Human Ai Collaboration Synergies And Challenges Springer

Part of the book series: International Series on Computer, Entertainment and Media Technology ((ISCEMT)) Co-Creation and Augmentation: Human-AI collaboration offers unprecedented opportunities for co-creation and augmentation, where AI systems work alongside humans to enhance productivity and creativity. This chapter explores the various ways in which humans and AI can collaborate, highlighting real-world examples and case studies. It discusses the benefits of human-AI collaboration, from improved decision-making to innovative problem-solving.Ethical and Practical Considerations: While human-AI collaboration holds great promise, it also presents ethical and practical challenges. This chapter examines the ethical considerations of human-AI collaboration, including issues of autonomy, accountability, and trust. It discusses practical strategies for ensuring that human-AI collaboration is conducted ethically and effectively, fostering a relationship that benefits both humans and AI systems.

This is a preview of subscription content, log in via an institution to check access. Tax calculation will be finalised at checkout © 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2529)) Included in the following conference series: Research on collaboration between human and non-human intelligent agents is extensive, yet a deeper understanding of the specific management and organizational challenges and the research methods used to study them is still needed.

A systematic literature review was conducted following methodological guidelines to address this gap, with the PRISMA 2020 flow diagram used to document the process. This study explores two key research questions: (RQ1) What specific management and organizational challenges are investigated in human-AI collaboration? (RQ2) What research methods are commonly employed in this field? Data were retrieved from Scopus (486 documents) and Web of Science (385 documents), resulting in 95 studies in the final analysis. The review identifies five key categories of management and organizational challenges: (1) Hybrid human-AI teams, (2) AI integration in work processes, (3) Human-AI decision-making, (4) Trust in AI, and (5) Human-robot collaboration (HRC). Quantitative methods were more frequently used than qualitative approaches, with experimental studies—particularly simulation designs—being the most common.

In qualitative research, grounded theory and case study designs were equally prominent. The findings contribute to the ongoing discourse on human-AI collaboration by mapping key challenges and methodological trends, providing insights for future research and organizational practice . This is a preview of subscription content, log in via an institution to check access. Tax calculation will be finalised at checkout Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 586)) Collaboration between humans and artificial intelligence (AI) has become increasingly prevalent as technology continues to advance.

This partnership holds immense potential in various fields, revolutionizing industries and transforming the way we work, make decisions, and interact with machines. The benefits of human-AI collaboration are multifaceted and far-reaching, offering unprecedented opportunities for innovation, efficiency, and problem-solving. One of the key advantages of human-AI collaboration is the ability to leverage the complementary strengths of both entities. AI systems possess unparalleled computational power and data processing capabilities, enabling rapid analysis of vast amounts of information. In contrast, humans excel in critical thinking, creativity, emotional intelligence, and complex decision-making. By combining these unique attributes, human-AI teams can achieve superior outcomes that surpass those achievable by either entity alone.

This is a preview of subscription content, log in via an institution to check access. Tax calculation will be finalised at checkout Antebi, L.: The Global Status of Artificial Intelligence. In artificial intelligence and national security in Israel (pp. 63–72). Institute for National Security Studies, (2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2134)) Included in the following conference series: A growing body of interdisciplinary literature indicates that human decision-making processes can be enhanced by Artificial Intelligence (AI). Nevertheless, the use of AI in critical domains has also raised significant concerns regarding its final users, those affected by the undertaken decisions, and the broader society. Consequently, recent studies are shifting their focus towards the development of human-centered frameworks that facilitate a synergistic human-machine collaboration while upholding ethical and legal standards. In this work, we present a taxonomy for hybrid decision-making systems to classify systems according to the type of interaction that occurs between human and artificial intelligence.

Furthermore, we identify gaps in the current body of literature and suggest potential directions for future research. This is a preview of subscription content, log in via an institution to check access. Tax calculation will be finalised at checkout Humanities and Social Sciences Communications volume 12, Article number: 821 (2025) Cite this article 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’... 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... These findings have implications for developing more effective human-AI classroom collaboration and promoting the effective integration of AI into teaching and learning. The rapid development of artificial intelligence (AI) technology has brought unprecedented opportunities and challenges to the education sector. Particularly, under the impetus of human–AI collaboration, AI not only serves as an auxiliary tool to support teachers’ instructional tasks but also actively participates in classroom interactions in an intelligent manner, facilitating a profound...

2024; Zhou and Hou, 2024; Hilpert et al. 2023). Traditional educational models have limitations in facilitating personalized learning and optimizing resource allocation. Human–AI collaborative teaching offers an efficient and sustainable lens to address these challenges through close collaboration between AI systems and teachers (Díaz and Nussbaum, 2024; Chen et al. 2022). Human-AI collaboration in education, as an emerging interdisciplinary field, integrates cutting-edge theories and technologies from AI, education, cognitive science, and human–computer interaction.

The Hybrid Intelligence Learning Environments design aims to develop and implement effective human–AI collaboration in education. Its core philosophy lies in the seamless integration of human intelligence and machine intelligence to achieve optimized teaching outcomes through their synergistic collaboration (Cukurova, 2024; Bredeweg and Kragten, 2022). Within this environment, AI not only functions as an assistant to teachers but also plays a vital role in personalized learning (Mittal et al. 2024), real-time feedback (Weber et al. 2024), cognitive intervention (Fan et al. 2024), and emotional engagement (Järvelä et al.

2023). In recent years, researchers started to look into the design of educational robots, the application of AI in teaching, and the cognitive and behavioural dynamics of human–AI collaboration (Schecter et al. 2022; Niu et al. 2024; Wu et al. 2024). Existing studies have demonstrated significant advantages of human–AI collaboration in enhancing learning efficiency and supporting personalized learning (Huang et al.

2021). For instance, the integration of AI applications in real-time question answering (Fang et al. 2023) and homework grading (Duan et al. 2023) has effectively reduced teachers’ workload while improved instructional quality. The most extensive literature on AI applications in education focused on technological aspects, lacking systematic examination of human-AI collaboration during classroom instruction (Vössing et al. 2022; Yue and Li, 2023).

Given that effective human-AI collaboration relies on effective collaboration between humans and AI systems, investigating the effectiveness of collaboration and the degree of synergy in classrooms becomes crucial. To address this research gap, this study aims to develop a framework to evaluate and enhance the synergy and orderliness of human–AI collaboration in classrooms. In previous chapters, we outlined how AI can transform DevOps practices—from coding, testing, infrastructure, and data provisioning to CI/CD orchestration—culminating in autonomous multiagent systems (Chapter 10). Yet the journey to a NoOps environment is not just about technology. It also demands a cultural and organizational shift in how humans work alongside AI agents. This is a preview of subscription content, log in via an institution to check access.

Tax calculation will be finalised at checkout © 2025 The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature Vorel, R. (2025). Human–AI Collaboration. In: NoOps.

Apress, Berkeley, CA. https://doi.org/10.1007/979-8-8688-1694-9_11 Nature Human Behaviour volume 8, pages 2293–2303 (2024)Cite this article Inspired by the increasing use of artificial intelligence (AI) to augment humans, researchers have studied human–AI systems involving different tasks, systems and populations. Despite such a large body of work, we lack a broad conceptual understanding of when combinations of humans and AI are better than either alone. Here we addressed this question by conducting a preregistered systematic review and meta-analysis of 106 experimental studies reporting 370 effect sizes.

We searched an interdisciplinary set of databases (the Association for Computing Machinery Digital Library, the Web of Science and the Association for Information Systems eLibrary) for studies published between 1 January 2020 and 30... Each study was required to include an original human-participants experiment that evaluated the performance of humans alone, AI alone and human–AI combinations. First, we found that, on average, human–AI combinations performed significantly worse than the best of humans or AI alone (Hedges’ g = −0.23; 95% confidence interval, −0.39 to −0.07). Second, we found performance losses in tasks that involved making decisions and significantly greater gains in tasks that involved creating content. Finally, when humans outperformed AI alone, we found performance gains in the combination, but when AI outperformed humans alone, we found losses. Limitations of the evidence assessed here include possible publication bias and variations in the study designs analysed.

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