Human-machine Collaboration Research Articles

The Future of Artificial Intelligence: Trends and Predictions

Artificial Intelligence (AI) has evolved rapidly, transforming diverse industries and societal functions. This paper provides a comprehensive overview of AI's current landscape, examining its advancements, applications, and ethical challenges. Key trends are explored, including innovations in machine learning and deep learning, AI’s expanding role across industries, and its potential for addressing climate change and sustainability. Furthermore, the paper highlights AI's role in enhancing human-machine collaboration, paving the way for systems that augment rather than replace human capabilities. Predictions for AI’s future are discussed, such as the emergence of artificial general intelligence (AGI), advancements in autonomous systems, the impact of quantum computing on AI, and innovations in AI-specific hardware. The paper also examines ethical and societal challenges, such as privacy, algorithmic bias, and the need for global governance, addressing the urgent call for responsible AI. In light of these trends, the paper emphasizes future research directions, encouraging interdisciplinary collaboration and a focus on explainable, robust, and resilient AI models. This work aims to shed light on the transformative potential of AI while advocating for ethical practices to ensure a positive and sustainable impact on society.

On-body sensing technologies and signal processing techniques in addressing safety of human machine collaboration

AbstractSafety is a challenge in human machine collaboration despite of the advantages in achieving efficiency, cost reduction and productivity in a collaborative scenario between human and machine/robot. During collaboration with machines, the user might not be able to follow the collaborative tasks as expected due to the cognitive burden causing potential safety concerns such as collision. Addressing this challenge, the aim of this paper is to explore the potential of on-body sensing systems in study of user experience and the psychological condition during the collaboration between machines and human. As the psychological condition is reflected in physiological signals, sensing technologies and signal processing techniques to extract features from physiological signals are explored with applicability in human machine collaboration scenarios. An experiment is designed utilising an industrial collaborative robot arm while quantitative and qualitative data is gathered for this purpose exploring the problem to study user experience and impact of mental strain and cognitive workload on user performance and experience during human machine collaboration. Results show that an adaptive machine to user experience measured by on-body sensing systems during the collaboration has the potential to address safety in human machine collaboration while improving performance and user experience.

RISH Assistance: Voice Activated Digital Assistant for Developers

The increasing use of computing devices brings convenience but also challenges. Traditional interfaces can lead to repetitive strain, slow interactions, and accessibility problemsfor software developers, who often find it hard to turn complex ideas into code, especially with unfamiliar APIs. In response, there is a growing interest in automation and smart machines that act like humans. However, a major obstacle remains—the gap in human-machine interaction. Introducing “RISH Assistance,” a voice-activated digital assis- tant designed for developers. RISH enables users to navigate tasks more efficiently, reducing reliance on screens and tra- ditional input methods. Utilizing advanced voice recognition and Natural Language Processing (NLP), RISH transforms the developer experience by providing easy access to resources, seamless code suggestions, and natural troubleshooting—all through voice commands. Additionally, it can manage phone functions, allowing calls and messages directly from the desktop interface, enhancing productivity. Security is ensured with face authentication, and current session chat history tracking allowsusers to revisit previous interactions. RISH boosts productivity through hands-free interaction, en- abling developers to focus on creative problem-solving. It im- proves accessibility by offering an alternative input method and streamlines workflows, reducing cognitive load while fostering a more intuitive development environment. By facilitating natural and efficient engagement with machines, RISH Assistance aims to enhance human-machine collaboration and create a more inclusive software development landscape.

Technological State and Optimization Analysis of High-Rise Glass Curtain Wall Cleaning Robots

Abstract. With the proliferation of high-rise buildings in urban areas, glass curtain walls are widely used due to their excellent light transmittance and modern appearance. However, these glass curtain walls are prone to accumulating dust, rain stains, and other dirt during use, affecting their aesthetics and transparency, thus requiring regular cleaning. Traditional manual cleaning methods are inefficient and pose significant safety risks. Consequently, high-rise glass curtain wall cleaning robot technology has become a research hotspot. This paper reviews the current research status of high-rise glass curtain wall cleaning robots, which provides a detailed analysis of four main adhesion methods (magnetic adhesion, thrust adhesion, vacuum negative pressure adhesion, and bionic adhesion), four main motion modes (legged, wheeled, tracked, and external assistance), and two main cleaning methods (physical and chemical). For path planning, this paper explores the application status and challenges of regular scanning, random walking, sensor feedback dynamic planning, and bionic path planning. Through the evaluation of the advantages and disadvantages of existing technologies, this paper identifies the main issues faced by each technology and proposes optimization strategies for adhesion technology, motion modes, cleaning efficiency, and path planning. Future directions include improvements in intelligence and automation, multifunctional integration, energy efficiency enhancement, and human-machine collaboration. This paper aims to provide a comprehensive technological review and constructive suggestions to advance high-rise glass curtain wall cleaning robot technology.

Human-machine collaborative inspection of transmission corridors based on the BeiDou positioning

Abstract Inadequate inspection performance of transmission lines leads to damages, posing threats to the safety of power grids. To enhance the efficiency of transmission line inspection and to reduce safety hazards, a method based on the BeiDou positioning for human-machine collaboration in transmission line inspection is proposed. Drones equipped with then BeiDou positioning are utilized to collect transmission line data via the Web Mercator projection, determining the inspection routes. Fault parameters of transmission lines are extracted for manual monitoring, identifying overlay current and voltage frequency fault characteristics. The optimal inspection route is then determined based on these features, achieving human-machine collaboration in transmission line inspection. Experimental results demonstrate that the proposed method detected 11 fault nodes, with inspection paths closely matching the transmission line structure. It identified six defects in power system transmission lines, with an inspection time of only 2.3 seconds. The area under the ROC curve, encompassing true positive and false positive rates, is approximately 5000, indicating the method’s effectiveness, efficiency, and accuracy in inspection.

Carbon Emission under Industry 5.0

Industry 5.0 integrates people and machines into production to achieve more personalization, possibilities, and sustainable development. Industry 5.0 has three core elements: Humancentric, sustainability, and resilience. Humancentric emphasizes human-machine collaboration. Talent is the core of industrial operation. Technology is to serve humans better, not to replace humans. The sustainability of Industry 5.0 includes economic, environmental, social, and technological sustainability. In these aspects, Industry 5.0 has new requirements and development directions for sustainability. The carbon footprint runs through every link of the industry. Reducing carbon emissions is the requirement of Industry 5.0 for environmental sustainability. The technological innovation and progress of Industry 5.0, as well as the concept of sustainability, all affect and reduce carbon emissions to varying degrees.

Printing the Future Layer by Layer: A Comprehensive Exploration of Additive Manufacturing in the Era of Industry 4.0

In the era of Industry 4.0, 3D printing, or additive manufacturing (AM), has revolutionized product design and manufacturing across various sectors. This review explores the evolution of 3D printing technology and its impact on industrial innovation, highlighting advancements in aeronautics, the automotive industry, and biomedicine. Various AM processes, such as binder jetting, direct energy deposition, and powder bed fusion, and materials like metals, polymers, ceramics, and composites, are discussed. Innovations like high-speed sintering, continuous liquid interface production, and bioprinting demonstrate ongoing advancements. The potential of 3D printing in personalized medical applications is emphasized due to its flexibility in geometry and materials. Despite progress, challenges like standardization, material quality, recycling, sustainability, and economic feasibility hinder widespread adoption. Overcoming these challenges is crucial for optimizing 3D printing technologies, ensuring high-quality, efficient, and affordable production. The review also addresses the future prospects of 4D and 5D printing technologies and their potential applications in various industries. This overview underscores 3D printing’s role in shaping the future of manufacturing within the context of Industry 5.0, emphasizing human–machine collaboration and sustainability.

The Impact of Perceived AI Replacement on Employee Job Satisfaction: Exploring the Mediating Role of Self-Esteem

Artificial intelligence (AI) has become more prevalent in the workforce due to the quick development of technology, which has led to a generalized fear of job loss among workers. AI integration in the workplace has resulted in a decline in workers’ self-esteem, which has lowered their drive to find work and decreased their job happiness.By employing convenience sampling and doing data analysis on employee anxiety, self-esteem, and job satisfaction, it confirmed that the introduction of artificial intelligence into the workforce has a significant impact on workers.AI is an emotionless instrument that cannot interact with employees in a typical interpersonal manner. People’s concerns about their abilities and self-worth are frequently heightened by this absence of meaningful interaction. In the absence of the interpersonal dynamics that define human relationships, employees could experience feelings of isolation and ambiguity over their position in the workforce. Employers must therefore address this issue by encouraging a work environment that recognizes the advantages of AI while simultaneously valuing human abilities and innovation. Employers may lessen the detrimental effects of AI on worker morale and engagement by creating a work environment that promotes human-machine collaboration. Giving staff members opportunities for skill enhancement through training and development can also help them adjust to the changing nature of the labor market.

DESIGN AND DEVELOPMENT OF A SMART FACTORY USING INDUSTRY 4.0 TECHNOLOGIES

This systematic literature review examines the operational and organizational impacts of Industry 4.0 technologies on smart factories, drawing on insights from 120 peer-reviewed articles published between 2010 and 2024. The study follows the PRISMA guidelines to ensure a transparent and rigorous review process, focusing on the key enablers of smart manufacturing, including cyber-physical systems (CPS), the Internet of Things (IoT), big data analytics, artificial intelligence (AI), and machine learning (ML). The findings reveal that smart factories offer significant benefits, including enhanced flexibility and customization, predictive maintenance that reduces downtime by up to 50%, and improved supply chain integration through real-time data sharing. Big data analytics plays a crucial role in optimizing operations by allowing factories to perform continuous real-time adjustments, improving efficiency and reducing resource waste. The review also highlights the evolving role of the workforce, with a growing need for technical skills and increased human-machine collaboration in smart manufacturing environments. However, challenges such as interoperability, cybersecurity, and the economic feasibility of large-scale smart factory implementations remain underexplored in the literature. Emerging technologies like blockchain and 5G offer promising solutions, but further research is required to assess their full potential. Overall, this review provides a comprehensive understanding of the current state of smart factory technologies and outlines key areas for future research, particularly in addressing gaps related to standards, workforce adaptation, and security concerns.

An Overview of AI Applications in Cybersecurity for IT Management

Background: Cybersecurity serves as a defense mechanism against unauthorized access, theft, information disclosure, and service disruptions for electronic data, networks, and computer systems. Traditional cybersecurity strategies have struggled to counter increasingly sophisticated and automated cyberattacks, necessitating the exploration of advanced solutions such as artificial intelligence (AI). Aim: This research aims to highlight the role of AI technology and systems in enhancing cybersecurity measures, addressing how AI-based solutions can defend against cyber threats and identifying the associated challenges and future research directions. Methodology: A systematic literature review was conducted, focusing on recent advancements in AI applications for cybersecurity. Key databases and search engines were utilized to gather relevant articles, which were then filtered based on criteria such as language, publication quality, citation count, and accessibility. The collected literature was analyzed to extract insights on AI methods and their uses in cybersecurity. Results: The study found that AI offers several benefits in addressing cybersecurity concerns. AI systems can identify novel and complex cyber threats, handle substantial volumes of security data, and establish baselines for normal network behavior to detect deviations. Various AI techniques, including neural networks, expert systems, intelligent agents, and machine learning, were identified as effective tools in combating cyber threats. AI has demonstrated significant improvements in the detection and prevention of cyberattacks, enhancing overall cybersecurity effectiveness. Findings: While AI enhances cybersecurity capabilities, it also introduces new challenges such as the need for substantial data and resources, managing false alarms, and vulnerability to adversarial attacks. The study underscores the importance of continuous research to develop robust AI systems that can adapt to evolving cyber threats. Future research should focus on addressing the limitations of AI in cybersecurity, including adversarial threats, data integrity, and human-machine collaboration.

Haste Makes Waste: A Moderated Mediation Model of the Mechanisms Linking Artificial Intelligence Advancement to Film Firm Performance

Artificial intelligence (AI) has emerged as a transformative force in the modern film industry, revolutionizing production processes and redefining audience experiences. This study delves into the mechanisms through which AI advancement impacts film firm performance, with a focus on the mediating roles of innovation speed and quality, and the moderating effect of human-machine collaboration. Employing a resource-based view, we construct a moderated mediation model and analyze data from 355 global film firms. Our findings reveal that AI advancement positively influences film firm performance, with innovation quality serving as a significant mediator. However, the mediating role of innovation speed is not pronounced. Moreover, the degree of human-machine collaboration positively moderates the relationships between AI advancement and both innovation speed and quality. However, its moderating role between AI advancement and firm performance is not significant. The study underscores the theoretical and practical implications of utilizing advanced AI to foster innovation and competitive advantage in film firms.

From the “Desk Set” to “Doraemon”: A comparative analysis on the sociotechnical imaginaries of artificial intelligence in news work

Artificial intelligence has become increasingly pervasive throughout the entire news cycle. In response to this trend, this paper explores journalists’ sociotechnical imaginaries concerning the integration of AI in news production, focusing on their perceptions of AI’s opportunities and ethical challenges. The study also examines the influence of diverse media and discourse cultures on these perceptions by conducting problem-centered interviews with journalists from China, Japan, Switzerland, and the UK. Through an inductive thematic analysis of the interviews, the results reveal that journalists across these four countries acknowledge the potential advantages of AI in journalism, such as enhanced efficiency and improved data analysis. However, their expectations regarding human-machine collaboration in news work vary according to cultural contexts. Furthermore, the findings highlight that the interviewed journalists advocate for the design and implementation of AI systems to adhere to ethical standards.

Herd behavior identification based on coevolution in human–machine collaborative multi-stage large group decision-making

As the scale of multi-stage large group decision-making (LGDM) continues to expand, the possibility of low-contribution individuals exhibiting herd behavior also increases, potentially leading to the phenomenon of “fishing in troubled waters.” This may obstruct the speed of consensus reaching while generating no valuable opinions, which is a topic worthy of exploration. Considering that humans are easily influenced by interests, the employment of machine intelligence to objectively identify herd behavior is more appropriate. In this context, a herd behavior identification method based on behavioral characteristics clustering from the perspective of human–machine collaboration is herein proposed. First, from the human side, an opinion–social network coevolution model is constructed to simulate the consensus reaching process (CRP) of the expert group. Then, the group is clustered into three subgroups in consideration of behavior that encompasses both opinion changes and trust relationship changes. Based on this, the low-contribution cluster with a herd behavior pattern can be optimized from the machine side. Through simulation experiments, it is verified that herd behavior management significantly accelerates the consensus-reaching speed under the premise of having minimal impact on the decision-making results. In general terms, this study is the first to propose the concept of herd behavior and provides a solution to manage it from a new perspective, which is suitable for application in multi-stage LGDM scenarios.

Industry 5.0's pillars and Lean Six Sigma: mapping the current interrelationship and future research directions

PurposeIn the evolving landscape of Industry 5.0 (I5.0), which emphasises sustainability, human-centricity and resilience, mapping the current interrelationship and future research directions, the role of Lean Six Sigma (LSS) methodology remains underexplored. Our study addresses this gap by examining the potential of LSS to support I5.0 while identifying areas for further investigation.Design/methodology/approachThis study's multifaceted approach, which includes systematic literature review (SLR), bibliographic network analysis (BNA) and expert validation (EV), provides a holistic exploration of the interaction between LSS and I5.0 as the basis for well-founded conclusions.FindingsThe analysis yields several valuable insights. Firstly, it demonstrates the absence of a direct link between LSS and I5.0. Secondly, the substantial body of literature analysed establishes connections between LSS and its pillars. Thirdly, the analysis identifies points of intersection, difference and similarity between LSS and I5.0, highlighting the potential of LSS to facilitate implementation of I5.0 through its proven methodologies, continuous improvement culture, risk management, error learning, human–machine collaboration and training and skill development.Originality/valueThis study pioneers the effort to realise the latent potential of LSS in the context of I5.0. Its systematic identification of the synergies between these paradigms fills a critical gap in the literature and gives policymakers, managers and researchers a guide for informed decision-making to maximise the benefits of I5.0 for individuals, companies, society and the planet.

Digital capabilities driving industry 4.0 and 5.0 transformation: Insights from an interview study in the maintenance domain

This study examines the role of digital capabilities in facilitating organizational transformation within the maintenance domain, focusing on the need for transitioning from Industry 4.0 to Industry 5.0. The evolving concept of Industry 5.0 complements Industry 4.0 by shifting the focus from automation to human-machine collaboration. Industry 5.0 aims to create a more cooperative relationship between humans and machines to maximize productivity and innovation. To facilitate digital transformation, companies need to develop suitable digital capabilities. Through interviews with maintenance specialists and practitioners, we identified key challenges and facilitators to implementing emerging technologies. Key findings highlight the importance of providing high-quality education, aligning digital tools with work tasks, and offering user-friendly digital solutions. Furthermore, the findings emphasize the need for robust information sharing, cooperation, and the formalization of responsibilities between maintenance and production functions. Lastly, establishing a clear digital strategy, commitment to investments, and testing new technologies are crucial for facilitating digital transformation and promoting a culture of innovation.

Design and practice of human-machine cooperative international Chinese character teaching in ChatGPT application

The incorporation of artificial intelligence (AI) into language education has created new opportunities for improving the instruction and acquisition of Chinese characters. Nevertheless, the cognitive difficulties linked to the acquisition of Chinese characters, such as their intricate visual features and lack of clear meaning, necessitate thoughtful deliberation when developing AI-supported learning interventions. The objective of this project is to explore the capacity of a collaborative method between humans and machines in teaching Chinese characters, utilising the advantages of both human expertise and AI technology. We specifically investigate the utilisation of ChatGPT, a substantial language model, for the creation of instructional materials and evaluation methods aimed at teaching Chinese characters to individuals who are not native speakers. The study utilises a mixed-methods approach, which involves both qualitative examination of lesson plans created by ChatGPT and quantitative evaluation of student learning outcomes. The results indicate that the suggested framework for human-machine collaboration can successfully tackle the cognitive difficulties associated with learning Chinese characters, resulting in enhanced learner involvement and performance. Nevertheless, the research also emphasises the constraints of AI-generated material and the significance of human involvement in guaranteeing the accuracy and dependability of educational interventions. This research adds to the expanding collection of literature on AI-assisted language learning and offers practical insights for educators and instructional designers who aim to use AI tools into Chinese language curriculum. The results emphasise the necessity of employing a multi-disciplinary strategy in AI-supported language learning, incorporating knowledge from cognitive psychology, educational technology, and second language acquisition.

The impact of human-AIGC tools collaboration on the learning effect of college students: a key factor for future education?

PurposeThe maturity of artificial intelligence technology and the emergence of AI-generated content (AIGC) tools have endowed college students with a human-AIGC tools collaboration learning mode. However, there is still a great controversy about its impact on learning effect. This paper is aimed at investigating the impact of the human-AIGC tools collaboration on the learning effect of college students.Design/methodology/approachIn this paper, a hypothesized model was constructed to investigate the effects of dependence, usage purpose, trust level, frequency, and proficiency of using AIGC tools on the learning effect, respectively. This paper distributed questionnaires through random sampling. Then, the improved Analytic Hierarchy Process (AHP) was used to assign weights and normalize data. Lastly, one-way ANOVA and multiple linear regression analyses were used to measure and analyze variables, revealing the mechanism of influence.FindingsThe usage purpose, frequency, and proficiency of using AIGC tools have a significant positive effect on learning. Being clear about the usage purpose of AIGC tools and matching the specific study tasks will enhance the learning effect. College students should organically integrate AIGC tools into each learning process, which is conducive to building a learning flow applicable to oneself, improving efficiency, and then enhancing learning effects. The trust level in AIGC tools is significant, but positively and weakly correlated, indicating that college students need to screen the generated content based on their knowledge system framework and view it dialectically. The dependence on AIGC tools has a negative and significant effect on learning effect. College students are supposed to systematically combine self-reflection and the use of AIGC tools to avoid overdependence on them.Research limitations/implicationsBased on the findings, the learning suggestions for college students in human-machine collaboration in the AIGC era are proposed to provide ideas for the future information-based education system. For further research, scholars can expand on different groups, professions, and fields of study.Originality/valuePrevious studies have focused more on the impact of AIGC on the education system. This paper analyzed the impact of the various factors of using AIGC tools in the learning process on the learning effect from the perspective of college students.

Analysis of virtual standardized patients for assessing clinical fundamental skills of medical students: a prospective study

BackgroundHistory-taking is an essential clinical competency for qualified doctors. The limitations of the standardized patient (SP) in taking history can be addressed by the virtual standardized patient (VSP). This paper investigates the accuracy of virtual standardized patient simulators and evaluates the applicability of the improved system’s accuracy for diagnostic teaching support and performance assessment.MethodsData from the application of VSP to medical residents and students were gathered for this prospective study. In a human–machine collaboration mode, students completed exams involving taking SP histories while VSP provided real-time scoring. Every participant had VSP and SP scores. Lastly, using the voice and text records as a guide, the technicians will adjust the system’s intention recognition accuracy and speech recognition accuracy.ResultsThe research revealed significant differences in scoring across several iterations of VSP and SP (p < 0.001). Across various clinical cases, there were differences in application accuracy for different versions of VSP (p < 0.001). Among training groups, the diarrhea case showed significant differences in speech recognition accuracy (Z = -2.719, p = 0.007) and intent recognition accuracy (Z = -2.406, p = 0.016). Scoring and intent recognition accuracy improved significantly after system upgrades.ConclusionVSP has a comprehensive and detailed scoring system and demonstrates good scoring accuracy, which can be a valuable tool for history-taking training.

Reshaping the Teacher-Student Relationship in Higher Education Through ChatGPT

The rapid progress in artificial intelligence, especially with advancements in generative AI such as ChatGPT within Natural Language Processing (NLP), has introduced both new opportunities and challenges for traditional teaching models and the teacher-student relationship in higher education. ChatGPT's advanced deep learning and NLP capabilities have overcome limitations inherent in conventional teacher-student interactions, such as one-way communication and geographical constraints, and have promoted the growth of personalized teaching. This innovation has fostered a learning environment where "Everyone can be a teacher, every place can be a classroom, and teaching can happen at any time". However, this technology also brings challenges, including greater reliance on technology, diminished emotional connections between teachers and students, and issues related to algorithmic transparency. This paper examines ChatGPT's application in higher education, analyzing its effects on evolving teaching and learning models and the transformation of teacher-student relationships. By employing the “teacher-student-AI” triadic interaction model, the study highlights the significance of human-machine collaboration. It aims to optimize AI's self-learning processes, enhance human-centered education, and advance intelligent education in a more efficient and empathetic direction, offering both theoretical insights and practical guidance for transforming teacher-student relationships and improving undergraduate education standards.

AI-Powered Obstacle Detection for Safer Human-Machine Collaboration

Abstract This article deals with ensuring and increasing the safety of mobile robotic systems in human-machine collaboration. The goal of the research was to design and implement an artificial intelligence application that recognizes obstacles, including humans, and increases safety. The resulting mobile Android application uses a MiDaS model to generate a depth map of the environment from the drone’s camera to approximate the distance from all obstacles to avoid the drone’s collision. Besides, this work introduced us to DJI Mobile SDK and neural network optimizations for their use on smartphones.