Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
6.5
Journals
AI
Special Issues
Human–AI Collaboration and Augmented Intelligence: Bridging Theory, Design and Practice
share announcement

Human–AI Collaboration and Augmented Intelligence: Bridging Theory, Design and Practice

A Special Issue of AI (ISSN 2673-2688).

Deadline for manuscript submissions: 31 December 2026 | Viewed by 5201

Editors

Dr. Phivos Mylonas

E-Mail Website
Guest Editor
Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece
Interests: artificial intelligence; context data representation; data science; information retrieval; machine learning; personalisation technologies; semantics; user modelling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ioannis Voyiatzis

E-Mail Website
Guest Editor
Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece
Interests: dependability and security of digital systems; machine learning and AI algorithms; accelerator design for AI; secure and blockchain systems; unmanned aerial vehicles (drones)
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nicolas Tsapatsoulis

E-Mail Website
Guest Editor
Department of Communication and Marketing, Cyprus University of Technology, 3036 Limassol, Cyprus
Interests: multimedia information retrieval; web data mining; recommender systems; collective intelligence and affective computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue invites submissions that explore the next generation of human–AI collaboration systems and augmented intelligence, aligning with the HAICAI 2026 (https://haicai2026.uniwa.gr/) conference themes. It focuses on the co-design of AI and human capabilities, ranging from interactive machine learning, explainable decision support, and human-centric interface design to ethical, trustworthy, and adaptive architectures that enhance human productivity, creativity, and decision making. Manuscript topics may include hybrid intelligence systems (humans + AI), transparency and interpretability in collaborative settings, human-in-the-loop learning, adaptive/assistive agents, multi-modal interaction, socio-technical evaluation, user-centered trust models, and the deployment of collaborative AI in real-world domains. Our goal is to bring together theoretical advances, novel system implementations, user studies, and ethical frameworks under a unified umbrella. Submissions may originate from HAICAI authors or related research to extend and deepen their contributions. This Special Issue will strengthen the connection between the HAICAI community and the broader audience for AI research and foster cross-pollination between academic, industrial, and human-centered design perspectives.

Dr. Phivos Mylonas
Prof. Dr. Ioannis Voyiatzis
Prof. Dr. Nicolas Tsapatsoulis
Guest Editors

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AI 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
  • augmented intelligence systems
  • explainable artificial intelligence (XAI)
  • human-in-the-loop machine learning
  • adaptive and interactive ai applications

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

32 pages, 1371 KB  
Article
The AI Literacy Leadership Framework (AILLF): A Framework for AI-Enabled Leadership in Higher Education
by Alaa Mohasseb, Ronel Beukman and Andreas Kanavos
AI 2026, 7(9), 328; https://doi.org/10.3390/ai7090328 - 26 Aug 2026
Viewed by 755
Abstract
The integration of Artificial Intelligence (AI) in higher education is reshaping institutional decision-making, governance, policy development, and educational innovation. Effective leadership in AI-enabled environments requires more than technical competence, extending to strategic awareness, ethical judgement, and the ability to critically evaluate the broader [...] Read more.
The integration of Artificial Intelligence (AI) in higher education is reshaping institutional decision-making, governance, policy development, and educational innovation. Effective leadership in AI-enabled environments requires more than technical competence, extending to strategic awareness, ethical judgement, and the ability to critically evaluate the broader implications of AI technologies. Drawing on survey and interview data from 52 academic leaders across UK higher education institutions, this study examines current levels of AI literacy and explores how AI capability relates to institutional readiness and leadership practice. The findings reveal variation in participants’ self-reported AI literacy and engagement, identify technical, strategic, ethical, and organisational capability gaps, and highlight structural barriers including limited time, fragmented professional development, and insufficient institutional support for institution-wide AI adoption. In response, the paper presents the AI Literacy Leadership Framework (AILLF), which conceptualises AI literacy as a multidimensional leadership capability comprising technical, strategic, ethical, and applied dimensions that support four interconnected leadership domains: innovation, decision-making, ethical governance, and policy development. Informed by leadership theory, international AI governance frameworks, and the study’s exploratory empirical findings, the AILLF is accompanied by a proposed capability progression model and role-differentiated leadership competency guide to provide implementation guidance for higher education institutions. The study contributes an empirically informed conceptual framework that integrates AI literacy, leadership theory, and AI governance, providing a foundation for future research and institutional approaches to AI leadership within higher education. Full article
(This article belongs to the Special Issue Human–AI Collaboration and Augmented Intelligence: Bridging Theory, Design and Practice)
►▼ Show Figures

Figure 1

20 pages, 1163 KB  
Article
Smart Evidence Management: Concept, Design and Evaluation of an AI-Assisted Approach for Automotive SPICE Assessments
by Rüdiger Heimgärtner
AI 2026, 7(7), 270; https://doi.org/10.3390/ai7070270 - 21 Jul 2026
Viewed by 564
Abstract
Automotive SPICE® (ASPICE) conformance activities require systematic discovery and evaluation of project documentation against standardised process indicators, consuming an estimated 40–60% of assessment preparation time with high extraneous cognitive load. This paper presents Smart Evidence Management (SEM), a principled Human–AI Collaboration (HAIC) [...] Read more.
Automotive SPICE® (ASPICE) conformance activities require systematic discovery and evaluation of project documentation against standardised process indicators, consuming an estimated 40–60% of assessment preparation time with high extraneous cognitive load. This paper presents Smart Evidence Management (SEM), a principled Human–AI Collaboration (HAIC) approach grounded in Cognitive Load Theory, Trust in Automation, and Situation Awareness theory. SEM defines four design principles—AI-assisted discovery, expert-validated judgement, transparent reasoning, and full data sovereignty—as a novel generalisable HAIC pattern for documentary evidence evaluation in regulated professional domains. By reducing evidence-hunting effort, SEM operationalises the Plan-Do-Check-Act (PDCA) continuous improvement principle, enabling iterative conformance gap detection throughout development rather than only at formal assessment events. The Smart Evidence Manager, a prototype instantiation for ASPICE Process Assessment Model (PAM) v4.0, implements a two-stage hybrid symbolic–neural Retrieval-Augmented Generation (RAG) architecture with fully local Large Language Model (LLM) inference. An expert agreement study (n = 120 findings, five intacs® certified assessors) yielded a combined acceptance rate of 90.0% (95% Confidence Interval (CI) [83.3%, 94.2%]). A beta-test study is under recruitment (target n ≥ 30 practitioners) to quantify cognitive augmentation effects, usability, and trust calibration. Cultural dimensions of trust calibration are discussed, extending SEM to intercultural deployment contexts. Full article
(This article belongs to the Special Issue Human–AI Collaboration and Augmented Intelligence: Bridging Theory, Design and Practice)
►▼ Show Figures

Figure 1

14 pages, 946 KB  
Article
Towards HCXAI: Explainability Preferences of Healthcare Professionals
by Mishell Cadena-Yanez, Angela Bernardini and Marisol Gómez
AI 2026, 7(7), 247; https://doi.org/10.3390/ai7070247 - 2 Jul 2026
Viewed by 685
Abstract
This study examined how healthcare professionals (HCP) perceive and trust AI in clinical settings, and what kinds of explanations they need to use it effectively. Using interviews with six HCPsand a questionnaire (N=41), the findings show that trust was [...] Read more.
This study examined how healthcare professionals (HCP) perceive and trust AI in clinical settings, and what kinds of explanations they need to use it effectively. Using interviews with six HCPsand a questionnaire (N=41), the findings show that trust was higher for administrative tasks and lower for direct clinical decisions, regardless of their prior AI or clinical experience or AI literacy. Clinical validation and algorithmic bias ranked above explainability as trust factors, indicating that explainability is not a primary trust-building mechanism. However, HCP consistently demanded explanations as a tool to support their own clinical reasoning, preferring to receive them across all clinical contexts rather than only under disagreement with the system, and valued grounding in medical evidence and consistency with clinical protocols over clarity or simplicity. These findings argue for an HCXAI design approach that treats explainability as a critical-reasoning tool rather than a primary trust mechanism. Full article
(This article belongs to the Special Issue Human–AI Collaboration and Augmented Intelligence: Bridging Theory, Design and Practice)
►▼ Show Figures

Figure 1

16 pages, 319 KB  
Article
When Algorithms Create Culture: An Integrative Model of Consumer Acceptance of AI-Generated Music
by Panagiotis Douros, Konstantinos Kasaras and Konstantinos Milioris
AI 2026, 7(6), 212; https://doi.org/10.3390/ai7060212 - 11 Jun 2026
Viewed by 1365
Abstract
Background: The rapid advancement of generative artificial intelligence is transforming music composition from an exclusively human-centric activity into a hybrid human–algorithmic domain. Despite technological progress and growing commercial integration, consumer acceptance of AI-generated music remains empirically underexplored. Methods: This study formulates and empirically [...] Read more.
Background: The rapid advancement of generative artificial intelligence is transforming music composition from an exclusively human-centric activity into a hybrid human–algorithmic domain. Despite technological progress and growing commercial integration, consumer acceptance of AI-generated music remains empirically underexplored. Methods: This study formulates and empirically evaluates a multidimensional theoretical model integrating nine frameworks—including UTAUT2, parasocial interaction theory, anthropomorphism theory, authenticity theory, and innovation resistance theory—through a quantitative cross-sectional survey of 466 young adults aged 17–28. Confirmatory factor analysis and multiple regression analysis (with robust standard errors) were employed. Results: The model explained 63.6% of the variance in behavioral intention (R2 = 0.636). Five constructs emerged as significant predictors: hedonic motivation (β = 0.136, p = 0.017), parasocial relationships (β = 0.121, p = 0.002), social influence (β = 0.126, p = 0.002), performance expectancy (β = 0.102, p = 0.019), and innovation resistance (β = −0.089, p = 0.029). Authenticity concerns, ethical AI concerns, anthropomorphic perceptions, and technological substitution fears were non-significant in the multivariate model. Conclusions: Young consumers’ acceptance of AI-generated music is primarily driven by experiential, social, and relational factors rather than ethico-cultural concerns. These findings have substantive implications for creative industries navigating algorithmic cultural production. Full article
(This article belongs to the Special Issue Human–AI Collaboration and Augmented Intelligence: Bridging Theory, Design and Practice)
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop