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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 October 2026 | Viewed by 2924

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Special Issue Editors

Prof. Dr. Koen Smit

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Guest Editor

Digital Ethics Research Group, HU University of Applied Sciences Utrecht, Heidelberglaan 15, 3584 CS Utrecht, The Netherlands
Interests: business rules management/decision management; decision mining; digital twin technology; human-computer interaction; value sensitive design

Dr. Raquel Sebastião

E-Mail Website
Guest Editor

Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Aveiro, 3810-193 Aveiro, Portugal
Interests: data mining, decision support; artificial Intelligence; machine learning; adaptive systems; computer-related health issues; patient-centred medicine

Special Issue Information

Dear Colleagues,

Human–Computer Interaction (HCI)-based Decision Support Systems (DSSs) represent a transformative approach to enhancing decision-making processes across diverse domains, including government, healthcare, finance, manufacturing, and emergency response. By integrating intuitive user interfaces with advanced computational models, these systems facilitate seamless interaction between humans and machines. They enable users to interpret complex data, simulate scenarios, and make informed decisions in real time.

This Special Issue explores the applications of HCI-based DSSs, emphasizing their role in improving user experience, reducing cognitive load, and increasing decision accuracy. Key technologies—such as natural language processing, adaptive interfaces, and multimodal interaction—are examined for their contributions to system responsiveness and personalization.

Case studies may include (but are not limited to) implementations in clinical diagnostics, disaster management, and smart city planning, demonstrating these systems’ capacity to support both strategic and operational decisions while ensuring legal validity and accuracy. The Issue also seeks to highlight the importance of user-centered design approaches (e.g., value-sensitive design) and continuous feedback loops in optimizing system performance. We particularly welcome contributions that explore the concept of affordances in this context.

Future directions include the integration of artificial intelligence and machine learning to further enhance adaptability and predictive capabilities. Finally, this Special Issue welcomes papers addressing the interplay between HCI-based DSS and developments in Generative AI.

Prof. Dr. Koen Smit
Dr. Raquel Sebastião
Guest Editors

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Keywords

human–computer interaction
user-centered design
decision support systems
decision management
decision-making
value-sensitive design
generative artificial intelligence
large language models
multimodal interpretation

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Published Papers (4 papers)

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Research

33 pages, 1424 KB

Open AccessArticle

Beating the Page Limit: Minimizing the Size of Documents

by Inbal Roshanski, Tal Abraham, Michal Alhindi, Lidor Ashtamker, Or Fuchs, Adi Toris, Amit Witkowski, Lidor Zur and Meir Kalech

Appl. Sci. 2026, 16(10), 4846; https://doi.org/10.3390/app16104846 - 13 May 2026

Viewed by 250

Abstract

Compilation-based text editors, such as Overleaf for enable users to insert text alongside commands, which are then compiled into a formatted document such as a PDF. A major drawback of these editors is the inability to preview the final document layout without compiling. Consequently, users often resort to trial-and-error methods to condense documents to meet space constraints, making changes in the text editor and repeatedly compiling to check whether the document fits the required space. In addition, space-saving modifications may compromise the readability of the document. For example, reducing the size of a figure by 50% can render it unreadable. In this paper, we formally define the problem of optimizing document layout to fit space constraints while minimizing the magnitude of layout modifications and thereby reducing the likelihood of harming readability. To address this challenge, we propose an automated decision-support system that employs machine learning models to estimate the space saved by specific adjustments. This system provides recommendations for modifications that optimize space usage while favoring smaller geometric changes, with final readability and layout quality verified separately. To evaluate the quality and real-world applicability of our algorithms, we conducted extensive experiments. First, we tested our algorithms on a dataset of 329,000 generated files, achieving improved reductions in document length compared with baseline heuristics. To assess applicability to real-world scenarios, we further evaluated our algorithms on 140 articles accepted to AAAI and obtained from arXiv. These experiments show that, in this benchmark setting, the proposed method achieves the best observed performance among the compared approaches, with moderate but consistent improvements over simpler methods and heuristics. Full article

(This article belongs to the Special Issue Applications of Human–Computer Interaction-Based Decision Support Systems)

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31 pages, 7614 KB

Open AccessArticle

A Conceptual Framework for Athlete Health Using AIoT, Wearables, and Personalized Performance Intelligence

by Ernesto William De Luca, Nicola Dall’Ora, Romeo Giuliano, Carlo della Valle, Alessandra di Cagno, Alessandra Ferramosca, Alessandro Lucidi, Daniele Passaretti, Chiara Parretti, Paolo Senesi, Samuele Germiniani, Stefano Aldegheri, Vincenzo Zara and Gabriele Arcidiacono

Appl. Sci. 2026, 16(9), 4542; https://doi.org/10.3390/app16094542 - 5 May 2026

Viewed by 411

Abstract

Advancing athlete health requires a shift from reactive sports medicine toward proactive, personalized, and longitudinal care. This article presents a conceptual framework for an Interdisciplinary AI Center for Longevity and Well-Being designed to integrate Artificial Intelligence of Things (AIoT), wearable sensing, and multimodal analytics into a unified athlete health ecosystem. The manuscript contextualizes the proposed framework with relevant literature across key technical domains and presents a reference edge–fog–cloud architecture together with a proof-of-concept dashboard pipeline to illustrate technical feasibility. Within this framework, heterogeneous data streams from wearable physiological sensors, biomechanical devices, non-invasive biomarker monitors, and environmental trackers are organized to support multimodal analysis and individualized performance intelligence. The paper outlines five target application domains: real-time health monitoring, injury risk assessment, performance optimization, holistic well-being evaluation, and longevity-oriented health management. Privacy-preserving and interpretable AI components, including federated learning, differential privacy, and explainability-oriented design considerations, are presented as key architectural priorities, while several elements are explicitly identified as future development directions. Rather than claiming full real-world validation, this work provides an interdisciplinary blueprint and prototype-informed foundation for future research and implementation at the intersection of computer science, biomedical engineering, and sports science. Full article

(This article belongs to the Special Issue Applications of Human–Computer Interaction-Based Decision Support Systems)

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20 pages, 2370 KB

Open AccessArticle

An Explainable HCI-Based Decision Support Framework for Human-AI Co-Design

by Linna Zhu, Yu Xie, Ningyu Xiang and Gang Chen

Appl. Sci. 2026, 16(8), 4007; https://doi.org/10.3390/app16084007 - 20 Apr 2026

Viewed by 352

Abstract

In ethics-sensitive product development, Generative AI can improve the efficiency of concept generation, but it also raises challenges related to accountability, value alignment, and decision transparency. To address limitations in current human-AI co-design processes, including unclear allocation of decision-making authority, insufficiently structured translation from design requirements to design constraints, and limited explainability in scheme evaluation, this study proposes an explainable Human–Computer Interaction (HCI)-based decision support framework for human-AI co-design, termed GAGT. The framework integrates Generative AI with multi-criteria decision-making methods. Specifically, the Analytic Hierarchy Process (AHP) is used to structure design requirements and determine their priorities, Grey Relational Analysis (GRA) is used to compare candidate schemes, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used to support transparent final ranking. Within the framework, human designers are mainly responsible for requirement confirmation, priority judgment, review at key checkpoints, and final scheme selection, while AI mainly supports information organization, candidate scheme generation, and quantitative comparison. The framework was applied to the design of a community medical vehicle through a small-sample, case-based, quasi-experimental study. Compared with the human-only condition, the GAGT-supported condition reduced design time by 56.1%. Compared with the AI-autonomous condition, it showed no observed HIPAA violations and a Value Drift Index of 16.1%, indicating better consistency with human-defined priorities. The results suggest that the proposed framework may improve design efficiency while supporting clearer human oversight and decision explainability in Generative AI-assisted design, and may provide a structured approach to organizing human and AI roles in ethics-sensitive design tasks. Full article

(This article belongs to the Special Issue Applications of Human–Computer Interaction-Based Decision Support Systems)

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20 pages, 1215 KB

Open AccessArticle

Precision, Fitness, Generalization, and Simplicity as Quality Dimensions for Decision Discovery Algorithms

by Sam Leewis, Koen Smit and Annemae van de Hoef

Appl. Sci. 2025, 15(20), 11060; https://doi.org/10.3390/app152011060 - 15 Oct 2025

Viewed by 1240

Abstract

Operational decisions significantly influence organizational performance and individual well-being. Decision mining offers a method to discover and analyze decision logic from decision logs, enhancing decision-making processes. However, evaluating the quality of decision discovery algorithms remains a challenge. While precision, fitness, generalization, and simplicity are well-established quality dimensions in process mining, their adaptation to the decision mining domain is underexplored. This study adapts these four dimensions to the necessary characteristics of decision models, providing a framework for evaluating decision discovery algorithms. Using a design science research approach, we develop tailored metrics and functions and demonstrate their application through a practical example of environmental permit management modeled in Decision Model and Notation (DMN). Precision measures how the discovered decision model reproduces the observed fact types and values from the decision log, detecting over-specification in the decision model. Fitness evaluates how completely the decision model covers the behavior in the decision log, identifying missing or under-specified elements in the decision model. Generalization assesses the model’s robustness to unseen decision cases by quantifying how well the discovered decision model performs beyond the training data. Simplicity captures the complexity in the discovered decision model in relation to a human actor-specified threshold. These insights guide decision model improvements, contributing to higher transparency, accountability, and fairness in operational decision-making processes. This research bridges a gap in the body of knowledge by providing a concrete methodology for evaluating decision discovery algorithms. The results support organizations in aligning decision models with regulatory requirements and public values, while also laying a foundation for future research. Full article

(This article belongs to the Special Issue Applications of Human–Computer Interaction-Based Decision Support Systems)

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