Adaptability: Thriving in Constant Change

The integrating AI into teaching and learning has the potential to transform traditional classroom environments into hybrid intelligence learning environments, whereby human teachers and AI teachers (educational robots) work together synergistically to enhance students’ learning processes and outcomes. To understand and optimize the synergistic effect of human–AI collaboration in hybrid intelligence learning environments, this study proposes a human–AI synergy degree model (HAI-SDM). A case study was conducted to examine the synergy degree and order degree in human–AI collaboration, involving forty students and one teacher from a class in a junior high school. The results indicate that the order degree between human teacher and AI machines remains at a moderate level while undergoing dynamic changes. The synergy degree fluctuates between low and moderate, reflecting relatively orderly development among the three subsystems (collaboration subject subsystem, collaboration process subsystem and collaboration environment subsystem), but one subsystem may exhibit disordered behaviours in contrast to the others. These findings have implications for developing more effective human-AI classroom collaboration and promoting the effective integration of AI into teaching and learning.

Search and rescue (SAR) operations in mountainous terrain present significant challenges due to complex environments, time-critical decisions, and the need for effective human–AI collaboration. Existing approaches typically employ either fully autonomous systems that lack adaptability to varying task requirements, or fixed human–AI authority allocations that fail to leverage the distinct strengths of humans and AI across different mission phases. This paper proposes Phase-Aware Hierarchical Reinforcement Learning (PAHRL), a novel framework that dynamically allocates decision-making authority between human operators and AI agents based on identified task phases. First, we formulate the mountain SAR problem as a three-phase task structure: Wide Search (WS), Target Confirmation (TC), and Rescue Coordination (RC), and examine the consistency of this decomposition through unsupervised clustering analysis, supported by bootstrap stability (ARI = 0.983 ± 0.083) and multiple clustering metrics. Second, we design an adaptive authority mechanism with four levels (L1: Human-Led to L4: Full-Auto) that automatically adjusts human involvement based on current phase characteristics and environmental uncertainty estimates. Third, we introduce a priority-based task execution module that ensures efficient resource allocation across multiple rescue objectives while respecting authority constraints. Extensive experiments demonstrate that PAHRL outperforms baseline methods, achieving a 20.9% higher success rate compared to standard PPO (59.0% vs. 48.8%) and 66.7% improvement over heuristic approaches. PAHRL maintains 96.9% precision even under 60% noise conditions with only 0.09 false rescues per episode. Ablation studies further reveal that phase awareness serves as a critical robustness mechanism; removing phase detection causes complete mission failure under noisy conditions. These results evaluate that phase-aware dynamic authority allocation significantly enhances both efficiency and robustness in human–AI collaborative SAR missions. While demonstrated in a proof-of-concept simulation with computational human models, validation with real operators and more complex environments remains essential before operational deployment.

Purpose This paper examines how human–AI collaboration boundaries are conceptualized and implemented differently across two key European sectors: healthcare and financial services. Using a narrative literature review methodology and the AI–human collaboration theoretical framework, it analyzes sectoral variations in collaborative dynamics. Design/methodology/approach A narrative literature review approach is employed to synthesize existing knowledge across disciplines. The AI–human collaboration theoretical framework is applied to analyze sector-specific patterns in human-AI interaction based on professional identities, risk models and expertise traditions. Hypothetical case illustrations demonstrate practical applications. Findings The research identifies distinct human–AI collaboration models across sectors: healthcare prioritizes clinical authority and financial services implements tiered authority models based on risk profiles. Sectoral contexts significantly shape collaborative boundaries through professional traditions, regulatory environments and knowledge integration patterns. Originality/value This research contributes to innovation management theory by demonstrating how sector-specific professional identities and expertise traditions shape collaborative boundaries between human and technological agents. It offers a structured comparative framework for analyzing human–AI collaboration and provides actionable insights for designing collaborative systems that improve human capabilities within sectoral contexts.

Purpose Despite growing research on human–AI collaboration, practical methods for building effective human–AI partnerships that maximize organizational performance remain limited. The study addresses this gap by developing an applied model grounded in economic theory to optimize task allocation between humans and AI within organizations and to guide human capital investments that strategically leverage and amplify unique human strengths. Design/methodology/approach The study uses theory-based conceptual analysis combined with mathematical modeling to operationalize Ricardo's comparative advantage theory for optimizing human–AI collaboration outcomes. Mathematical models were developed to optimize task allocation and identify human capital investment priorities. Validation was conducted using simulated data of semiconductor processing technicians, comparing the comparative advantage model's performance against random task assignment baselines. Findings The comparative advantage model demonstrates 63% higher productivity compared to random task assignments when validated with semiconductor processing technician data. Results confirm that strategic alignment of human and AI capabilities through comparative advantage principles significantly enhances collective performance, validating economic theory application in AI-era human resource management (HRM). Practical implications The study equips HRM practitioners with systematic methodologies for optimizing human–AI collaboration through strategic task allocation and targeted human capital investments. The mathematical models provide actionable tools for workforce planning, training needs assessment and performance optimization in AI-integrated work environments. Originality/value This study demonstrates the first systematic application of comparative advantage theory to human–AI collaboration, introducing a fresh analytical lens for allocating tasks between humans and AI to maximize organizational effectiveness. By integrating economic theory into HRM, an area where scholarly work remains limited, this interdisciplinary study makes a novel contribution that advances theory, informs future empirical research and provides HR professionals with actionable models to prepare the future workforce in the AI era.

The rapid advancement of artificial intelligence is profoundly reshaping postgraduate education. Beyond mastering disciplinary knowledge, cultivating graduates capable of effective human–AI collaboration has become an essential goal of talent development. To address this emerging demand, this study conducts a systematic review of domestic and international research on postgraduate education and human–AI collaboration and proposes a four-dimensional core competency model comprising AI Tool Mastery, Higher-Order Cognitive Construction, Human–AI Social Collaboration, and Value-Oriented Ethical Agency. The Analytic Hierarchy Process (AHP) is further employed to determine the relative weights of these dimensions. The findings aim to offer theoretical insights and practical guidance for promoting the high-quality development of postgraduate education in the era of artificial intelligence.

<sec> <title>BACKGROUND</title> Patient recruitment remains a major bottleneck in clinical trials. Large language models (LLMs) show promise for patient–trial matching and eligibility screening, but existing work emphasizes algorithmic performance in controlled settings. Less is known about how LLMs should be embedded in multi-stakeholder recruitment workflows where human oversight, process design, and economic incentives jointly determine outcomes. </sec> <sec> <title>OBJECTIVE</title> To propose a theory-grounded conceptual framework—the LLM-Embedded Clinical Recruitment Architecture (LECRA)—that explains how LLM integration in clinical trial recruitment may influence operational and economic outcomes through intermediate mechanisms involving data complexity, LLM processing, and human–AI collaboration. </sec> <sec> <title>METHODS</title> We synthesize evidence from recent empirical and review studies on LLM-based patient–trial matching, human–AI teaming in clinical settings, and AI governance in healthcare. Socio-technical systems theory is used to justify joint optimization of technical and social subsystems, and transaction cost economics is used to interpret information-processing costs, uncertainty, and scale effects. The framework is specified as four interdependent layers, and six propositions are derived for future empirical testing. </sec> <sec> <title>RESULTS</title> LECRA distinguishes (1) data complexity, (2) LLM processing, (3) human–AI collaboration, and (4) economic impact. The framework posits that LLM value is not solely a property of model accuracy; collaboration design (e.g., teaming mode, oversight, explainability) mediates how technical capability translates into recruitment performance, and economic returns may increase with scale as fixed deployment costs are spread across cases. The propositions link complexity, teaming, and scale to expected differences in processing efficiency and cost-related outcomes. </sec> <sec> <title>CONCLUSIONS</title> This conceptual work reframes LLM-enabled recruitment as a socio-technical and economic problem, not only an algorithmic one. It offers a structured basis for empirical studies—especially quasi-experimental evaluations in organizational settings—and for practice by highlighting workflow integration and governance choices that determine whether LLM adoption realizes its potential benefits. </sec> <sec> <title>CLINICALTRIAL</title> Not applicable. This manuscript presents a conceptual framework and literature synthesis; it does not report a prospectively registered interventional clinical trial. </sec>

Organisations struggle to optimise human–AI collaboration in knowledge‐intensive decision‐making. This paper proposes the Trust–Complementarity Model of Collective Intelligence (TCM‐CI), explaining how calibrated trust and complementary capability utilisation drive superior organisational performance. Through systematic synthesis of human–AI interaction and knowledge management research, we identify three core mechanisms: (1) calibrated trust maximises collective intelligence by balancing appropriate reliance with necessary oversight, (2) complementarity–trust interaction determines optimal performance when high capability utilisation combines with appropriate trust levels and (3) dynamic feedback loops create reinforcing organisational learning cycles. The framework provides practical guidance for executives designing human–AI teams, developing trust calibration training, and establishing performance metrics. By integrating psychological trust factors with cognitive capability optimisation, this model offers actionable insights for knowledge management practitioners implementing AI‐augmented decision systems while advancing theoretical understanding of human–AI collaboration effectiveness.

The integration of AI into Cyber-Physical Systems (CPS) has transformed various domains, such as smart cities, healthcare, transportation, and industrial automation. Sustainable CPS systems aim to maximize operating efficiency, the lowest possible environmental impact, and the resource economy. Effective operation of these systems depends on human-AI cooperation, using human knowledge in concert with artificial intelligence-driven automation. Emphasizing ethical consequences, decision-making processes, and real-time modifications, this chapter examines the cooperative involvement of humans and artificial intelligence in sustainable Cyber-Physical Systems. Beginning with a definition of CPS and its sustainability objectives, the chapter follows with an examination of how artificial intelligence could improve system intelligence, resilience, and autonomy. While artificial intelligence manages data-driven innovations, anomaly detection, and predictive analytics, the focus is on cooperative frameworks whereby people provide contextual understanding, ethical judgment, and oversight. Moreover, covered in the analysis are issues including human confidence in AI-generated conclusions, security issues, and algorithmic bias. Transparency and responsibility are sought by means of strategies to enhance human-AI interaction including explainable artificial intelligence (XAI), human-in---the-loop systems, and reinforcement learning approaches. Emphasizing the importance of multidisciplinary approaches combining artificial intelligence ethics, regulatory structures, and human-centered design, the chapter ends with defining future research prospects.

In this paper, we discuss technologies and approaches based on Knowledge Graphs (KGs) that enable the management of inline human interventions in AI-assisted manufacturing processes in Industry 5.0 under potentially changing conditions in order to maintain or improve the overall system performance. Whereas KG-based systems are commonly based on a static view with their structure fixed at design time, we argue that the dynamic challenge of inline Human-AI (H-AI) collaboration in industrial settings calls for a late shaping design principle. In contrast to early shaping, which determines the system's behavior at design time in a fine granular manner, late shaping is a coarse-to-fine approach that leaves more space for fine-tuning, adaptation and integration of human intelligence at runtime. In this context we discuss approaches and lessons learned from the European manufacturing project Teaming.AI, https://www.teamingai-project.eu/, addressing general challenges like the modeling of domain expertise with particular focus on vertical knowledge integration, as well as challenges linked to an industrial KG of choice, such as its dynamic population and the late shaping of KG embeddings as the foundation of relational machine learning models which have emerged as an effective tool for exploiting graph-structured data to infer new insights.

With the rapid advancement of artificial intelligence (AI), the educational landscape is evolving, increasingly integrating AI into classrooms. In response to this trend, the present study investigates how students perceive human-AI collaboration in AI-based education. Using an online experiment where undergraduate students in the U.S. watched to a lecture delivered by an AI instructor, data were collected to explore these dynamics. The findings reveal two primary insights. First, the impact of human-AI collaboration on students’ perceptions varies by their level of self-efficacy in learning. Students with high self-efficacy hold more favorable views toward an AI instructor and AI-based education, particularly when AI-based education is facilitated by human-AI collaboration. Second, the perceived credibility of the AI instructor plays a critical mediating role in fostering positive perceptions of AI-based education. These findings offer significant implications for both research and practical applications in AI in education.

Industry 4.0 primarily centers around widespread digitalization, whereas Industry 5.0 aims to combine modern technologies with human actors, emphasizing a value-driven strategy rather than a technology-centric one. The primary goals of the Industry 5.0 paradigm, which were not emphasized in Industry 4.0, highlight the importance of not only digitizing production but also ensuring its resilience, sustainability, and human-centeredness. Industry 5.0 is a project focused on value rather than technology, aiming to promote technological transformation with a specific goal in mind. In industry 5.0, which focuses on real-world applications of AI-based technology such service robots, the usage of AI is clearly seen in several sectors like tourism, education, manufacturing, and retail. Recent research highlights the importance of interactions between humans and machines, and how they contribute to creating value by enhancing their own capacities. The primary objective of human-machine collaboration is to enhance the well-being of stakeholders, including consumers, employees, and society. This chapter focuses on human -machine collaboration, practical implementation of human-AI collaboration, review of literature on human-AEI frameworks, advantages and disadvantages of collaboration between human and AI, human- AI collaboration in education and finally comes the conclusion.

This study explores how human–AI collaboration moderates the relationship between digital transformation and Green Human Resource Management (Green HRM) within Nepal’s community colleges. Bridging digital and green HRM frameworks, it introduces a supply chain–inspired lens to examine service delivery and sustainability in public higher education. Guided by Resource-Based View (RBV) and Socio-Technical Systems Theory, the research applies a mixed-methods design combining Structural Equation Modeling (SEM) with fuzzy-set Qualitative Comparative Analysis (fsQCA) on data from 285 staff members across five Tribhuvan University–affiliated community colleges in Bagmati Province.SEM results confirm that digital transformation significantly enhances Green HRM (β = 0.48, p &lt; 0.001), and this relationship is strengthened by effective human–AI collaboration (interaction β = 0.25, p &lt; 0.001). fsQCA identifies two equifinal pathways to high Green HRM: (1) Tech + AI Synergy (high digitalization and high human–AI collaboration), and (2) Tech-Driven Path (high digitalization alone). These findings reveal that while AI augmentation enhances green outcomes, foundational digital infrastructure alone can also yield substantial sustainability gains. Theoretically, this is one of the first empirical studies to integrate digital transformation, Green HRM, and human–AI collaboration within a developing country education system, extending supply chain models from corporate to academic contexts. Practically, it provides actionable insights for Internal Quality Assurance Cells (IQACs) to align digital and green agendas and suggests that policy bodies like UGC and MoEST should embed AI-readiness and sustainability metrics into accreditation frameworks. The study underscores the importance of socio-technical alignment in enabling sustainable, tech-enabled institutional quality in Nepal’s higher education landscape.

The purpose of this study was to investigate the impact of AI-Coursera technology integration in philosophy-based French language learning on critical thinking, self-evaluation, academic resilience, and speaking skills. A quasi-experimental method was used which involved 250 students divided into two groups: an experimental group and a control group. The experimental group received a philosophy-based French language learning intervention with AI-Coursera technology, while the control group received conventional French language learning. Data analysis used MANOVA, t-test, and effect test. The results showed that the integration of GenAI-Coursera in French language learning significantly improved speaking skills, academic resilience, critical thinking skills, and self-evaluation. Improved speaking skills were evident in the use of new language, fluency, and in-depth responses to philosophical questions. Critical thinking skills were enhanced, evident in the use of critical ideas and the reinforcement of French terminology. Academic resilience was enhanced, evident in the ability to resolve conflicts or problems during the learning process. Furthermore, self-evaluation skills were evident in the ability to assess and improve speaking skills. The improvement of all competencies occurs due to GenAI-Coursera's features (discourses, reflective phases, and feedback) and philosophy-based French language teaching principles. Therefore, the use of GenAI-Coursera technology in philosophy-based French language learning not only improves speaking skills but also higher-order thinking skills. This research implies that foreign language teaching requires learning design and the use of GenAI to improve higher-order thinking skills and foreign language competencies.

Dominant frameworks structuring cybersecurity education, from the NICE Workforce Framework to K-12 Cybersecurity Learning Standards, embed humanist assumptions locating agency within individual learners, knowledge as possessed by autonomous subjects, and assessment as the measurement of isolated human performance. These assumptions prove increasingly inadequate for articulating what actually occurs in postdigital learning environments where curriculum emerges through human–AI collaboration and student understanding develops through iterative dialogue with large language models. This chapter synthesizes critical posthumanism with postdigital scholarship to develop a framework revealing what humanist perspectives systematically occlude. Through analysis of three productive tensions, agency configured as individual competence versus distributed across assemblages, knowledge framed as human possession versus relationally produced through socio-material practice, and assessment oriented toward individual performance versus assemblage effectiveness, the chapter demonstrates how posthuman lenses make visible dynamics invisible to humanist framings. Reflexive practitioner inquiry grounds these theoretical insights in the author’s own cybersecurity ethics teaching, examining curriculum design and student engagement as sites where posthuman conditions already constitute educational practice. The analysis contributes the concept of assemblage literacy, the capacity to recognize how distributed human–AI configurations shape what becomes perceivable, thinkable, and actionable, as a form of postdigital expertise increasingly central to professional practice. For postdigital research methodology, the chapter demonstrates how an “on-yet-around” orientation to technology, combined with productive tensions analysis, enables theoretical synthesis as legitimate scholarly intervention in professional domains where humanist frameworks cannot articulate emergent practices.

Abstract: Augmented Human Intelligence (AHI) represents a paradigm shift in human-AI collaboration, leveraging Generative AI, Quantum Computing, and Extended Reality (XR) to enhance cognitive capabilities, decision-making, and immersive interactions. Generative AI enables real-time knowledge augmentation, automated creativity, and adaptive learning, while Quantum Computing accelerates AI optimization, pattern recognition, and complex problem-solving. XR technologies provide intuitive, immersive environments for AI-driven collaboration, bridging the gap between digital and physical experiences. The convergence of these technologies fosters hybrid intelligence, where AI amplifies human potential rather than replacing it. This research explores AI-augmented cognition, quantum-enhanced simulations, and AI-driven spatial computing, addressing ethical, security, and societal implications of human-machine synergy. By integrating decentralized AI governance, privacy-preserving AI techniques, and brain-computer interfaces, this study outlines a scalable framework for next-generation augmented intelligence applications in healthcare, enterprise intelligence, scientific discovery, and immersive learning. The future of AHI lies in hybrid intelligence systems that co-evolve with human cognition, ensuring responsible and transparent AI augmentation to unlock new frontiers in human potential. Keywords: Augmented Human Intelligence, Generative AI, Quantum Computing, Extended Reality, XR, AI-driven Cognition, Hybrid Intelligence, Brain-Computer Interfaces, AI Ethics, AI-enhanced Learning, Spatial Computing, Quantum AI, Immersive AI, Human-AI Collaboration, Ethical AI Frameworks.