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Applications of Human–Computer Interaction-Based Decision Support Systems

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 March 2026 | Viewed by 856

Special Issue Editors


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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

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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

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
  • 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 (1 paper)

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Research

20 pages, 1215 KB  
Article
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 626
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 [...] Read more.
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
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