is a Transformer in Role-Playing to create Privacy Decision Serious Games

Understanding the synergistic potential of human collaboration with artificial intelligence (AI) in creative endeavours, such as ideating Serious Game (SG) concepts, is of vital interest in our era of digital transformation. This study probes two pivotal questions: First, how does the incorporation of a GPT-4 transformer AI, assuming the role of a teacher, influence support for student teams during the ideation and balancing of SG concepts? Second, what are the students' perceptions of AI integration when co-designing these concepts with an AI in the educator role? In a between-group research design, two distinct groups engaged in a collaborative role-playing activity with digital role-specific cards and a visualised board to ideate and balance Serious Games addressing privacy decision-making. The first group, engaging in a local setting, collaborated with an AI that played the teacher role. In contrast, the second group played the co-ideation activity in a remote setting, with a human playing the role of the teacher. The findings indicate that generative AI can successfully be sourced to play the teacher role in a collaborative role-playing activity. Crucially, the timing of AI intervention thereby emerged as an essential factor that can impair creative support. Scheduled AI interventions can offer fresh insights but may not align with immediate team needs. The insights underscore the requirement to determine the most effective timing for AI intervention in human-AI co-playing ideation sessions to foster the full potential of an AI filling a role in a collaborative design process. Implications synthesised from the analytical findings and practical insights on AI-suggested design propositions/conflicts are discussed conclusively.

Designing information systems for business administration through human and computer interaction

AI is increasingly incorporated into business operations; it appears in every aspect of life. However, a strategy that can integrate human and machine interaction is required for long-term implementation. To identify characteristics that can enhance domain operations and interpersonal interactions. To elucidate these obstacles and underscore specific pivotal decisional considerations that necessitate resolution before the effective collaboration of cognitive machines and humans in delivering authentic financial services. This article utilizes the published framework to analyze a case study in retail banking to identify the necessary cognitive abilities, individually and collectively. Each of these capabilities provides usage examples and demonstrates how they comprise a unified deliberative architecture for human-robot interaction. Customer service is an area where this design could be advantageous. Experimental evidence indicates that explicit knowledge management at the geometric and symbolic levels facilitates the incorporation of human-level semantics into the deliberative system of the robot, thereby enhancing the quality and authenticity of human-robot interactions.

Industry 5.0: Transforming ship design through human-centered approach

Industry 5.0 heralds a paradigm shift by reinstating the significance of human centricity alongside technology. Incorporating human collaboration into the design methodology aligns with general project management methodology and addresses the imperative of facilitating sustainable goals in the industry. Focusing on human skills and aspirations offers a viable path to expedite the adoption of new technology into the mainstream, aligning with the evolving needs of the shipbuilding industry and green targets of society development. The article delves into the implications of the Shipbuilding 5.0 paradigm to design process and methodology, the potential changes it brings, and the potential benefits it can offer to the evolution of the shipbuilding industry.

Communicating Unnamable Risks: Aligning Open World Situation Models Using Strategies from Creative Writing

How can a machine warn its human collaborator about an unexpected risk if the machine does not possess the explicit language required to name it? This research transfers techniques from creative writing into a conversational format that could enable a machine to convey a novel, open-world threat. Professional writers specialize in communicating unexpected conditions with inadequate language, using overlapping contextual and analogical inferences to adjust a reader’s situation model. This paper explores how a similar approach could be used in conversation by a machine to adapt its human collaborator’s situation model to include unexpected information. This method is necessarily bi-directional, as the process of refining unexpected meaning requires each side to check in with each other and incrementally adjust. A proposed method and example is presented, set five years hence, to envisage a new kind of capability in human-machine interaction. A near-term goal is to develop foundations for autonomous communication that can adapt across heterogeneous contexts, especially when a trusted outcome is critical. A larger goal is to make visible the level of communication above explicit communication, where language is collaboratively adapted.

Empowering Women Employees’ Safety through Artificial Intelligence – An Empirical Study

Now a day it is very common human collaboration with machine. Artificial Intelligence, Internet of Things and Business Analytics are the important terms in technology. Top business companies through the world have started concentrating on Artificial Intelligence and machines. In present era, machines have started to act like human. Artificial intelligence has been adopted in various sectors since this technology, improves productivity, decreases costs, and quickly resolves challenging issues. It has been predicted that one of the most promising technologies of the future will be artificial intelligence. As one of the latest modern technologies, AI is influencing developments throughout a number of industries and societal safety norms. In this paper has an attempt to know women employee safety level through Artificial Intelligence and to identify the factors which are influencing for women empowerment safety through AI. An online survey was conducted and circulated through emails to collected the data, to study the status and level of empowerment among women safety through Artificial Intelligence. The status of women empowerment was evaluated using questionnaire. The information like age, educational qualification, occupation, marital status, work experience, city of residence was collected and analysed with five-point Likert scale. The questionnaire was administrated and circulated on a sample of 120 women associated with various email groups. The questionnaire was response rate was 42%, as 50 women respondents retuned fully filled, usable questionnaire. Data collected for this study were evaluated using Multiple Regression and ANOVA in SPSS statistical package. Empowering women employees’ safety through AI involved a combination of technology, regulations, policies and a supportive working environment. It is very essential to implement AI systems that respect privacy and maintain ethical standards while enhancing safety

Enhancing human–robot collaborative transportation through obstacle-aware vibrotactile warning and virtual fixtures

Transporting large and heavy objects can benefit from Human–Robot Collaboration (HRC), increasing the contribution of robots to our daily tasks and addressing challenges arising from labor shortages. This strategy typically positions the human collaborator as the leader, with the robot assuming the follower role. However, when transporting large objects, the operator’s situational awareness can be compromised as the objects may occlude different parts of the environment, weakening the human leader’s decision-making capacity and leading to failure due to collision. This paper proposes a situational awareness framework for collaborative transportation to face this challenge. The framework integrates a multi-modal haptic-based Obstacle Feedback Module with two units. The first unit consists of a warning module that alerts the operator through a haptic belt with four vibrotactile devices that provide feedback about the location and proximity of the obstacles. The second unit implements virtual fixtures as hard constraints for mobility. The warning feedback and the virtual fixtures act online based on the information given by two Lidars mounted on a mobile manipulator to detect the obstacles in the surroundings. By enhancing the operator’s awareness of the environment, the proposed module improves the safety of the human–robot team in collaborative transportation scenarios by preventing collisions. Experiments with 16 non-expert subjects in four feedback modalities during four scenarios report an objective evaluation thanks to quantitative metrics and subjective evaluations based on user-level experiences. The results reveal the strengths and weaknesses of the implemented feedback modalities while providing solid evidence of the increased situational awareness of the operator when the two haptic units are employed.

A Research Study on How AI Creates Fiction Stories

Abstract: This paper includes the study of complex process by which artificial intelligence systems create fictional stories. In this process we cover the five processes such as The pre -processing steps for using AI to create a fictional story, Plot structure and plot production process for creating fictional stories using artificial intelligence, Character growth in the process of creating a virtual story using artificial intelligence, Generating dialogue in the process of creating a virtual story using artificial intelligence, and Using AI to revisit and iterate the process of creating fictional stories. Using state-of-the-art artificial intelligence models, especially those based on deep learning architectures such as recurrent neural networks (RNNs) and transformer models such as generative pre- trained transformers (GPTs), we analyse how these systems understand and manipulate language to create consistent fictional pieces. This paper This Make up story through empirical experiments and qualitative analysis, we carefully study the impact of different parameters, including Algorithm, Education, Fine-tuning Hint, Generation, and Evaluation methods, on the quality and diversity of the generated stories. The study also explores the role of artificial intelligence as well as human collaboration in the fiction stories writing and creative process. It discuss the Future Directions of AI’S role in creating fiction stories.

Characterising CSCW Research on Human-Robot Collaboration

Human-Robot Collaboration (HRC) is an increasingly prominent topic in CSCW. From telepresence systems through field robots for extreme missions to social robots in homes, HRC is becoming a recurring theme across a broad range of CSCW research. Based on the growing interest, we explore the coupling of the field of CSCW and research on robotics at this critical time. This paper presents a primary and secondary literature review of CSCW proceedings and venues for robotics research. We identified 29 CSCW papers that contribute to the body of HRC research and analyse the foundations on which these papers rely. Then, we identified 138 papers published in robotics outlets that either (1) cite one or more CSCW papers, or (2) use the term CSCW. We discuss how CSCW is currently contributing to HRC research through five research topics: Sociomateriality, teamwork, awareness, embodiment, and communication. The paper contributes by offering three key future pathways for expanding this area of research, outlining questions and gaps that have yet to be explored for (1) expanding the application domains and diversifying robot types for HRC, (2) methodological implications for HRC, and (3) learning from human collaboration.

Comparing Student and Writing Instructor Perceptions of Academic Dishonesty When Collaborators Are Artificial Intelligence or Human

It remains unclear if perceptions of academic dishonesty concerning artificial intelligence writing technologies (AIWTs) present new challenges or if they reflect prior, non-AI concerns. To structure this problem, we used a randomized control survey experiment. We compared student ( n = 603) and instructor ( n = 312) attitudes toward dishonesty in collaborations involving humans versus AIWT in 10 writing-related scenarios. Results suggest similar perception patterns among students and instructors, with both populations expressing significant differences in perceived dishonesty between AI and human collaborators in some scenarios. This experiment structures the problem of AI writing and academic dishonesty for future research in this emerging field.

A Sustainable Collaborative Talent Management Through Collaborative Intelligence Mindset Theory: A Systematic Review

As society evolves, moving forward into the realms of Industry 5.0, it is imperative to develop a robust collaborative talent management model that can endure external crises such as the recent Covid-19 pandemic. Despite the antiquity of collaborative practices—traceable back to early human societies—both practitioners and academics have frequently focused primarily on exploring the catalysts for collaboration, often neglecting to provide a holistic conceptualization of effective human collaboration. In alignment with the Sustainable Development Goals, this paper endeavors to critically review and delineate the evolution of talent management and collaborative intelligence literature. Employing a systematic review methodology, this study aims to formulate a novel framework for collaborative talent management, drawing upon the principles of a collaborative intelligence mindset, and to suggest avenues for future research in this domain. The findings propose the employment of collaborative intelligence through a mindset perspective as a means to bridge the literature gaps identified in areas such as interdisciplinary and cross-sector collaboration, digitalization and artificial intelligence, collaborative leadership styles, and performance measurement within the field.

Robotics in Manufacturing: A Review of Advances in Automation and Workforce Implications

The integration of robotics in manufacturing processes has undergone significant advancements, reshaping the landscape of industrial production and introducing transformative changes. This review explores the latest developments in robotics within the manufacturing sector, shedding light on the technological breakthroughs and their implications on the workforce. The recent surge in robotics adoption in manufacturing has been driven by the pursuit of increased efficiency, precision, and cost-effectiveness. From traditional robotic arms to cutting-edge collaborative robots (cobots) that work alongside human operators, the manufacturing industry has witnessed a paradigm shift. Advanced robotic systems equipped with artificial intelligence (AI) and machine learning algorithms enable adaptive and autonomous decision-making, further enhancing their capabilities. Automation in manufacturing, fueled by robotics, has yielded numerous benefits such as improved product quality, reduced production cycle times, and increased overall productivity. The review delves into case studies and real-world applications where robotics have proven instrumental in optimizing manufacturing processes across diverse industries. However, as the manufacturing landscape evolves, the implications for the workforce cannot be overlooked. The integration of robotics poses challenges and opportunities for human workers. While automation has the potential to eliminate routine and hazardous tasks, it also necessitates upskilling and reskilling of the workforce to operate, program, and maintain robotic systems. The review examines the socio-economic impacts of increased automation, discussing potential shifts in employment patterns and the need for a strategic approach to workforce development. This review provides a comprehensive analysis of the advances in robotics within manufacturing and their profound implications on the workforce. Striking a balance between automation and human collaboration is crucial for the future of manufacturing, emphasizing the importance of proactive strategies to harness the benefits of robotics while ensuring a resilient and adaptable workforce.