This study challenges the prevailing digital native’s paradigm by examining technology adoption patterns across different generational cohorts in collaborative online learning environments. It investigates how generational differences influence technology acceptance, usage behaviors, and learning outcomes in digital educational spaces. A mixed-methods approach was employed, combining quantitative surveys (n = 847) and qualitative interviews (n = 32) across four generational cohorts: Generation Z (born 1997–2012), Millennials (1981–1996), Generation X (1965–1980), and Baby Boomers (1946–1964). The study utilized the extended technology acceptance model (TAM2) framework, incorporating social influence and cognitive instrumental processes. Findings reveal significant variations in technology adoption patterns that transcend traditional generational assumptions. While Generation Z demonstrated higher initial technology acceptance rates (M = 4.23, SD = 0.87), Generation X showed superior sustained engagement in collaborative learning activities (M = 4.45, SD = 0.76). Baby Boomers exhibited unexpected adaptability when provided with appropriate scaffolding and support mechanisms. The digital natives concept oversimplifies technology adoption behaviors. Crossgenerational collaboration in online learning spaces benefits from differentiated instructional design approaches that acknowledge varying technological competencies while leveraging the unique strengths of each generational cohort.

In a collaborative learning environment involving strategic alliances, the process of integration is critical. The main objective of this research is to conduct a comprehensive examination of dynamic integration capabilities (relationship capital, surfacing, joint learning structure, and knowledge acquisition), particularly in the context of learning within strategic alliances. This research is to answer how the public institution can stay competitive and improve their integration capability through strategic alliances in learning area specifically. This research is groundbreaking as it is the first to empirically examine all the learning framework’s constructs, which includes relationship capital, surfacing, joint learning structure, and knowledge acquisition, in relation to dynamic integration capability and strategic alliance performance at the same time. The research utilized a quantitative method. The seven-point Likert scale survey was conducted through online to 83 respondents from 83 strategic alliances within a public institution in Indonesia from November 2023 to June 2024. Additionally, open-answered interviews with some respondents were organized to deepen the understanding of research findings. The research reveals that dynamic integration capability has a positive correlation with the performance of strategic alliances. Furthermore, it indicates that all constructs within the learning framework significantly impact dynamic integration capabilities. Among these, the construct of knowledge acquisition shows the strongest correlation with dynamic integration capability. The knowledge acquisition has the most significant impact on dynamic integration capability and suggested that public institutions and strategic alliances are effectively promoting cooperation through joint research projects and other similar activities to stay competitive. Keywords: Dynamic Integration Capability; Learning Framework; Strategic Alliance; Dynamic Capability.

Purpose The purpose of this review study is to examine how students’ competency-based education is affected by technology-based collaborative learning. The study intends to determine how technology integration in collaborative learning environments affects students’ development of competencies necessary for success in academics and beyond through a thorough evaluation of the body of existing literature. Design/methodology/approach In this study, major electronic bibliographic databases have been the focus of this narrative literature review for selecting related studies. Based on screening, inclusion and exclusion criteria, 105 articles from 1998 to 2024 were included in this study. The literature search produced seven themes describing advancement, advantages, challenges and mitigating strategies for implementing technology-based collaborative learning in Higher Education Institutions globally. Findings Technology has assimilated into contemporary education and provides a wealth of chances for group learning. Incorporating technology into collaborative learning methodologies for students improves their competency-based education. Technology also makes it easier for students to communicate and work together outside of the classroom by connecting them with resources, professionals and classmates from a variety of locations and backgrounds. Nevertheless, despite the possible advantages, there are obstacles to overcome in the successful application of technology-based collaborative learning. Practical implications The findings will provide valuable insights that will help educators, policymakers, industry partners and researchers better prepare students for the demands of the digital age and improve the quality of education by identifying successful strategies for integrating technology into collaborative learning environments. Originality/value This study is unique because it synthesizes and critically analyzes previous research to identify the gaps in technology-based collaborative learning and how it affects students’ competency-based education in higher education institutions. There is limited work conducted in this domain based on this discourse.

This paper investigates the formation control problem of multi-UAV systems in the presence of switched trajectories and time-varying communication topologies. A distributed formation control protocol is proposed to enable UAVs to track piecewise continuous trajectories while the underlying communication network switches among a finite set of directed graphs. Sufficient and necessary conditions for achieving accurate formation tracking under dual-switching scenarios are derived through stability analysis while the stability of the overall switched system is proven by using multiple Lyapunov functions. To ensure collision avoidance during both trajectory and topology transitions, control barrier functions (CBFs) are employed to construct safety sets, and a quadratic programming(QP)-based optimization framework is designed to modify control inputs in real time. Simulation results demonstrate that the proposed approach effectively coordinates formation tracking, topology switching, and inter-agent safety, offering a solution for UAV collaboration in dynamic and uncertain environments.

The increasing use of digital devices by young learners often results in passive content consumption rather than active skill development. This exploratory study examines whether a peer-like Artificial Intelligence (AI) agent can improve the quality of computer-supported collaborative learning. The aim was to assess the impact of a hidden AI-based chatbot on the dynamics and outcomes of group problem-solving in a school setting. A gamified application was developed in which student groups collaborated on challenging tasks. In a controlled experiment, some groups included a hidden AI-based chatbot acting as a peer, programmed to provide Socratic prompts and motivational scaffolding without giving direct answers, while control groups consisted only of human participants. Quantitative and qualitative data, including time to solution, answer correctness, and chat logs, were collected to compare performance and interaction patterns between the two conditions. Given the limited sample size and primarily descriptive analyses, the findings should be interpreted as preliminary. The results suggest differences in collaborative dynamics and problem-solving efficiency between groups assisted by the AI agent and the unassisted control groups. The findings suggest that integrating a hidden, peer-like pedagogical agent may represent a promising approach for supporting collaborative learning processes, enhancing group engagement by subtly guiding discussion without disrupting the natural peer-to-peer dynamic. These results highlight the potential of hidden AI to enhance collaborative learning environments through non-intrusive support. Further research with larger samples is needed to validate these initial observations.

This article presents the development of the Congruence Engine (CE) Data Register, a prototype for a minimum, community-driven infrastructure to support a distributed United Kingdom (UK) national collection. Developed as part of the Arts and Humanities Research Council’s Towards a National Collection programme (2021–2025) and led by the Science Museum Group, the CE project aimed to connect industrial heritage collections across museums, archives, and community groups through open, scalable digital tools. Drawing inspiration from Bradford-based participatory research and the concept of the “social machine”, the CE Data Register leverages Wikibase.Cloud—closely related to and interoperable with Wikidata—to create a transparent, collaborative environment for describing and linking cultural heritage data. Complemented by a custom web application that lowers technical barriers to participation, the register exemplifies how lightweight, open infrastructures such as Wikibase and Wikidata can support sustainable, inclusive cultural heritage research. By emphasizing minimal metadata requirements, interoperability, and strong documentation practices, this prototype demonstrates a practical pathway toward a resilient, community-led digital ecosystem for connecting the UK’s distributed collections.

ABSTRACT Digitalized manufacturing processes necessitate a shift from traditional production control systems to more intelligent frameworks. Existing systems reliant on programmable logical controllers fall short in handling the influx of data generated by the Internet of Things layer and the array of IT systems integral to modern operations. This article presents technical developments made within MESLedger, a collaborative project aiming to enhance manufacturing execution by integrating blockchain technology and artificial intelligence. The first part of our work investigates how deep reinforcement learning, particularly the proposed Dual Attention Network for Multi-Objective Proximal Policy Optimization, can optimize job scheduling by balancing objectives such as makespan and energy consumption. This approach formulates the Flexible Job Shop Scheduling Problem as a Markov decision process and uses actor-critic networks enhanced with dual attention mechanisms to generate adaptive and context-aware scheduling strategies. The second part explores how blockchain technology, specifically Hyperledger Fabric, can be used to secure communication within collaborative industrial environments by managing critical manufacturing processes through smart contracts and decentralized architectures. Together, these components form the foundation of an intelligent manufacturing execution systems environment capable of dynamically analyzing data and orchestrating operations based on customizable performance priorities.

Due to the advantages of high spatial immersion and strong real-time interactivity, Collaborative Virtual Environments (CVEs) are gradually shifting toward immersive 3D CVEs based on Mixed Reality technology in recent years. However, these new design elements have introduced certain adverse effects on remote collaboration effectiveness, such as reduced collaboration efficiency, declined communication quality, and diminished team cohesion and engagement. Most current research on immersive remote collaboration addresses these issues from the perspectives of tools and team factors, lacking consideration for task-related factors. Therefore, this study explores the impact of task factors (task type and task complexity) on collaborative states and collaborative effectiveness in remote collaboration in 3D CVEs. The research indicates that high-complexity tasks enhance users’ collaborative interdependence and sense of coexistence, thereby improving the collaborative state. This study provides a new perspective for designing efficient 3D virtual collaborative environments.

Challenges and constraints in global health practice such as inadequate international cooperation, cultural sensitivity, and interdisciplinary collaboration inform key gaps and opportunities to strengthen global health education, including technical skills and competencies needed for the next generation of global health leaders. We describe an innovative model of global health education leveraging advances in educational technology, co-developed and implemented by universities within the Association Pacific Rim Universities. The model was developed to address challenges and opportunities to strengthen global health education and has been implemented in two global health courses—one on leadership and one on ethics—for almost 10 years. The model was innovative in providing a virtual global learning environment in collaboration with multiple universities in diverse contexts, a novel approach back in 2015 and one that to this day is not widely available across universities. An evaluation of the model was conducted in 2025, and findings are reported. To evaluate the global health education model, we applied a mixed methods approach including surveys and discussion groups with students and faculty participating in the global health leadership course in the past 2 years. Approximately 83% of students (148/179) responded to the student survey and more than 80% of respondents reported high levels of mastery of course learning objectives and competencies, while 97% of students reported satisfaction with the course, with an emphasis on the model’s benefits for developing and practicing global collaboration and communication. Faculty reported high satisfaction with the course’s inclusion and modelling of a decolonized approach to global health education with the diversity of participating learners, guest speakers, and case studies and readings in the course. Developing and implementing effective global health education is critical for improving education and practice. Our intention in sharing this case study is to offer readers the opportunity to learn about and adapt this model in their own curricula and settings as desired as we collectively strive for improved global health education. The more such models are implemented across various topics and settings, the more we can achieve increased scalability and success in key components of global health training and practice.

The teaching of English First Additional Language (EFAL) in the Intermediate Phase presents persistent challenges, particularly during learners' transition from the Foundation Phase. Integrating modern media technologies into the curriculum offers potential solutions to support both teachers and learners during this critical stage. This study examines how media technologies can be utilized to overcome barriers in EFAL teaching and learning, particularly in resource-constrained and rural school contexts. A qualitative research design was adopted, using purposive sampling to select 16 participants with lived experiences in EFAL instruction. Data collection included semi-structured face-to-face interviews, non-participant classroom observations, and a review of teaching and learning documents to support triangulation. Findings reveal that teachers' awareness and use of media technologies are limited, primarily restricted to laptops, smartphones, and smart TVs. Few participants referenced the pedagogical value of audio, visual, or audio-visual media. The study also highlights a lack of strategic prioritization by the Department of Basic Education (DBE) in promoting the integration of media technology, particularly in rural schools. The study recommends targeted teacher training programmes that encompass a broad spectrum of media tools. Furthermore, investment in infrastructure, which includes reliable Wi-Fi access and learner devices, is essential. Developing strong policy frameworks, offering ongoing professional development workshops, and fostering collaborative learning environments are also necessary to enhance EFAL instruction through media technology integration.

Abstract This article presents a transfer learning method to address the kinematics problem of a planar continuum parallel manipulator with large-deflection links. This method is motivated by the need for lightweight, space-efficient mechanisms in small spaces or human–machine collaborative environments. The manipulator has two independent branch chains with highly flexible panels as links, and the moving platform is driven by the bending deflections of these links. For kinematic analysis, sensitivity analysis is used to identify key parameters influencing the motion. Neural networks are then constructed for forward and inverse kinematics. Simulation data, such as end-effector pose and actuation lengths, are collected for preliminary network optimization. Considering the differences between actual and simulation platforms, transfer learning is applied to further optimize the network parameters. The proposed method leverages the nonlinear prediction capability of neural networks to handle complex large-deformation link modeling. Transfer learning significantly reduces training data and time while enhancing prediction accuracy. Experiments validate the method by comparing it with results without transfer learning. The maximum and average position errors of the end-effector are reduced from over 10.46 mm and 3.56 mm to approximately 1.21 mm and 0.19 mm, respectively.

Under the background of increasing medical demand and digital transformation, hospital resource scheduling in edge-terminal collaborative environments faces challenges such as information delay and insufficient decision-making accuracy. This study proposes a hospital resource allocation and scheduling framework based on genetic algorithm, which integrates edge terminal collaboration architecture, and heuristic optimization logic. The edge computing node is deployed in key medical areas, with a 1000 Mbps transmission rate and a 5 ms low latency hybrid network, to achieve real-time data acquisition and preprocessing of the terminal. The core of genetic algorithm adopts resource processing capability chromosome encoding, with a crossover probability of 0.7 and a mutation probability of 0.1. It minimizes task completion time, patient waiting time, and resource conflict cost through multi-objective fitness functions (α=0.4, β=0.3, γ=0.3). The validation results based on the MIMIC-III (Medical Information Mart for Intensive Care III) dataset show that the model's basic performance (accuracy 87%, F1 value 86%) and resource management effectiveness (accuracy 89%, F1 value 88%) outperform traditional FCFS (First Come, First Served) scheduling models, with an overall performance improvement of 2% -14%. This framework effectively improves resource utilization and service efficiency, provides practical solutions for optimizing hospital resource scheduling, and expands the application scenarios of heuristic algorithms in the medical field.

Industrial robotic arms utilized in contemporary industrial and collaborative environments must operate within increasingly congested and dynamically restricted workspaces while adhering to rigorous standards of safety, precision, and motion quality. This paper presents a two-stage framework for path planning and optimization of a 6-DOF industrial robotic arm navigating amid randomly distributed obstacles. A collision-free reference motion is initially created by integrating B-spline geometric interpolation with a bidirectional RRT-Connect planner, augmented by short-cutting and effective joint-space collision verification for a KUKA KR 4 R600 manipulator. The baseline trajectory is subsequently enhanced through two metaheuristic optimizers: a Whale Genetic hybrid algorithm (WGA) and the Grey Wolf Optimizer (GWO). These optimizers minimize a composite objective that incorporates end-effector trajectory length, joint-level energy consumption based on established motor characteristics, and trajectory smoothness measured by joint jerk. Simulation results indicate that, while the raw Bi-RRT trajectory is geometrically efficient and energy-efficient, it demonstrates excessively high jerk. The suggested enhancements based on WGA and GWO diminish the jerk index of the original Bi-RRT solution by roughly 94-96%, resulting in relatively slight increases in trajectory length and energy, while producing dynamically smooth, collision-free trajectories that adhere to all kinematic constraints. This work presents a comprehensive, implementation-ready methodology that compares sampling-based planning, and multi-objective metaheuristic optimization to produce executable, energy-efficient, and jerk-minimized motions for industrial manipulators in intricate environments.

Artificial intelligence (AI) holds immense promise for enhancing medical imaging analysis, particularly in the realm of anatomical segmentation. However, the technical complexities of developing and deploying AI models, often requiring substantial coding expertise, have traditionally posed a barrier to entry for many clinicians. This review explores the emerging landscape of low-code (LC) AI solutions in medical imaging, focusing on their potential to empower radiologists and other healthcare professionals to actively participate in AI development. We examine a practical workflow using the fastMONAI library, an LC extension of established tools like MONAI and fastai, demonstrating how clinicians can train a U-Net-based model for cardiac MRI segmentation with minimal coding. This approach significantly reduces the technical overhead, enabling clinicians to focus on clinically relevant aspects of model development and customization. The review highlights the benefits of LC AI in fostering a more inclusive and collaborative environment for AI innovation in radiology, while also acknowledging the potential limitations and considerations for successful implementation.

This paper proposes a dynamic metric-constrained optimization algorithm for edge-cloud collaborative environments with multi-dimensional situation verification under federated SOA services. The framework integrates adaptive metric encoding, federated optimization, and constraint verification to ensure robust and efficient service orchestration. Experimental evaluations on Google, Alibaba and Azure cluster traces demonstrate the superiority of the proposed approach over HEFT, NSGA-II and MOEA/D. Specifically, latency violations were reduced to 5%, while other constraint breaches were maintained below 7%. SLA satisfaction consistently exceeded 88% across diverse stress conditions, peaking at 91% under node churn. Furthermore, the algorithm achieved 170[Formula: see text]ms latency, 92.7% reliability and 75.6[Formula: see text]Mbps throughput with reduced energy consumption of 92.3[Formula: see text]J, outperforming static baselines. Runtime overhead was limited to 9.6[Formula: see text]ms with 95[Formula: see text]KB communication per round, enabling 5200 decisions per second. These results confirm that the proposed framework achieves a practical balance between adaptability, efficiency and robustness, making it suitable for deployment in dynamic edge-cloud systems.

Laboratory robotics is advancing from routine automation toward autonomous systems capable of intelligent decision-making and flexible execution. This perspective outlines key milestones and introduces the ADePT framework, which defines four core dimensions of robotic capability proficiency: adaptability and learning, dexterity, perception, and task complexity. We discuss future directions for self-driving laboratories, including robot-centric, end-to-end robotic integration, and collaborative human-robot environments. These scenarios highlight the importance of technological enablers and evolving regulatory paradigms. By connecting present technologies to emerging system configurations, this work offers a foundation for designing autonomous laboratory ecosystems that support scientific discovery and operational efficiency.

Industry 5.0, regarded as the fifth industrial revolution, marks a transition that prioritizes human uniqueness and contribution over the mere technical efficiency of machines. Here, a collaborative environment emerges where humans and machines—specifically Artificial Intelligence—work in tandem. In such a converged landscape, technological management must be founded not only on mathematical formulas and data analytics but also on humanitarian values, ethics, and social responsibility. To fulfill this fundamental requirement, Thiruvalluvar’s Thirukkural provides an exceptional philosophical foundation. By structuring human life and social systems upon the three pillars of Virtue (Aram), Wealth (Porul), and Love (Inbam), this treatise serves as a guiding light for the new challenges of the industrial world. The role of Thirukkural in shaping Human-Centric AI Management is multifaceted. Firstly, leadership concepts such as "A king is he who possesses a broad and discerning mind" (Kural 381) highlight the necessity of comprehensive knowledge. This emphasizes the ability to integrate AI outputs with human intelligence to make ethically sound decisions. Secondly, the principle from the Porutpal (Book of Wealth) to "Earn wealth... with grace" (Kural 755) stresses that profit-making should not be the sole aim; rather, wealth should be acquired through honest means that respect the welfare of all. This acts as an ethical compass for modern industrial management, which must reconcile profit motives with social welfare and environmental responsibility. Thirdly, the emphasis on discipline—"Conduct yields excellence" (Kural 131)—reminds us that ethical conduct must be present in every dimension of technological management, including data privacy, algorithmic ethics, and employee codes of conduct. Thus, Industry 5.0 bridges the gap between humans and machines. This connection must remain centered on humanitarian values. Thirukkural’s timeless philosophies offer the perfect foundation for this. By adopting its values of integrity, responsibility, justice, discipline, and humanity as the driving force of AI management, technological progress will truly become a reliable instrument for social advancement.

Accurate perception of a diver's position and orientation by Autonomous Underwater Vehicles (AUVs) is essential for effective human-robot collaboration in underwater environments. However, conventional position and orientation estimation methods that combine deep learning with Perspective-n-Point (PnP) algorithms are primarily designed for rigid objects. In contrast, divers exhibit highly variable postures underwater, with no fixed configuration. To address this limitation, this paper proposes a framework for estimating the six-degree-of-freedom (6-DoF) position and the orientation of a diver. In addition, a novel network architecture, termed "VideoPose5CH," is proposed. In the proposed framework, temporal sequences of 2D joint coordinates are provided to VideoPose5CH, which then outputs the 3D joint coordinates of the current frame as well as the corresponding refined 2D joint locations. Subsequently, the diver's 6-DoF position and orientation relative to the camera are further recovered via a PnP algorithm. To mitigate the scarcity of underwater 3D human pose datasets, a land-based 3D human pose dataset augmentation strategy tailored to underwater conditions is further proposed. With this strategy, diver pose estimation accuracy is improved and the robustness of the proposed method across diverse scenarios is enhanced. Experimental results demonstrate that the proposed method can stably estimate the 6-DoF position and orientation of the diver within a distance range of 2.643 m to 11.477 m. The average position errors along the three axes are 7.33 cm, 4.04 cm, and 27.15 cm, respectively, while the average orientation errors are 6.96°, 8.47°, and 2.62°.

This study analyzes Malaysia's legal experience in managing tax compliance risk, a model highly regarded for its transparency and effectiveness. Malaysia's legal framework, operated by the Inland Revenue Board of Malaysia (IRBM), employs a risk classification and cooperation system for taxpayers, combined with big data analytics to detect errors in tax declarations and assess the probability of violations. Simultaneously, coordination mechanisms play a crucial role in promoting voluntary compliance, enhancing accountability, and fostering a collaborative tax governance environment between the tax authority and businesses. Based on Malaysia's experience, the study proposes several policy implications for Vietnam to contribute to the modernization of the Vietnamese tax system towards transparency, risk-based management, and sustainable tax compliance.

Problem definition: Worker attrition is critical and costly, disrupting operations across industries and leading to significant productivity losses. In healthcare, nurse attrition poses even greater challenges, which are exacerbated by persistent shortages and increasing burnout. Despite its importance, nurse attrition remains underexplored in operations management (OM) literature, particularly concerning how different workload dimensions influence voluntary attrition. This study aims to address this gap by investigating how various workload dimensions, including nurse responsibility, overtime shift, emotional toll, and cumulative workload, affect voluntary attrition among ICU nurses. Methodology/results: Utilizing high-resolution data from a large U.S. hospital system, we analyze 26 months of operational, clinical, and HR records, capturing nurses’ dimensions of workload leading up to voluntary attrition decisions. Our findings reveal a nuanced relationship; whereas greater nurse responsibility during a shift reduces the likelihood of voluntary attrition, cumulative workload over time shows a U-shaped relationship with the likelihood of voluntary attrition. Additionally, both the emotional fatigue from handling patient death events and the burnout from attending overtime shifts heighten the likelihood of voluntary attrition. One more incident of emotional fatigue increases the odds of voluntary attrition by 54.3%. One more overtime shift increases the odds of voluntary attrition by 58.5%. However, supportive coworkers can help mitigate some negative effects, highlighting the importance of collaborative environments. Managerial implications: This research makes several contributions. First, we estimate the distinct effects of workload dimensions on voluntary attrition. Second, we demonstrate how supportive coworkers act as a buffer against burnout-induced attrition. Finally, we offer actionable strategies for managers to enhance workforce retention: monitoring workloads to prevent fatigue-driven attrition, implementing flexible scheduling to allow recovery, and fostering peer support systems. By addressing a critical issue in high-stakes environments like healthcare, this study enriches OM literature and provides practical insights for organizations seeking to retain talent in knowledge-intensive fields. History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2025.0037 .