Pdf Understanding Human Ai Collaboration A Systematic Review Of Spring

Bonisiwe Shabane

-Nov 29, 2025, 9:14 PM

pdf understanding human ai collaboration a systematic review of spring

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Aman, Fadhilah and Abdul Hamid, Siti Rohani and Mat Isa, Azman and Mohamad, Muslim Har Sani (2025) A systematic review of trends in human–AI collaboration research.

International Journal of Technology, Knowledge, and Society, 21 (1). pp. 190-217. ISSN 1832-3669 E-ISSN 2835-0391 Collaboration of artificial intelligence (AI) with humans in achieving efficient business processes and decision-making is a rapidly evolving research area. This study explores current research trends in human–AI team collaboration, with a specific focus on its role in enhancing decision-making processes within organizations.

Using a systematic literature review of seventy-five articles published between 2018 and 2024 from the Scopus and Web of Science databases, the study employed content analysis to identify themes and subthemes related to human–AI... The analysis revealed twelve key themes, including human–AI collaboration, organizational dynamics, decision-making and problem-solving, trust and reliance on AI, AI effectiveness and evaluation, and ethical considerations in AI, among others. Among these, trust, task delegation, evaluation, and feedback and communication emerged as the most trending topics, with task delegation identified as the highest trending subtheme. The findings underscore that human–AI collaboration extends beyond technological integration to involve structured frameworks, such as division of labor configurations and the “Human–AI Collaboration” (HACO) taxonomy, that facilitate task allocation, emphasize trust, and clarify... These frameworks enable organizations to optimize human–AI partnerships, combining AI’s data-driven precision with human creativity and empathy. The critical role of trust calibration, transparency, and reliability in fostering effective collaboration and ensuring user confidence in AI systems is also highlighted.

This study lays a foundational understanding of human–AI collaboration, bridging the gap between theory and practice, and identifies opportunities for future research to explore scalable strategies for trust and transparency, the intersection of these... © Nov 2017 - Powered by APW Themes & Theme by Agung Prasetyo Wibowo. 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: Lecture Notes in Business Information Processing ((LNBIP,volume 549)) Included in the following conference series: The function and role of AI in collaborating in future work scenarios has received a great deal of attention across industries, but there has been limited work on assessing the current state of research...

To fill this gap, we conducted a systematic review of 43 research papers on human-AI collaboration in the field of Information Systems. Our analysis focuses on conceptualization of human-AI collaboration, collaboration mechanism, human and AI characteristics in the literature. Based on the findings, we developed a conceptual framework that highlights the interrelationships between the four parts. Finally, we identify research gaps and propose future research directions for further exploration in this expanding research area. This is a preview of subscription content, log in via an institution to check access. Tax calculation will be finalised at checkout

Pdf Understanding Human Ai Collaboration A Systematic Review Of Spring

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