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Advances in Human–Computer Interaction and Collaboration

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 April 2026 | Viewed by 966

Special Issue Editors


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Guest Editor
Technical Department, University of Slavonski Brod, Ulica 108. brigade ZNG 36, 35000 Slavonski Brod, Croatia
Interests: manufacturing engineering; renewable energy sources

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Guest Editor Assistant
Department of Information and Communication Sciences, Faculty of Tourism and Rural Development, University of Osijek, Vukovarska 17, 34000 Požega, Croatia
Interests: machine learning; LLMs; microcontrollers; media density

Special Issue Information

Dear Colleagues,

This Special Issue presents new trends in theoretical, basic, and applied research at the interdisciplinary boundary between human–computer interaction, artificial intelligence, psychology, and engineering that promote advanced interactive systems and technologies for specific applications. Human–computer interaction encompasses all types of interfaces, from traditional graphical user interfaces to new natural user interfaces, brain–computer interfaces, and ambient intelligence systems. The development of effective collaboration tools depends on the performance of these advanced interaction paradigms.

Researchers are invited to submit their latest findings and results in the form of full articles or reviews. Topics of interest include the design and development of new interaction techniques, their usability and user experience evaluation, cognitive and perceptual studies of human–computer interaction and their applications in remote collaboration, virtual and augmented reality, adaptive user interfaces, and accessible technologies. This Special Issue also accepts articles that address the potential for enhancing human capabilities through interaction with computers in various fields, such as education, healthcare, and industry.

Dr. Mladen Bošnjaković
Guest Editor

Dr. Kristian Dokic
Guest Editor Assistant

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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • human–computer interaction
  • natural user interfaces
  • large language models
  • gesture recognition
  • adaptive interfaces
  • artificial intelligence
  • augmented reality
  • virtual reality

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Published Papers (1 paper)

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Research

25 pages, 1935 KB  
Article
Innovation Flow: A Human–AI Collaborative Framework for Managing Innovation with Generative Artificial Intelligence
by Michelle Catta-Preta, Alex Trejo Omeñaca, Jan Ferrer i Picó and Josep Maria Monguet-Fierro
Appl. Sci. 2025, 15(22), 11951; https://doi.org/10.3390/app152211951 - 11 Nov 2025
Viewed by 646
Abstract
Conventional innovation management methodologies (IMMs) often struggle to respond to the complexity, uncertainty, and cognitive diversity that characterise contemporary innovation projects. This study introduces Innovation Flow (IF), a human-centred and adaptive framework grounded in Flow Theory and enhanced by Generative Artificial Intelligence (GenAI). [...] Read more.
Conventional innovation management methodologies (IMMs) often struggle to respond to the complexity, uncertainty, and cognitive diversity that characterise contemporary innovation projects. This study introduces Innovation Flow (IF), a human-centred and adaptive framework grounded in Flow Theory and enhanced by Generative Artificial Intelligence (GenAI). At its core, IF operationalises Personalised Innovation Techniques (PInnTs)—adaptive variations of established methods tailored to project genetics and team profiles, generated dynamically through a GenAI-based system. Unlike traditional IMMs that rely on static toolkits and expert facilitation, Innovation Flow (IF) introduces a dynamic, GenAI-enhanced system capable of tailoring techniques in real time to each project’s characteristics and team profile. This adaptive model achieved a 60% reduction in ideation and prototyping time while maintaining high creative performance and autonomy. IF thus bridges the gap between human-centred design and AI augmentation, providing a scalable, personalised, and more inclusive pathway for managing innovation. Using a mixed-methods design that combines grounded theory with quasi-experimental validation, the framework was tested in 28 innovation projects across healthcare, manufacturing, and education. Findings show that personalisation improves application fidelity, engagement, and resilience, with 87% of cases achieving high efficacy. GenAI integration accelerated ideation and prototyping by more than 60%, reduced dependence on expert facilitators, and broadened participation by lowering the expertise barrier. Qualitative analyses emphasised the continuing centrality of human agency, as the most effective teams critically adapted rather than passively adopted AI outputs. The research establishes IF as a scalable methodology that augments, rather than replaces, human creativity, accelerating innovation cycles while reinforcing motivation and autonomy. Full article
(This article belongs to the Special Issue Advances in Human–Computer Interaction and Collaboration)
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