Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.4
3.8
Journals
Systems
Special Issues
Human-AI (H-AI) Teams: Designing for Human-AI Interactions
share announcement

Human-AI (H-AI) Teams: Designing for Human-AI Interactions

A special issue of Systems (ISSN 2079-8954).

Deadline for manuscript submissions: 31 December 2026 | Viewed by 4159

Editors

Dr. John Robert Turner

E-Mail Website
Guest Editor
College of Education and Human Development, Texas A&M University, 537 Harrington Hall, College Station, TX 77843-4226, USA
Interests: team science; complexity; leadership; decision-making; human-AI teams
Special Issues, Collections and Topics in MDPI journals
Dr. Rose Baker

E-Mail Website
Guest Editor
Department of Learning Technologies, College of Information, University of North Texas, Denton, TX 76207, USA
Interests: impact of career and technology education; performance improvement; survey and evaluation design; evaluation; training and development

Special Issue Information

Dear Colleagues,

With the integration of artificial intelligence (AI) into the workplace, and with today’s workplace being structured around teams, the integration of AI and teams is a fairly new phenomenon that warrants further discovery. This Special Issue focuses on Human-AI (H-AI) teams. H-AI teams can be structured along a continuum, with full human control supplemented by AI technologies on one end, to having AI technologies having complete control over human teams.

This Special Issue particularly looks forward to articles presenting, among others, the following:

  • New design principles and interaction strategies necessary to support H-AI teams.
  • Empirical studies on H-AI teams.
  • Techniques for supplementing AI technologies in human team interactions.
  • Ethical issues that need to be addressed when integrating AI technologies with human teams.
  • Impacts of different H-AI team interaction strategies.
  • Explainable AI (XAI) techniques necessary to transfer data into information for H-AI teams when making decisions.
  • Communication strategies for H-AI teams.

Dr. John Robert Turner
Dr. Rose Baker
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. Systems 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 2400 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

  • H-AI teams
  • HAT
  • AI
  • teams
  • human-AI interaction
  • explainable AI

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 (3 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

Jump to: Other

22 pages, 879 KB  
Article
Designing Human–AI Collaboration for Hybrid Intelligence in Immersive Learning Environments: A Conceptual Framework
by Chih-Pu Dai, Mohan Yang and Sumi Lee
Systems 2026, 14(6), 639; https://doi.org/10.3390/systems14060639 - 3 Jun 2026
Cited by 1 | Viewed by 1096
Abstract
The shift toward hybrid intelligence in learning systems emphasizes the integration of human and AI cognitive capabilities into unified problem-solving processes. Yet, design principles for enabling such systems in immersive learning environments remain insufficiently understood. Immersive learning environments, realized through extended reality (XR), [...] Read more.
The shift toward hybrid intelligence in learning systems emphasizes the integration of human and AI cognitive capabilities into unified problem-solving processes. Yet, design principles for enabling such systems in immersive learning environments remain insufficiently understood. Immersive learning environments, realized through extended reality (XR), introduce unique affordances and challenges for embodied interaction, spatial communication, and co-presence that demand rethinking how collaboration unfolds. This conceptual paper proposes a framework and a set of design commitments for enabling sensible Human–AI collaboration for hybrid intelligence in immersive learning environments. Drawing on research and theories in Human–AI teaming and Human–AI collaboration, XR interaction design, learning sciences, and cognitive ergonomics, we identified four key dimensions of collaboration: collaborative agency and role distribution, shared attention and regulation, embodied and spatial interaction, and mutual intelligibility and adaptive support. We outline a conceptual framework describing how humans and AI can jointly achieve goals, negotiate roles, coordinate attention, and engage in knowledge co-construction within immersive learning spaces for hybrid intelligence. We further argue that immersive contexts require new forms of mutual intelligibility, spatial communication, and adaptive support to enable hybrid intelligence characterized by adaptive co-intelligence that improves learning processes in real time. Further, we advance a definition of hybrid intelligence specific to immersive learning that identifies three emergent properties: collaborative fluency, adaptive co-presence, and distributed knowledge growth. The paper closes with implications for researchers and practitioners and identifies constitutive design tensions that future work would navigate. Finally, this paper is conceptual in nature; the framework presented is offered as a theoretically grounded hypothesis for future empirical inquiry. Future research directions include validating the emergent properties through observational and experimental studies in actual XR environments and developing measurement tools adequate to the spatial, embodied, and real-time dimensions the framework identified. Full article
(This article belongs to the Special Issue Human-AI (H-AI) Teams: Designing for Human-AI Interactions)
►▼ Show Figures

Figure 1

18 pages, 421 KB  
Article
Embrace LLM-Based Cognitive Architecture to Boost Team Problem-Solving in Open-Ended Tasks
by Hashmath Shaik, Gnaneswar Villuri and Alex Doboli
Systems 2026, 14(3), 313; https://doi.org/10.3390/systems14030313 - 16 Mar 2026
Cited by 1 | Viewed by 1236
Abstract
Open-ended, team-based problem solving demands (i) a bridge between stochastic language models and symbolic control, (ii) mechanisms for idea elaboration, (iii) feature-level concept combination, and (iv) internal representations that support understanding beyond mere association. We present a cognitive architecture (CA) that couples an [...] Read more.
Open-ended, team-based problem solving demands (i) a bridge between stochastic language models and symbolic control, (ii) mechanisms for idea elaboration, (iii) feature-level concept combination, and (iv) internal representations that support understanding beyond mere association. We present a cognitive architecture (CA) that couples an LLM with an editable knowledge-graph (KG) scaffold and a controller that adaptively schedules five reasoning strategies. Elaborations are cast as graph updates validated against coverage and consistency checks; combinations produce property- and relation-level recompositions. On 30 collaborative programming dialogs (nine representative scenarios), adaptive prompting improves solution completeness by 19.1% and reduces required turns by 18.5% over a CoT baseline; explicit concept combinations increase Distinct-3 by 12.4 points with a +0.7 gain in human-rated creativity. Ablations show that Soft→Pruning scaffolds best support early elaboration, while Hard partitioning helps under ambiguity. The CA demonstrates a practical route to aligning LLMs with team intent in open-ended tasks. Full article
(This article belongs to the Special Issue Human-AI (H-AI) Teams: Designing for Human-AI Interactions)
►▼ Show Figures

Figure 1

Other

Jump to: Research

28 pages, 2156 KB  
Systematic Review
X-AI Techniques for Human–AI Teams: The Implementation-Design Framework
by John Turner, Hoda Parvaneh Shirazi, Heesun Kim, Jiajia Du, Yeonji Jung and Xiaoyan Xu
Systems 2026, 14(7), 862; https://doi.org/10.3390/systems14070862 - 20 Jul 2026
Viewed by 617
Abstract
Explainable artificial intelligence (X-AI) techniques aim to make the actions and decisions of autonomous systems understandable to humans interacting with these systems. In human–AI teams, explainability supports individual understanding and coordination, shared mental models, and collective decision-making among humans and AI agents. Research [...] Read more.
Explainable artificial intelligence (X-AI) techniques aim to make the actions and decisions of autonomous systems understandable to humans interacting with these systems. In human–AI teams, explainability supports individual understanding and coordination, shared mental models, and collective decision-making among humans and AI agents. Research has shown that X-AI enhances trust in autonomous systems, improves human–AI team performance, and supports collaboration across domains including aviation, finance, healthcare, hospitality, and sports. However, X-AI technologies face difficult challenges, including a lack of transparency and interpretability due to complex underlying models, also known as the “black-box” nature of AI systems. These technologies also lack any universally accepted evaluation metrics and have limited generalizability across applications. One deficit in the X-AI literature is that most frameworks focus on individual-level outcomes, with limited attention to team-level processes. The current study conducted a systematic literature review adhering to PRISMA guidelines and the SALSA framework. This study introduces the Implementation-Design (I-D) framework that organizes X-AI approaches along two dimensions: implementation, ranging from visual to interactive approaches, and design, ranging from isolated explanations to workflow-integrated systems. This framework captures lower-level engagement, involving individual users, to higher-level understanding that is necessary for teams and collectives. Findings indicate that visual explanation approaches support user engagement, while interactive workflow approaches promote deeper understanding, appropriate reliance, and distributed cognition within human–AI teams. Implications highlight the need for team-oriented explainability grounded in shared mental models, transactive memory systems, and collaborative X-AI artifacts. Practical guidelines are included to support researchers and practitioners in selecting appropriate X-AI techniques based on their context and level of analysis. The I-D framework is offered as a conceptual organizing model to guide research and practice, and empirical validation is identified as a priority for future work. Full article
(This article belongs to the Special Issue Human-AI (H-AI) Teams: Designing for Human-AI Interactions)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop