Medea in the age of AI: reimagining the monstrous mother through adaptation and technology

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.

In recent years, Human AI Collaboration has become an exciting new approach to IT systems design that is designed to balance automation and human expertise. Specifically, this paper investigates a broad framework of smart scenario co-creation with IT systems in general, where human and AI work together in dynamically sharing IT tasks, AI provides decision tools for augmentation, and mutual performance is optimized by dynamically adjusting learning parameters. The research employs a mixed method, and the case studies together with the surveys and the quantitative data analysis are used to assess the existing collaboration models. We find that hybrid teams, consisting of both AI agents and human experts, increase productivity by up to 40% when executing iterative design processes. In addition, the study provides important insights regarding the critical success factors such as adaptive system interfaces, trust building mechanisms and the skill augmentation strategies. This information presents a path for overcoming ubiquitous challenge in utilizing collaborative frameworks, such as technological misalignment and user resistance. The proposed framework is intended to enable replication of such integration in the real time IT environment offering flexibility, scalability and long-term efficiency. Second, this research adds to the expanding repository of knowledge in terms of human centered AI development and offers IT leaders practical approaches to take advantage of human AI synergy for innovation and competitiveness.

Introduction. Collaborative human-AI annotation is a promising approach for various tasks with large-scale and complex data. Tools and methods to support effective human-AI collaboration for data annotation are an important direction for research. In this paper, we present CHAIRA: a Collaborative Human-AI Risk Annotation tool that enables human and AI agents to collaboratively annotate online incivility. Method. We leveraged Large Language Models (LLMs) to facilitate the interaction between human and AI annotators and examine four different prompting strategies. The developed CHAIRA system combines multiple prompting approaches with human-AI collaboration for online incivility data annotation. Analysis. We evaluated CHAIRA on 457 user comments with ground truth labels based on the inter-rater agreement between human and AI coders. Results. We found that the most collaborative prompt supported a high level of agreement between a human agent and AI, comparable to that of two human coders. While the AI missed some implicit incivility that human coders easily identified, it also spotted politically nuanced incivility that human coders overlooked. Conclusions. Our study reveals the benefits and challenges of using AI agents for incivility annotation and provides design implications and best practices for human-AI collaboration in subjective data annotation.

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.

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

Human–AI collaboration has attracted interest from both scholars and practitioners. However, the relationships in human–AI teamwork have not been fully investigated. This study aims to research the influencing factors of trust in AI teammates and the intention to cooperate with AI teammates. We conducted an empirical study by developing a research model of human–AI collaboration. The model presents the influencing mechanisms of interactive characteristics (i.e., perceived anthropomorphism, perceived rapport, and perceived enjoyment), environmental characteristics (i.e., peer influence and facilitating conditions), and personal characteristics (i.e., self-efficacy) on trust in teammates and cooperative intention. A total of 423 valid surveys were collected to test the research model and hypothesized relationships. The results show that perceived rapport, perceived enjoyment, peer influence, facilitating conditions, and self-efficacy positively affect trust in AI teammates. Moreover, self-efficacy and trust positively relate to the intention to cooperate with AI teammates. This study contributes to the teamwork and human–AI collaboration literature by investigating different antecedents of the trust relationship and cooperative intention.

Purpose This paper aims to conceptualize “synthetic ethics” as a theoretical framework within posthuman leadership contexts, examining how algorithmic systems mediate ethical reasoning in organizations. Design/methodology/approach A narrative literature review methodology synthesizes insights from posthumanist theory, business ethics and AI governance to develop an understanding of synthetic ethics in posthuman leadership contexts. Findings This study argues that algorithmic governance can be understood as generating hybrid forms of moral agency through human–AI interaction that extend beyond conventional anthropocentric ethics. Leadership functions increasingly emerge from human–AI assemblages, challenging traditional notions of responsibility, accountability and ethical decision-making. Practical implications Organizations must develop new governance structures, leadership competencies and ethical frameworks to navigate posthuman leadership arrangements. This includes implementing transparency mechanisms, such as periodic algorithmic audits or bias-impact reviews, reconfiguring decision-making processes and cultivating ethical awareness for human–AI collaboration. Originality/value This paper introduces “synthetic ethics” as a novel theoretical construct for understanding the emergent ethical frameworks arising from human–AI collaborations in leadership roles. It contributes to business ethics by addressing the anthropocentric bias in existing frameworks and proposing more inclusive approaches that accommodate the distributed nature of agency in algorithmically governed organizations.

Human-AI collaboration (HAIC) is emerging as a pivotal approach in various sectors, enhancing productivity, creativity, and decision-making. This journal explores the current state of HAIC, its future prospects, and ethical considerations, emphasizing the importance of quality in collaboration. Quality in HAIC encompasses the effectiveness, reliability, and ethical implications of AI systems in collaborative environments. As organizations increasingly integrate AI into their workflows, understanding the dynamics of HAIC becomes essential for maximizing benefits while addressing challenges. This exploration aims to provide insights into how human-AI collaboration can be optimized for better outcomes in diverse fields. Keywords: Human-AI collaboration, AI ethics, productivity, quality, decision-making, workforce transformation.

This commentary explores three fundamental premises surrounding the human-AI partnership. First, a human-AI collaboration is perhaps superior to either working independently, as AI enhances human capabilities but requires oversight to ensure ethical and accurate outcomes. Second, AI's effectiveness is limited by the quality and biases of its training data, which underscores the need for diverse, unbiased datasets. Without proper data, AI could perpetuate flawed or biased decisions, impacting areas such as hiring, healthcare, and empathy-driven interactions. Finally, generative AI is prone to “hallucinations,” where it produces plausible yet incorrect outputs. These errors pose significant risks in high-stakes sectors like healthcare and security. As AI becomes more ingrained in society, these challenges raise ethical concerns around job displacement, loss of human autonomy, and biased decision-making. Here, we also examine the implications of AI hallucinations and model collapse, stressing the importance of continuous human intervention to mitigate AI-driven inaccuracies. Ultimately, a balanced partnership between human judgment and AI's scalability, along with rigorous oversight, is necessary to unlock AI's potential while safeguarding societal values.

Purpose: This paper proposes structured frameworks for effective Human-AI collaboration within business processes. It aims to identify and model optimal task divisions where humans contribute oversight, creativity, and strategic judgment while AI provides computational power, automation, and analytical insights. Methodology: We explore collaboration models based on role-based division, process integration, and task adaptability. We analyze real-world business applications to demonstrate the efficacy of these models in improving productivity, decision-making, and innovation. Findings: We propose three key frameworks: (1) Augmented Creativity, where AI enhances human ideation, (2) Hybrid Decision Systems, where AI assists human judgment through predictive insights, and (3) Oversight-Driven Automation, where humans maintain control over automated tasks. Implications: The study highlights pathways for achieving synergistic Human-AI interactions to optimize business outcomes, enhance agility, and ensure ethical AI deployment

AI has gained significant traction in manufacturing, offering tremendous potential for enhancing production efficiency, cost reduction, and safety improvements. Consequently, developing AI-based software platforms that facilitate collaboration between human operators and AI services is crucial. However, integrating the different stakeholder perspectives into a common framework is a complex process that requires careful consideration. Our research has focused on identifying the individual relevance of varying quality characteristics per stakeholder toward such a software platform. Therefore, this work proposes an overview on the vital success factors related to human-AI teaming that can be used to measure fulfillment.

Purpose Most prior studies have primarily investigated AI adoption, with less attention given to AI assimilation in human resource management (HRM). Additionally, prior studies often lack empirical verification of the extent to which human–AI collaboration might alleviate challenges and promote AI assimilation in the HRM context. Thus, this study aims to explore AI assimilation in recruitment with a balanced view that identifies both enabling and inhibiting factors while examining the role of human–AI collaboration in mitigating the effects of inhibiting factors. Design/methodology/approach We used a mixed-method approach. Using an open-ended survey questionnaire approach and collecting data from 26 HR professionals, we identified five factors, namely, AI competency, recruitment agility, AI opacity, AI empathy and human–AI collaboration, potentially impacting AI assimilation. Thereafter, drawing from the enabler–inhibitor perspective, we theorize that AI competency and recruitment agility are the enablers, whereas AI opacity and AI empathy are the inhibitors of an organization’s efforts to assimilate AI in recruitment practices. We tested our proposed model by collecting data from 309 HR professionals. Findings The findings showed that both enablers, AI competency and recruitment agility, significantly influence AI assimilation; however, both inhibitors, AI opacity and AI empathy, are non-significant for AI assimilation. While looking into the reasons for these non-significant effects, we observed that the interaction term between AI empathy and human–AI-collaboration as well as between AI opacity and human–AI-collaboration both had significant effects on AI assimilation. These interaction effects suggest that human–AI collaboration mitigates the constraining impact of both inhibitors. Originality/value Drawing from the enabler–inhibitor perspective and by empirically testing our proposed model, this paper significantly contributes to the IS literature. Our study not only identifies factors that promote and inhibit AI assimilation in the context of HRM practices but also reveals how human–AI collaboration may mitigate the effects of inhibitors. Our findings suggest that organizations should have a collaborative recruitment environment where AI handles repetitive tasks, and humans focus on roles requiring emotional intelligence. This approach enhances the integration of AI-powered tools, addresses AI assimilation inhibitors and optimizes recruitment effectiveness.

This meta-analysis of 146 experiments in the healthcare and public sectors examines human–AI synergy versus augmentation amid substantial heterogeneity. We find that AI augmentation reliably improves human performance (Hedges’ g = 0.622), whereas synergy effects are generally negative, with AI alone often outperforming human–AI teams (Hedges’ g = −0.380), although publication bias favours positive augmentation results. Additionally, task type, AI transparency, and user expertise significantly moderate outcomes. These results caution against assuming inherent benefits of human–AI collaboration and instead support selective automation of structured tasks with human oversight for ethically complex decisions, guiding policymakers and leaders in optimizing human–AI integration.

Trust is one of the principles that human-AI teams must attain for the fulfillment of their mission. Ex-plainable AI and the principle of computational re-liabilism provide AI-intrinsic solutions for trust management. When human-AI collaboration breaks down, human-AI teams turn to common sense and intuition to recover trust. In addition, research on earlier innovations has shown that institutional and organizational mechanisms such as citizen adviso-ry boards and standardization promote trust. This paper sketches a framework for deployable and actionable trust management mechanisms. To that end, it will: (1) Identify three dimensions of trust. (2) Examine the role of heterogeneous stakehold-ers in human-AI systems. (3) Address the links among interpersonal trust, institutional trust, and trust in algorithms. (4) Suggest that stakeholder heterogeneity is a multi-level and multi-faceted imperative for establishing trust in human-AI teams.