Human–AI Collaboration: Emerging Technologies and Applications

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Assistive Technologies".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 585

Special Issue Editor


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Guest Editor
Department of Computer Science and Engineering, Alma Mater Studiorum Università di Bologna, 40126 Bologna, Italy
Interests: human–AI interaction; machine learning; human–computer interaction
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Special Issue Information

Dear Colleagues,

As artificial intelligence (AI) systems evolve, the synergy between humans and intelligent technologies is becoming central to innovation across various domains. Human–AI collaboration aims to leverage the strengths of both human cognition and machine intelligence, resulting in enhanced decision-making, creativity, productivity, and problem-solving capabilities. This Special Issue seeks to explore emerging technologies, theoretical frameworks, practical applications, and interdisciplinary approaches that enable effective collaboration between humans and AI.

We invite original research, case studies, and review articles covering the following topics: co-creative AI systems; human-in-the-loop learning; explainable and trustworthy AI; adaptive interfaces; AI-augmented decision support; human–robot interaction; collaborative autonomous systems; and human–AI teaming. Contributions from fields including computer science, cognitive science, HCI, robotics, and psychology are encouraged.

By bringing together diverse perspectives and innovations, this Special Issue aims to provide a comprehensive understanding of how humans and AI can effectively collaborate in real-world environments ranging from education and healthcare to manufacturing, finance, and various creative industries.

We look forward to receiving your contributions.

Dr. Giovanni Delnevo
Guest Editor

Manuscript Submission Information

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Keywords

  • human–AI collaboration
  • co-creative AI systems
  • human-in-the-loop learning
  • explainable AI (XAI)
  • trustworthy AI
  • human–robot interaction
  • collaborative decision-making
  • intelligent user interfaces
  • adaptive systems

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Published Papers (1 paper)

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Research

27 pages, 1938 KB  
Article
Generative AI and Cognitive Challenges in Research: Balancing Cognitive Load, Fatigue, and Human Resilience
by Syed Md Faisal Ali Khan and Salem Suhluli
Technologies 2025, 13(11), 486; https://doi.org/10.3390/technologies13110486 - 28 Oct 2025
Viewed by 58
Abstract
This study examines the interaction between cognitive demands and generative artificial intelligence (GenAI) technologies in shaping the quality and influence of academic research. While GenAI tools such as ChatGPT and Elicit are increasingly adopted to ease information processing and automate repetitive tasks, their [...] Read more.
This study examines the interaction between cognitive demands and generative artificial intelligence (GenAI) technologies in shaping the quality and influence of academic research. While GenAI tools such as ChatGPT and Elicit are increasingly adopted to ease information processing and automate repetitive tasks, their broader impact on researchers’ cognitive performance remains underexplored. Using data from 998 researchers and applying structural equation modeling (SEM-PLS), we examined the effects of cognitive load, task fatigue, and resilience on research outcomes, with GenAI immersion as a higher-order moderator. Results reveal that both cognitive load and fatigue negatively affect research quality, while engagement and resilience offer partial protection. Unexpectedly, high immersion in GenAI intensified the negative impact of cognitive strain, suggesting that over-reliance on AI can amplify mental burden rather than reduce it. These results enhance the design and responsible integration of AI technologies in academic environments by demonstrating that sustainable adoption necessitates a balance between efficiency and human creativity and resilience. The study provides evidence-based insights for researchers, institutions, and policymakers seeking to optimize AI-supported workflows without compromising research integrity or well-being. Full article
(This article belongs to the Special Issue Human–AI Collaboration: Emerging Technologies and Applications)
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