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Technologies
Special Issues
Human–AI Collaboration: Emerging Technologies and Applications
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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 8364

Special Issue Editor

Dr. Giovanni Delnevo

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Technologies is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

54 pages, 613 KB  
Article
Behavioral Lifestyle Factors Versus Medical History in Determining the Predictive Power of Machine Learning-Based Obesity Classification
by Ann Murickan and Milan Toma
Technologies 2026, 14(5), 264; https://doi.org/10.3390/technologies14050264 - 27 Apr 2026
Abstract
Obesity represents a multifactorial health condition influenced by complex interactions among behavioral, environmental, and physiological factors, yet the relative predictive importance of lifestyle behaviors versus medical history indicators remains incompletely characterized. This investigation employed a three-phase machine learning approach to systematically compare the [...] Read more.
Obesity represents a multifactorial health condition influenced by complex interactions among behavioral, environmental, and physiological factors, yet the relative predictive importance of lifestyle behaviors versus medical history indicators remains incompletely characterized. This investigation employed a three-phase machine learning approach to systematically compare the predictive power of behavioral lifestyle factors, medical history variables, and their integration for obesity classification. Phase A utilized a dedicated obesity dataset containing demographic, dietary, and lifestyle predictors to perform seven-category obesity classification, achieving 81.65% test accuracy with an optimized Random Forest ensemble and macro-averaged F1-score of 0.82. Phase B addressed binary obesity classification using health indicators from diabetes screening data, where a Gradient Boosting model with optimized decision threshold achieved 67.84% accuracy and AUC of 0.735, demonstrating substantially lower performance than behavioral predictors. Phase C integrated both feature sets into a unified model, where Gradient Boosting achieved 68.31% accuracy and AUC of 0.747, representing marginal improvement over medical history alone. Cross-validated performance comparisons revealed that behavioral lifestyle factors provided superior discriminative power compared to medical history indicators, with dedicated lifestyle predictors achieving 13.81 percentage points higher accuracy than medical indicators. Feature importance analysis confirmed that transportation mode, physical activity patterns, and dietary behaviors ranked among the most influential predictors in the combined model. These findings demonstrate that behavioral lifestyle factors constitute stronger obesity predictors than medical history variables, with implications for clinical screening strategies and public health intervention targeting that prioritize lifestyle assessment and modification programs. Full article
(This article belongs to the Special Issue Human–AI Collaboration: Emerging Technologies and Applications)
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24 pages, 527 KB  
Article
A Human–AI Collaborative Pipeline for Decision Support in Urban Development Projects Based on Large-Scale Social Media Text Analysis
by Alexander A. Kharlamov and Maria Pilgun
Technologies 2026, 14(4), 228; https://doi.org/10.3390/technologies14040228 - 14 Apr 2026
Viewed by 404
Abstract
The rapid growth of digital communication platforms has generated vast volumes of user-generated textual data and digital footprints, creating growing demand for scalable artificial intelligence systems capable of supporting evidence-based decision-making. This study proposes and evaluates a human–AI collaborative analytical pipeline for multi-class [...] Read more.
The rapid growth of digital communication platforms has generated vast volumes of user-generated textual data and digital footprints, creating growing demand for scalable artificial intelligence systems capable of supporting evidence-based decision-making. This study proposes and evaluates a human–AI collaborative analytical pipeline for multi-class sentiment and aggression analysis of large-scale social media data (N = 15,064 messages) related to an urban infrastructure project. The proposed framework integrates standard NLP preprocessing, machine learning-based classifiers, temporal aggregation, and controlled large language model (LLM)-assisted classification within a structured analytical workflow that incorporates expert validation and oversight. A stratified manual validation procedure (n = 301) demonstrated substantial inter-annotator agreement (κ = 0.70) and stable multi-class classification accuracy (80%). The results indicate that combining sentiment polarity and aggression detection as complementary linguistic indicators improves sensitivity to shifts in discourse dynamics and enables early identification of emerging social tension. The study demonstrates the potential of human–AI collaborative analytical frameworks for transparent, interpretable, and predictive large-scale social media analysis in decision-support contexts. Full article
(This article belongs to the Special Issue Human–AI Collaboration: Emerging Technologies and Applications)
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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
Cited by 7 | Viewed by 6763
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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