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Future Internet
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Human-Centered Artificial Intelligence
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Human-Centered Artificial Intelligence

A Special Issue of Future Internet (ISSN 1999-5903).

Deadline for manuscript submissions: closed (28 February 2026) | Viewed by 42555

Editors

Dr. Rui Yu

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, University of Louisville, Louisville, KY 40208, USA
Interests: computer vision, machine learning, human-computer interaction, and robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Sooyeon Lee

E-Mail Website
Guest Editor
Department of Informatics, Ying Wu College of Computing, New Jersey Institute of Technology, Newark, NJ 07102, USA
Interests: human-computer interaction, accessibility, human-AI interaction, and design research
Special Issues, Collections and Topics in MDPI journals
Dr. Syed Masum Billah

E-Mail Website
Guest Editor
College of Information Sciences and Technology, Pennsylvania State University, University Park, PA 16802, USA
Interests: human-computer interaction, intelligent interaction systems, AI for accessibility, accessible computing
Special Issues, Collections and Topics in MDPI journals
Dr. John M. Carroll

E-Mail Website
Guest Editor
College of Information Sciences and Technology, Pennsylvania State University, University Park, PA 16802, USA
Interests: human–computer interaction; computer-supported collaborative work; community informatics; design research; learning science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue of Future Internet entitled “Human-Centred Artificial Intelligence”. This Special Issue aims to explore the diverse aspects of designing and developing AI systems that prioritize human values, ethical considerations, and societal well-being. As AI continues to be integrated into various facets of our lives, from healthcare and education to transportation and entertainment, it is crucial that these systems are developed with a human-centric approach. Human-Centred Artificial Intelligence (HCAI) strives to create AI technologies that are innovative, efficient, responsible, and beneficial to society.

Unlike traditional AI approaches that focus on technical performance, HCAI emphasizes transparency, fairness, and user empowerment. This holistic approach addresses potential risks such as biases in decision making, lack of interpretability, and issues of trust as well as accountability. By fostering interdisciplinary collaboration, HCAI aims to develop AI systems that enhance human capabilities, support social good, and contribute to a more equitable and just society.

We invite submissions of original research papers, review articles, and short communications to this Special Issue to highlight cutting-edge research and innovative approaches that contribute to the advancement of HCAI. We welcome submissions on a broad range of topics within HCAI, including, but not limited to, the following:

  • Explainable AI (XAI).
  • Fairness and bias in AI.
  • Human–AI collaboration.
  • Ethical and trustworthy AI.
  • User-centered design for AI.
  • AI and accessibility.
  • Human factors in AI.
  • Societal impacts of AI.
  • Human–robot interaction (HRI).
  • Adaptive learning systems.
  • AI in education.
  • Patient-centered healthcare AI.

Dr. Rui Yu
Dr. Sooyeon Lee
Dr. Syed Masum Billah
Dr. John M. Carroll
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. Future Internet 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-centered AI
  • explainable AI
  • fairness in AI
  • human–AI collaboration

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

Related Special Issue

Published Papers (8 papers)

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Research

20 pages, 2708 KB  
Article
Enhancing Handball Analytics with Computer Vision and Machine Learning: An Exploratory Experiment
by Mostafa Farahat, Hassan Soubra, Donatien Koulla Moulla and Alain Abran
Future Internet 2026, 18(4), 199; https://doi.org/10.3390/fi18040199 - 10 Apr 2026
Viewed by 1265
Abstract
Recent advancements in artificial intelligence (AI) have strengthened the interaction between sports and digital technologies. However, unlike widely studied sports such as football and basketball, handball has received limited attention from the scientific community, despite its fast-paced nature and strategic importance. This study [...] Read more.
Recent advancements in artificial intelligence (AI) have strengthened the interaction between sports and digital technologies. However, unlike widely studied sports such as football and basketball, handball has received limited attention from the scientific community, despite its fast-paced nature and strategic importance. This study focuses on object detection in handball and targets key entities, such as players, referees, goalkeepers, and the ball. A comprehensive dataset was created through a collaborative annotation process, consisting of annotated images extracted from real handball games. The YOLOv8 model was then trained and evaluated on this dataset to assess its effectiveness in entity recognition. The proposed approach achieved an object detection accuracy of 86.8% on a relatively small held-out test set, providing an indicative first benchmark for the application of state-of-the-art machine learning models to handball. To the best of our knowledge, the dataset generated in this study is the first comprehensive collection of annotated handball images, providing a valuable resource for further research. By bridging sports analytics and computer vision, this study contributes to the advancement of performance assessment in handball. These exploratory results suggest potential directions for future real-time systems and practical applications, such as improved understanding of player performance, team dynamics, and strategic decision-making. Full article
(This article belongs to the Special Issue Human-Centered Artificial Intelligence)
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17 pages, 249 KB  
Article
ChatGPT-Assisted Task Analysis for Special Education Teachers: An Exploratory Study of Alignment, Readability, Efficiency, and Acceptability
by Serife Balikci, Nesime Kubra Terzioglu and Salih Rakap
Future Internet 2026, 18(3), 158; https://doi.org/10.3390/fi18030158 - 18 Mar 2026
Cited by 2 | Viewed by 1157
Abstract
Task analysis is a foundational component of instructional design in special education, yet it can impose substantial time and cognitive demands on teachers. Artificial intelligence (AI) tools such as ChatGPT may provide support for instructional planning tasks by assisting educators in generating and [...] Read more.
Task analysis is a foundational component of instructional design in special education, yet it can impose substantial time and cognitive demands on teachers. Artificial intelligence (AI) tools such as ChatGPT may provide support for instructional planning tasks by assisting educators in generating and organizing task sequences. This study examined the effectiveness, readability, time efficiency, and acceptability of ChatGPT-assisted task analysis compared to a traditional task analysis method. Thirty-two special education teachers participated in a randomized between-groups study in which they developed task analyses using either a traditional approach or ChatGPT supported by a structured interaction protocol. Task analyses were evaluated based on alignment with expert-developed models, readability, and development time, and teachers’ perceptions of acceptability were also examined. Results indicated that ChatGPT-assisted task analyses required significantly less development time while demonstrating strong alignment with expert-generated models. Readability levels and the number of task steps were similar across groups. Teachers who used ChatGPT also reported positive perceptions regarding the usefulness and acceptability of AI assistance in instructional planning. These findings suggest that AI-assisted tools may support teachers in developing task analyses more efficiently while maintaining instructional clarity. However, given the exploratory nature of the study and the limited sample, further research is needed to examine how AI-assisted task analysis may influence instructional practice and student learning outcomes in special education. Full article
(This article belongs to the Special Issue Human-Centered Artificial Intelligence)
34 pages, 9628 KB  
Article
Modeling Interaction Patterns in Visualizations with Eye-Tracking: A Characterization of Reading and Information Styles
by Angela Locoro and Luigi Lavazza
Future Internet 2025, 17(11), 504; https://doi.org/10.3390/fi17110504 - 3 Nov 2025
Cited by 1 | Viewed by 1546
Abstract
In data visualization, users’ scanning patterns are as crucial as their reading patterns in text-based media. Yet, no systematic attempt exists to characterize this activity with basic features, such as reading speed and scanpaths, nor to relate them to data complexity and information [...] Read more.
In data visualization, users’ scanning patterns are as crucial as their reading patterns in text-based media. Yet, no systematic attempt exists to characterize this activity with basic features, such as reading speed and scanpaths, nor to relate them to data complexity and information disposition. To fill this gap, this paper proposes a model-based method to analyze and interpret those features from eye-tracking data. To this end, the bias-noise model is applied to a data visualization eye-tracking dataset available online, and enriched with areas of interest labels. The positive results of this method are as follows: (i) the identification of users’ reading styles like meticulous, systematic, and serendipitous; (ii) the characterization of information disposition as gathered or scattered, and of information complexity as more or less dense; (iii) the discovery of a behavioural pattern of efficiency, given that the more visualizations were read by a participant, the greater their reading speed, consistency, and predictability of reading; (iv) the identification of encoding and title areas of interest as the primary loci of attention in visualizations, with a peculiar back-and-forth reading pattern; (v) the identification of the encoding area of interest as the fastest to read in less dense visualization types, such as bars, circles, and lines charts. Future experiments involving participants from diverse cultural backgrounds could not only validate the observed behavioural patterns, but also enrich the experimental framework with additional perspectives. Full article
(This article belongs to the Special Issue Human-Centered Artificial Intelligence)
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23 pages, 539 KB  
Article
AI-Supported EUD for Data Visualization: An Exploratory Case Study
by Sara Beschi, Daniela Fogli, Luigi Gargioni and Angela Locoro
Future Internet 2025, 17(8), 349; https://doi.org/10.3390/fi17080349 - 1 Aug 2025
Cited by 3 | Viewed by 2710
Abstract
Data visualization is a key activity in data-driven decision making and is gaining momentum in many organizational contexts. However, the role and contribution of both end-user development (EUD) and artificial intelligence (AI) technologies for data visualization and analytics are still not clear or [...] Read more.
Data visualization is a key activity in data-driven decision making and is gaining momentum in many organizational contexts. However, the role and contribution of both end-user development (EUD) and artificial intelligence (AI) technologies for data visualization and analytics are still not clear or systematically studied. This work investigates how effectively AI-supported EUD tools may assist visual analytics tasks in organizations. An exploratory case study with eight interviews with key informants allowed a deep understanding of data analysis and visualization practices in a large Italian company. It aimed at identifying the various professional roles and competencies necessary in the business context, understanding the data sources and data formats exploited in daily activities, and formulating suitable hypotheses to guide the design of AI-supported EUD tools for data analysis and visualization. In particular, the results of interviews with key informants yielded the development of a prototype of an LLM-based EUD environment, which was then used with selected target users to collect their opinions and expectations about this type of intervention in their work practice and organization. All the data collected during the exploratory case study finally led to defining a set of design guidelines for AI-supported EUD for data visualization. Full article
(This article belongs to the Special Issue Human-Centered Artificial Intelligence)
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28 pages, 4256 KB  
Article
Accessible IoT Dashboard Design with AI-Enhanced Descriptions for Visually Impaired Users
by George Alex Stelea, Livia Sangeorzan and Nicoleta Enache-David
Future Internet 2025, 17(7), 274; https://doi.org/10.3390/fi17070274 - 21 Jun 2025
Cited by 6 | Viewed by 3707
Abstract
The proliferation of the Internet of Things (IoT) has led to an abundance of data streams and real-time dashboards in domains such as smart cities, healthcare, manufacturing, and agriculture. However, many current IoT dashboards emphasize complex visualizations with minimal textual cues, posing significant [...] Read more.
The proliferation of the Internet of Things (IoT) has led to an abundance of data streams and real-time dashboards in domains such as smart cities, healthcare, manufacturing, and agriculture. However, many current IoT dashboards emphasize complex visualizations with minimal textual cues, posing significant barriers to users with visual impairments who rely on screen readers or other assistive technologies. This paper presents AccessiDashboard, a web-based IoT dashboard platform that prioritizes accessible design from the ground up. The system uses semantic HTML5 and WAI-ARIA compliance to ensure that screen readers can accurately interpret and navigate the interface. In addition to standard chart presentations, AccessiDashboard automatically generates long descriptions of graphs and visual elements, offering a text-first alternative interface for non-visual data exploration. The platform supports multi-modal data consumption (visual charts, bullet lists, tables, and narrative descriptions) and leverages Large Language Models (LLMs) to produce context-aware textual representations of sensor data. A privacy-by-design approach is adopted for the AI integration to address ethical and regulatory concerns. Early evaluation suggests that AccessiDashboard reduces cognitive and navigational load for users with vision disabilities, demonstrating its potential as a blueprint for future inclusive IoT monitoring solutions. Full article
(This article belongs to the Special Issue Human-Centered Artificial Intelligence)
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55 pages, 4454 KB  
Article
The Future of Education: A Multi-Layered Metaverse Classroom Model for Immersive and Inclusive Learning
by Leyli Nouraei Yeganeh, Nicole Scarlett Fenty, Yu Chen, Amber Simpson and Mohsen Hatami
Future Internet 2025, 17(2), 63; https://doi.org/10.3390/fi17020063 - 4 Feb 2025
Cited by 103 | Viewed by 18966
Abstract
Modern education faces persistent challenges, including disengagement, inequitable access to learning resources, and the lack of personalized instruction, particularly in virtual environments. In this perspective, we envision a transformative Metaverse classroom model, the Multi-layered Immersive Learning Environment (Meta-MILE) to address these critical issues. [...] Read more.
Modern education faces persistent challenges, including disengagement, inequitable access to learning resources, and the lack of personalized instruction, particularly in virtual environments. In this perspective, we envision a transformative Metaverse classroom model, the Multi-layered Immersive Learning Environment (Meta-MILE) to address these critical issues. The Meta-MILE framework integrates essential components such as immersive infrastructure, personalized interactions, social collaboration, and advanced assessment techniques to enhance student engagement and inclusivity. By leveraging three-dimensional (3D) virtual environments, artificial intelligence (AI)-driven personalization, gamified learning pathways, and scenario-based evaluations, the Meta-MILE model offers tailored learning experiences that traditional virtual classrooms often struggle to achieve. Acknowledging potential challenges such as accessibility, infrastructure demands, and data security, the study proposed practical strategies to ensure equitable access and safe interactions within the Metaverse. Empirical findings from our pilot experiment demonstrated the framework’s effectiveness in improving engagement and skill acquisition, with broader implications for educational policy and competency-based, experiential learning approaches. Looking ahead, we advocate for ongoing research to validate long-term learning outcomes and technological advancements to make immersive learning more accessible and secure. Our perspective underscores the transformative potential of the Metaverse classroom in shaping inclusive, future-ready educational environments capable of meeting the diverse needs of learners worldwide. Full article
(This article belongs to the Special Issue Human-Centered Artificial Intelligence)
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21 pages, 2476 KB  
Article
Enhancing Human–Agent Interaction via Artificial Agents That Speculate About the Future
by Casey C. Bennett, Young-Ho Bae, Jun-Hyung Yoon, Say Young Kim and Benjamin Weiss
Future Internet 2025, 17(2), 52; https://doi.org/10.3390/fi17020052 - 21 Jan 2025
Cited by 2 | Viewed by 3181
Abstract
Human communication in daily life entails not only talking about what we are currently doing or will do, but also speculating about future possibilities that may (or may not) occur, i.e., “anticipatory speech”. Such conversations are central to social cooperation and social cohesion [...] Read more.
Human communication in daily life entails not only talking about what we are currently doing or will do, but also speculating about future possibilities that may (or may not) occur, i.e., “anticipatory speech”. Such conversations are central to social cooperation and social cohesion in humans. This suggests that such capabilities may also be critical for developing improved speech systems for artificial agents, e.g., human–agent interaction (HAI) and human–robot interaction (HRI). However, to do so successfully, it is imperative that we understand how anticipatory speech may affect the behavior of human users and, subsequently, the behavior of the agent/robot. Moreover, it is possible that such effects may vary across cultures and languages. To that end, we conducted an experiment where a human and autonomous 3D virtual avatar interacted in a cooperative gameplay environment. The experiment included 40 participants, comparing different languages (20 English, 20 Korean), where the artificial agent had anticipatory speech either enabled or disabled. The results showed that anticipatory speech significantly altered the speech patterns and turn-taking behavior of both the human and the agent, but those effects varied depending on the language spoken. We discuss how the use of such novel communication forms holds potential for enhancing HAI/HRI, as well as the development of mixed reality and virtual reality interactive systems for human users. Full article
(This article belongs to the Special Issue Human-Centered Artificial Intelligence)
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30 pages, 10493 KB  
Article
Visualisation Design Ideation with AI: A New Framework, Vocabulary, and Tool
by Aron E. Owen and Jonathan C. Roberts
Future Internet 2024, 16(11), 406; https://doi.org/10.3390/fi16110406 - 5 Nov 2024
Cited by 6 | Viewed by 7140
Abstract
This paper introduces an innovative framework for visualisation design ideation, which includes a collection of terms for creative visualisation design, the five-step process, and an implementation called VisAlchemy. Throughout the visualisation ideation process, individuals engage in exploring various concepts, brainstorming, sketching ideas, prototyping, [...] Read more.
This paper introduces an innovative framework for visualisation design ideation, which includes a collection of terms for creative visualisation design, the five-step process, and an implementation called VisAlchemy. Throughout the visualisation ideation process, individuals engage in exploring various concepts, brainstorming, sketching ideas, prototyping, and experimenting with different methods to visually represent data or information. Sometimes, designers feel incapable of sketching, and the ideation process can be quite lengthy. In such cases, generative AI can provide assistance. However, even with AI, it can be difficult to know which vocabulary to use and how to strategically approach the design process. Our strategy prompts imaginative and structured narratives for generative AI use, facilitating the generation and refinement of visualisation design ideas. We aim to inspire fresh and innovative ideas, encouraging creativity and exploring unconventional concepts. VisAlchemy is a five-step framework: a methodical approach to defining, exploring, and refining prompts to enhance the generative AI process. The framework blends design elements and aesthetics with context and application. In addition, we present a vocabulary set of 300 words, underpinned from a corpus of visualisation design and art papers, along with a demonstration tool called VisAlchemy. The interactive interface of the VisAlchemy tool allows users to adhere to the framework and generate innovative visualisation design concepts. It is built using the SDXL Turbo language model. Finally, we demonstrate its use through case studies and examples and show the transformative power of the framework to create inspired and exciting design ideas through refinement, re-ordering, weighting of words and word rephrasing. Full article
(This article belongs to the Special Issue Human-Centered Artificial Intelligence)
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