Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.9
2.4
Journals
Information
Special Issues
Advances in Human-Centered Artificial Intelligence
share announcement

Advances in Human-Centered Artificial Intelligence

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 9126

Special Issue Editors

Dr. Christos Troussas

E-Mail Website
Guest Editor
Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece
Interests: adaptive hypermedia systems; artificial intelligence in education; human-centered artificial intelligence; human–computer interaction; personalized software
Special Issues, Collections and Topics in MDPI journals
Dr. Akrivi Krouska

E-Mail
Guest Editor
Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece
Interests: web systems; artificial intelligence systems; social networking services; adaptive software and multimedia applications
Special Issues, Collections and Topics in MDPI journals
Dr. Phivos Mylonas

E-Mail Website
Guest Editor
Department of Informatics, Ionian University, 491 32 Corfu, Greece
Interests: knowledge management; context representation and analysis; knowledge-assisted multimedia analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Katerina Kabassi

E-Mail Website
Guest Editor
Department of Environment, Ionian University, 49100 Kerkira, Greece
Interests: software engineering; user modelling; human-computer interaction
Special Issues, Collections and Topics in MDPI journals
Dr. Jaime Caro

E-Mail Website
Guest Editor
Department of Computer Science, University of the Philippines Diliman, Quezon 3113, Philippines
Interests: software engineering; artificial intelligence; software services; HCI
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Cleo Sgouropoulou

E-Mail Website
Guest Editor
Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece
Interests: e-learning; open educational resources (OERs); learning outcomes; competence and skill modeling for strengthening LLL; mobility and training to employment pathways; open data and analytics for e-research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Human-centered artificial intelligence concentrates on algorithms that are part of broader "user-based" software and evolves by user intervention and collaboration. Software systems that constantly evolve as a result of user input and offer a positive interaction between human users and machines are referred to as human-centered artificial intelligence. As such, this field can exceed the limits of existing artificial intelligence solutions to reduce the disparity between computers and users by generating machine intelligence with the aim of comprehending natural speech, sentiment, and behaviour.

As mentioned above, by fusing powerful user experiences with integrated artificial intelligence services that users demand, human-centered artificial intelligence, as a compelling idea, empowers people to perceive, understand, produce, and interact in distinctive ways.

With precise guidelines for creating effective solutions that enhance, magnify, enable, and enrich individuals rather than replacing them, the HCAI approach addresses the barrier between ethics and practice. This change in perspective might pave the way for a future that is safer, clearer, and simpler to control. An HCAI strategy will lessen the likelihood of unchecked technology, allay concerns about machine-driven layoffs, and lessen dangers to security and privacy. Future developments that are centered on people will also uphold human values, honor human dignity, and foster strong respect for the human abilities that lead to innovative breakthroughs and innovations.

Toward this direction, this Special Issue is soliciting original research papers as well as review articles and short communications in specific relevant areas. Pertaining to the field of human-centered artificial intelligence, the topics of interest/application include, but are not limited to, the following:

  • Conversational agents and smart assistants
  • Technological advancements in healthcare
  • Autonomous systems
  • Support for people with disabilities
  • Personalized tutoring systems
  • Efficient green energy
  • Effective cybersecurity
  • User-centered and intelligent systems

Dr. Christos Troussas
Dr. Akrivi Krouska
Dr. Phivos Mylonas
Dr. Katerina Kabassi
Dr. Jaime Caro
Prof. Dr. Cleo Sgouropoulou
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Information 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 1600 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 artificial intelligence
  • enhanced user experience
  • human–artificial-intelligence interaction

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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (2 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

18 pages, 278 KiB  
Article
Higher Education Students’ Task Motivation in the Generative Artificial Intelligence Context: The Case of ChatGPT
by Mohammad Hmoud, Hadeel Swaity, Nardin Hamad, Omar Karram and Wajeeh Daher
Information 2024, 15(1), 33; https://doi.org/10.3390/info15010033 - 8 Jan 2024
Cited by 14 | Viewed by 7716
Abstract
Artificial intelligence has been attracting the attention of educational researchers recently, especially ChatGPT as a generative artificial intelligence tool. The context of generative artificial intelligence could impact different aspects of students’ learning, such as the motivational aspect. The present research intended to investigate [...] Read more.
Artificial intelligence has been attracting the attention of educational researchers recently, especially ChatGPT as a generative artificial intelligence tool. The context of generative artificial intelligence could impact different aspects of students’ learning, such as the motivational aspect. The present research intended to investigate the characteristics of students’ task motivation in the artificial intelligence context, specifically in the ChatGPT context. The researchers interviewed 15 students about their experiences with ChatGPT to collect data. The researchers used inductive and deductive content analysis to investigate students’ motivation when learning with ChatGPT. To arrive at the categories and sub-categories of students’ motivation, the researchers used the MAXQDA 2022. Five main categories emerged: task enjoyment, reported effort, result assessment, perceived relevance, and interaction. Each category comprised at least two sub-categories, and each sub-category was further organized into codes. The results indicated more positive characteristics of motivation than negative ones. The previous results could be due to the conversational or social aspect of the chatbot, enabling relationships with humans and enabling the maintenance of good quality conversations with them. We conclude that a generative AI could be utilized in educational settings to promote students’ motivation to learn and thus raise their learning achievement. Full article
(This article belongs to the Special Issue Advances in Human-Centered Artificial Intelligence)

Other

Jump to: Research

27 pages, 6706 KiB  
Systematic Review
Word Sense Disambiguation for Morphologically Rich Low-Resourced Languages: A Systematic Literature Review and Meta-Analysis
by Hlaudi Daniel Masethe, Mosima Anna Masethe, Sunday Olusegun Ojo, Fausto Giunchiglia and Pius Adewale Owolawi
Information 2024, 15(9), 540; https://doi.org/10.3390/info15090540 - 4 Sep 2024
Viewed by 450
Abstract
In natural language processing, word sense disambiguation (WSD) continues to be a major difficulty, especially for low-resource languages where linguistic variation and a lack of data make model training and evaluation more difficult. The goal of this comprehensive review and meta-analysis of the [...] Read more.
In natural language processing, word sense disambiguation (WSD) continues to be a major difficulty, especially for low-resource languages where linguistic variation and a lack of data make model training and evaluation more difficult. The goal of this comprehensive review and meta-analysis of the literature is to summarize the body of knowledge regarding WSD techniques for low-resource languages, emphasizing the advantages and disadvantages of different strategies. A thorough search of several databases for relevant literature produced articles assessing WSD methods in low-resource languages. Effect sizes and performance measures were extracted from a subset of trials through analysis. Heterogeneity was evaluated using pooled effect and estimates were computed by meta-analysis. The preferred reporting elements for systematic reviews and meta-analyses (PRISMA) were used to develop the process for choosing the relevant papers for extraction. The meta-analysis included 32 studies, encompassing a range of WSD methods and low-resourced languages. The overall pooled effect size indicated moderate effectiveness of WSD techniques. Heterogeneity among studies was high, with an I2 value of 82.29%, suggesting substantial variability in WSD performance across different studies. The (τ2) tau value of 5.819 further reflects the extent of between-study variance. This variability underscores the challenges in generalizing findings and highlights the influence of diverse factors such as language-specific characteristics, dataset quality, and methodological differences. The p-values from the meta-regression (0.454) and the meta-analysis (0.440) suggest that the variability in WSD performance is not statistically significantly associated with the investigated moderators, indicating that the performance differences may be influenced by factors not fully captured in the current analysis. The absence of significant p-values raises the possibility that the problems presented by low-resource situations are not yet well addressed by the models and techniques in use. Full article
(This article belongs to the Special Issue Advances in Human-Centered Artificial Intelligence)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop