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Advances in Human-Centered Artificial Intelligence
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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: 30 November 2025 | Viewed by 39638

Special Issue Editors

Dr. Christos Troussas

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Guest Editor
Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece
Interests: personalization; human–computer interaction; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Akrivi Krouska

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

E-Mail Website
Guest Editor
Department of Informatics, Ionian University, 49132 Corfu, Greece
Interests: knowledge management; context representation and analysis; knowledge-assisted multimedia analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Katerina Kabassi

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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

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Published Papers (12 papers)

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15 pages, 2021 KiB  
Article
Toward Annotation, Visualization, and Reproducible Archiving of Human–Human Dialog Video Recording Applications
by Verena Schreyer, Marco Xaver Bornschlegl and Matthias Hemmje
Information 2025, 16(5), 349; https://doi.org/10.3390/info16050349 - 26 Apr 2025
Viewed by 228
Abstract
The COVID-19 pandemic increased the number of video conferences, for example, through online teaching and home office meetings. Even in the medical environment, consultation sessions are now increasingly conducted in the form of video conferencing. This includes sessions between psychotherapists and one or [...] Read more.
The COVID-19 pandemic increased the number of video conferences, for example, through online teaching and home office meetings. Even in the medical environment, consultation sessions are now increasingly conducted in the form of video conferencing. This includes sessions between psychotherapists and one or more call participants (individual/group calls). To subsequently document and analyze patient conversations, as well as any other human–human dialog, it is possible to record these video conferences. This allows experts to concentrate better on the conversation during the dialog and to perform analysis afterward. Artificial intelligence (AI) and its machine learning approach, which has already been used extensively for innovations, can provide support for subsequent analyses. Among other things, emotion recognition algorithms can be used to determine dialog participants’ emotions and record them automatically. This can alert experts to any noticeable sections of the conversation during subsequent analysis, thus simplifying the analysis process. As a result, experts can identify the cause of such sections based on emotion sequence data and exchange ideas with other experts within the context of an analysis tool. Full article
(This article belongs to the Special Issue Advances in Human-Centered Artificial Intelligence)
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20 pages, 780 KiB  
Article
A Fuzzy-Neural Model for Personalized Learning Recommendations Grounded in Experiential Learning Theory
by Christos Troussas, Akrivi Krouska, Phivos Mylonas and Cleo Sgouropoulou
Information 2025, 16(5), 339; https://doi.org/10.3390/info16050339 - 23 Apr 2025
Viewed by 284
Abstract
Personalized learning is a defining characteristic of current education, with flexible and adaptable experiences that respond to individual learners’ requirements and approaches to learning. Traditional implementations of educational theories—such as Kolb’s Experiential Learning Theory—often follow rule-based approaches, offering predefined structures but lacking adaptability [...] Read more.
Personalized learning is a defining characteristic of current education, with flexible and adaptable experiences that respond to individual learners’ requirements and approaches to learning. Traditional implementations of educational theories—such as Kolb’s Experiential Learning Theory—often follow rule-based approaches, offering predefined structures but lacking adaptability to dynamically changing learner behavior. In contrast, AI-based approaches such as artificial neural networks (ANNs) have high adaptability but lack interpretability. In this work, a new model, a fuzzy-ANN model, is developed that combines fuzzy logic with ANNs to make recommendations for activities in the learning process, overcoming current model weaknesses. In the first stage, fuzzy logic is used to map Kolb’s dimensions of learning style onto continuous membership values, providing a flexible and easier-to-interpret representation of learners’ preferred approaches to learning. These fuzzy weights are then processed in an ANN, enabling refinement and improvement in learning recommendations through analysis of patterns and adaptable learning. To make recommendations adapt and develop over time, a Weighted Sum Model (WSM) is used, combining learner activity trends and real-time feedback in dynamically updating proposed activity recommendations. Experimental evaluation in an educational environment shows that the model effectively generates personalized and changing experiences for learners, in harmony with learners’ requirements and activity trends. Full article
(This article belongs to the Special Issue Advances in Human-Centered Artificial Intelligence)
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21 pages, 819 KiB  
Article
Beyond Trolling: Fine-Grained Detection of Antisocial Behavior in Social Media During the Pandemic
by Andrew Asante and Petr Hajek
Information 2025, 16(3), 173; https://doi.org/10.3390/info16030173 - 26 Feb 2025
Viewed by 585
Abstract
Antisocial behavior (ASB), including trolling and aggression, undermines constructive discourse and escalates during periods of societal stress, such as the COVID-19 pandemic. This study aimed to examine ASB on social media during the COVID-19 pandemic by leveraging a novel annotated dataset and state-of-the-art [...] Read more.
Antisocial behavior (ASB), including trolling and aggression, undermines constructive discourse and escalates during periods of societal stress, such as the COVID-19 pandemic. This study aimed to examine ASB on social media during the COVID-19 pandemic by leveraging a novel annotated dataset and state-of-the-art transformer models for detection and classification of ASB categories. Specifically, this study examined ASB within a gold-standard corpus of tweets collected from Ghana during a 21-day lockdown. Each tweet was meticulously annotated into ASB categories or non-ASB, enabling a comprehensive analysis of online behaviors. We employed three state-of-the-art transformer-based language models (BERT, RoBERTa, and ELECTRA) and compared their performance against traditional machine learning models. The results demonstrate that the transformer-based approaches substantially outperformed the baseline models, achieving a high detection accuracy across both binary and multiclass classification tasks. RoBERTa excelled in binary ASB detection, attaining a 95.59% accuracy and an F1-score of 94.99%, while BERT led in multiclass classification, with a 94.38% accuracy and an F1-score of 93.92%. Trolling emerged as the most prevalent ASB type, reflecting the polarizing nature of online interactions during the lockdown. This study highlights the potential of transformer-based models in detecting diverse online behaviors and emphasizes the societal implications of ASB during crises. The findings provide a foundation for enhancing moderation tools and fostering healthier online environments. Full article
(This article belongs to the Special Issue Advances in Human-Centered Artificial Intelligence)
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21 pages, 2248 KiB  
Article
AI vs. Human-Authored Headlines: Evaluating the Effectiveness, Trust, and Linguistic Features of ChatGPT-Generated Clickbait and Informative Headlines in Digital News
by Vasile Gherheș, Marcela Alina Fărcașiu, Mariana Cernicova-Buca and Claudiu Coman
Information 2025, 16(2), 150; https://doi.org/10.3390/info16020150 - 18 Feb 2025
Viewed by 1717
Abstract
This study explores possible applications of AI technology in online journalism, given the predictions that speed and adaptation to the new medium will increase the penetration of automation in the production business. The literature shows that while the human supervision of journalistic workflow [...] Read more.
This study explores possible applications of AI technology in online journalism, given the predictions that speed and adaptation to the new medium will increase the penetration of automation in the production business. The literature shows that while the human supervision of journalistic workflow is still considered vital, the journalistic workflow is changing in nature, with the writing of micro-content being entrusted to ChatGPT-3.5 among the most visible features. This research assesses readers’ reactions to different headline styles as tested on a sample of 624 students from Timisoara, Romania, asked to evaluate the qualities of a mix of human-written vs. AI-generated headlines. The results show that AI-generated, informative headlines were perceived by more than half of the respondents as the most trustworthy and representative of the media content. Clickbait headlines, regardless of their source, were considered misleading and rated as manipulative (44.7%). In addition, 54.5% of respondents reported a decrease in trust regarding publications that frequently use clickbait techniques. A linguistic analysis was conducted to grasp the qualities of the headlines that triggered the registered responses. This study provides insights into the potential of AI-enabled tools to reshape headline writing practices in digital journalism. Full article
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20 pages, 5332 KiB  
Article
An Adaptive Fatigue Detection Model for Virtual Reality-Based Physical Therapy
by Sergio Martinez-Cid, Mohamed Essalhi, Vanesa Herrera, Javier Albusac, Santiago Schez-Sobrino and David Vallejo
Information 2025, 16(2), 148; https://doi.org/10.3390/info16020148 - 17 Feb 2025
Viewed by 753
Abstract
This paper introduces a fatigue detection model specifically designed for immersive virtual reality (VR) environments, aimed at facilitating upper limb rehabilitation for individuals with spinal cord injuries (SCIs). The model’s primary application centers on the Box-and-Block Test, providing healthcare professionals with a reliable [...] Read more.
This paper introduces a fatigue detection model specifically designed for immersive virtual reality (VR) environments, aimed at facilitating upper limb rehabilitation for individuals with spinal cord injuries (SCIs). The model’s primary application centers on the Box-and-Block Test, providing healthcare professionals with a reliable tool to monitor patient progress and adapt rehabilitation routines. At its core, the model employs data fusion techniques via ordered weighted averaging (OWA) operators to aggregate multiple metrics captured by the VR rehabilitation system. Additionally, fuzzy logic is employed to personalize fatigue assessments. Therapists are provided with a detailed classification of fatigue levels alongside a video-based visual representation that highlights critical moments of fatigue during the exercises. The experimental methodology involved testing the fatigue detection model with both healthy participants and patients, using immersive VR-based rehabilitation scenarios and validating its accuracy through self-reported fatigue levels and therapist observations. Furthermore, the model’s scalable design promotes its integration into remote rehabilitation systems, highlighting its adaptability to diverse clinical scenarios and its potential to enhance accessibility to rehabilitation services. Full article
(This article belongs to the Special Issue Advances in Human-Centered Artificial Intelligence)
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16 pages, 837 KiB  
Article
Barriers and Enablers of AI Adoption in Human Resource Management: A Critical Analysis of Organizational and Technological Factors
by Mitra Madanchian and Hamed Taherdoost
Information 2025, 16(1), 51; https://doi.org/10.3390/info16010051 - 15 Jan 2025
Cited by 3 | Viewed by 8400
Abstract
This paper examines the key factors recognized as transformative in the field of human resource management (HRM) and explores their influence on the global adoption of artificial intelligence (AI). While AI holds significant promise for enhancing HRM efficiency, employee engagement, and Decision Making, [...] Read more.
This paper examines the key factors recognized as transformative in the field of human resource management (HRM) and explores their influence on the global adoption of artificial intelligence (AI). While AI holds significant promise for enhancing HRM efficiency, employee engagement, and Decision Making, its implementation presents a range of organizational, technical, and ethical challenges that organizations worldwide must navigate. Change aversion, data security worries, and integration expenses are major roadblocks, but strong digital leadership, company culture, and advancements in NLP and machine learning are key enablers. This paper presents a complex analysis that questions the common perception of AI as only disruptive by delving into the relationship between power dynamics, corporate culture, and technology infrastructures. In this paper, we bring together research from several fields to help scholars and practitioners understand the nuances of AI adoption in HRM, with an emphasis on the importance of inclusive methods and ethical frameworks. Full article
(This article belongs to the Special Issue Advances in Human-Centered Artificial Intelligence)
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22 pages, 4718 KiB  
Article
Meaningful Multimodal Emotion Recognition Based on Capsule Graph Transformer Architecture
by Hajar Filali, Chafik Boulealam, Khalid El Fazazy, Adnane Mohamed Mahraz, Hamid Tairi and Jamal Riffi
Information 2025, 16(1), 40; https://doi.org/10.3390/info16010040 - 10 Jan 2025
Viewed by 2219
Abstract
The development of emotionally intelligent computers depends on emotion recognition based on richer multimodal inputs, such as text, speech, and visual cues, as multiple modalities complement one another. The effectiveness of complex relationships between modalities for emotion recognition has been demonstrated, but these [...] Read more.
The development of emotionally intelligent computers depends on emotion recognition based on richer multimodal inputs, such as text, speech, and visual cues, as multiple modalities complement one another. The effectiveness of complex relationships between modalities for emotion recognition has been demonstrated, but these relationships are still largely unexplored. Various fusion mechanisms using simply concatenated information have been the mainstay of previous research in learning multimodal representations for emotion classification, rather than fully utilizing the benefits of deep learning. In this paper, a unique deep multimodal emotion model is proposed, which uses the meaningful neural network to learn meaningful multimodal representations while classifying data. Specifically, the proposed model concatenates multimodality inputs using a graph convolutional network to extract acoustic modality, a capsule network to generate the textual modality, and vision transformer to acquire the visual modality. Despite the effectiveness of MNN, we have used it as a methodological innovation that will be fed with the previously generated vector parameters to produce better predictive results. Our suggested approach for more accurate multimodal emotion recognition has been shown through extensive examinations, producing state-of-the-art results with accuracies of 69% and 56% on two public datasets, MELD and MOSEI, respectively. Full article
(This article belongs to the Special Issue Advances in Human-Centered Artificial Intelligence)
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33 pages, 7086 KiB  
Article
A Human-Centered Approach to Academic Performance Prediction Using Personality Factors in Educational AI
by Muhammad Adnan Aslam, Fiza Murtaza, Muhammad Ehatisham Ul Haq, Amanullah Yasin and Muhammad Awais Azam
Information 2024, 15(12), 777; https://doi.org/10.3390/info15120777 - 5 Dec 2024
Cited by 3 | Viewed by 1830
Abstract
As artificial intelligence (AI) becomes increasingly integrated into educational environments, adopting a human-centered approach is essential for enhancing student outcomes. This study investigates the role of personality factors in predicting academic performance, emphasizing the need for explainable and ethical AI systems. Utilizing the [...] Read more.
As artificial intelligence (AI) becomes increasingly integrated into educational environments, adopting a human-centered approach is essential for enhancing student outcomes. This study investigates the role of personality factors in predicting academic performance, emphasizing the need for explainable and ethical AI systems. Utilizing the SAPEx-D (Student Academic Performance Exploration) dataset from Air University, Islamabad, which comprises 494 records, we explore how individual personality traits can impact academic success. We employed advanced regression models, including Gradient Boosting Regressor, K-Nearest Neighbors Regressor, Linear Regression, and Support Vector Regression, to predict students’ Cumulative Grade Point Average (CGPA). Our findings reveal that the Gradient Boosting Regressor achieved an R-squared value of 0.63 with the lowest Mean Squared Error (MSE); incorporating personality factors elevated the R-squared to 0.83, significantly improving predictive accuracy. For letter grade classification, the incorporation of personality factors improved the accuracy for distinct classes to 0.67 and to 0.85 for broader class categories. The integration of the Shapley Additive Explanations (SHAPs) technique further allowed for the interpretation of how personality traits interact with other factors, underscoring their role in shaping academic outcomes. This research highlights the importance of designing AI systems that are not only accurate but also interpretable and aligned with human values, thereby fostering a more equitable educational landscape. Future work will expand on these findings by exploring the interaction effects of personality traits and applying more sophisticated machine learning techniques. Full article
(This article belongs to the Special Issue Advances in Human-Centered Artificial Intelligence)
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33 pages, 4031 KiB  
Article
Support of Migrant Reception, Integration, and Social Inclusion by Intelligent Technologies
by Leo Wanner, Daniel Bowen, Marta Burgos, Ester Carrasco, Jan Černocký, Toni Codina, Jevgenijs Danilins, Steffi Davey, Joan de Lara, Eleni Dimopoulou, Ekaterina Egorova, Christine Gebhard, Jens Grivolla, Elena Jaramillo-Rojas, Matthias Klusch, Athanasios Mavropoulos, Maria Moudatsou, Artemisia Nikolaidou, Dimos Ntioudis, Irene Rodríguez, Mirela Rosgova, Yash Shekhawat, Alexander Shvets, Oleksandr Sobko, Grigoris Tzionis and Stefanos Vrochidisadd Show full author list remove Hide full author list
Information 2024, 15(11), 686; https://doi.org/10.3390/info15110686 - 1 Nov 2024
Viewed by 1189
Abstract
Apart from being an economic struggle, migration is first of all a societal challenge; most migrants come from different cultural and social contexts, do not speak the language of the host country, and are not familiar with its societal, administrative, and labour market [...] Read more.
Apart from being an economic struggle, migration is first of all a societal challenge; most migrants come from different cultural and social contexts, do not speak the language of the host country, and are not familiar with its societal, administrative, and labour market infrastructure. This leaves them in need of dedicated personal assistance during their reception and integration. However, due to the continuously high number of people in need of attendance, public administrations and non-governmental organizations are often overstrained by this task. The objective of the Welcome Platform is to address the most pressing needs of migrants. The Platform incorporates advanced Embodied Conversational Agent and Virtual Reality technologies to support migrants in the context of reception, integration, and social inclusion in the host country. It has been successfully evaluated in trials with migrants in three European countries in view of potentially deviating needs at the municipal, regional, and national levels, respectively: the City of Hamm in Germany, Catalonia in Spain, and Greece. The results show that intelligent technologies can be a valuable supplementary tool for reducing the workload of personnel involved in migrant reception, integration, and inclusion. Full article
(This article belongs to the Special Issue Advances in Human-Centered Artificial Intelligence)
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10 pages, 508 KiB  
Article
Promptology: Enhancing Human–AI Interaction in Large Language Models
by Phillip Olla, Lauren Elliott, Mustafa Abumeeiz, Karen Mihelich and Joshua Olson
Information 2024, 15(10), 634; https://doi.org/10.3390/info15100634 - 14 Oct 2024
Cited by 4 | Viewed by 3325
Abstract
This study investigates the integration of generative AI in higher education and the development of the SPARRO framework, a structured approach to improving human–AI interaction in academic settings. This ethnographic study explores the integration of generative AI in healthcare and nursing education, detailing [...] Read more.
This study investigates the integration of generative AI in higher education and the development of the SPARRO framework, a structured approach to improving human–AI interaction in academic settings. This ethnographic study explores the integration of generative AI in healthcare and nursing education, detailing the development of the SPARRO framework based on observations of student and faculty interactions with AI tools across five courses. The study identifies key challenges such as AI hallucination, mistrust of AI-generated summaries, and the difficulty in formulating effective prompts. The SPARRO framework addresses these challenges, offering a step-by-step guide for planning, prompt design, reviewing, and refining AI outputs. While the framework shows promise in improving AI integration, future research is needed to validate its applicability across other academic disciplines and assess its long-term impact on critical thinking and academic integrity. This study contributes to the growing body of research on AI in education, offering practical solutions for ethically and effectively integrating AI tools in academic settings. Full article
(This article belongs to the Special Issue Advances in Human-Centered Artificial Intelligence)
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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 43 | Viewed by 13951
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)

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26 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
Cited by 3 | Viewed by 2000
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)
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