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Artificial Intelligence in Education and Sustainable Development
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Artificial Intelligence in Education and Sustainable Development

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Education and Approaches".

Deadline for manuscript submissions: 18 July 2025 | Viewed by 5070

Special Issue Editors

Prof. Dr. Pilar Colás-Bravo

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Guest Editor
Department of Research Methods and Diagnosis in Education, Faculty of Education Sciences, University of Seville, 41013 Seville, Spain
Interests: ICT in education; sustainability and inclusion in education; gender policy assessment
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Emilio Berrocal De Luna

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Guest Editor
Department of Research Methods and Diagnosis in Education, Faculty of Education Sciences, University of Granada, Granada, Spain
Interests: intercultural education; inclusion and attention to diversity in education systems; quantitative and qualitative research methods in education; ICT in education
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Calixto Gutiérrez Braojos

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Guest Editor
Department of Educational Research Methods, Assessment and Evaluation, University of Granada, 18071 Granada, Spain
Interests: knowledge building communities; training in educational research; design, creation, and evaluation of technologies for education

Special Issue Information

Dear Colleagues,

Artificial Intelligence in Education and Sustainable Development

Artificial intelligence (AI) brings powerful technological resources to address some of the great challenges of education today, including inequalities in access to knowledge, research, and respect for cultural diversity. But it is also a technology that can generate important changes in teaching and learning practices in both formal and non-formal education.

UNESCO proposes the incorporation of AI as a tool for the achievement of the 2030 Education Agenda, while ensuring respect for the basic principles of inclusion and equity in its use. The aim is to ensure that AI enhances progress towards the achievement of SDG 4, as well as to avoid widening the technology gap within countries.

The underlying idea is to achieve “AI for all”, enabling all people to access the fruits of this ongoing technological revolution, primarily in terms of innovation and knowledge. We invite researchers and professionals in the educational and technological fields to contribute their studies and experiences to address this fascinating field from various perspectives. The thematic areas that make up the proposal for this monograph are as follows:

  • AI and Innovation in Teaching and Learning.
  • AI in Learning Assessment.
  • AI to Ensure Inclusion and Diversity in Education
  • AI in Education for Sustainable Development
  • Ethical and Social Challenges Associated with AI in Education
  • AI in Educational Management

In summary, this monograph aims to be a space for reflection and dialogue on how artificial intelligence can be a catalyst for a more inclusive, personalized, and sustainability-oriented education. We invite researchers and educators to publish in this Special Issue, and the selected articles will contribute to forming a broad and deep overview of the possibilities and challenges that AI presents in the educational field, providing valuable insights for academics, professionals, and policymakers.

Prof. Dr. Pilar Colás-Bravo
Prof. Dr. Emilio Berrocal De Luna
Prof. Dr. Calixto Gutiérrez Braojos
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. Sustainability 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

  • artificial intelligence (AI)
  • sustainable development
  • education
  • teaching and learning
  • sustainability
  • learning assessment
  • educational management

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

Published Papers (3 papers)

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Research

30 pages, 1866 KiB  
Article
Key Factors Influencing Design Learners’ Behavioral Intention in Human-AI Collaboration Within the Educational Metaverse
by Ronghui Wu, Lin Gao, Jiaxin Li, Qianghong Huang and Younghwan Pan
Sustainability 2024, 16(22), 9942; https://doi.org/10.3390/su16229942 - 14 Nov 2024
Viewed by 499
Abstract
This study investigates the key factors which influence design learners’ behavioral intention to collaborate with AI in the educational metaverse (EMH-AIc). Engaging design learners in EMH-AIc enhances learning efficiency, personalizes learning experiences, and supports equitable and sustainable design education. However, limited research has [...] Read more.
This study investigates the key factors which influence design learners’ behavioral intention to collaborate with AI in the educational metaverse (EMH-AIc). Engaging design learners in EMH-AIc enhances learning efficiency, personalizes learning experiences, and supports equitable and sustainable design education. However, limited research has focused on these influencing factors, leading to a lack of theoretical grounding for user behavior in this context. Drawing on social cognitive theory (SCT), this study constructs a three-dimensional theoretical model comprising the external environment, individual cognition, and behavior, validated within an EMH-AIc setting. By using Spatial.io’s Apache Art Studio as the experimental platform and analyzing data from 533 design learners with SPSS 27.0, SmartPLS 4.0, and partial least squares structural equation modeling (PLS-SEM), this study identifies those rewards, teacher support, and facilitating conditions in the external environment, with self-efficacy, outcome expectation, and trust in cognition also significantly influencing behavioral intention. Additionally, individual cognition mediates the relationship between the external environment and behavioral intention. This study not only extends SCT application within the educational metaverse but also provides actionable insights for optimizing design learning experiences, contributing to the sustainable development of design education. Full article
(This article belongs to the Special Issue Artificial Intelligence in Education and Sustainable Development)
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13 pages, 798 KiB  
Article
Unveiling the Canvas: Sustainable Integration of AI in Visual Art Education
by Hanjun Su and Nur Azlina Mohamed Mokmin
Sustainability 2024, 16(17), 7849; https://doi.org/10.3390/su16177849 - 9 Sep 2024
Viewed by 2918
Abstract
The rapid advancement of technology is transforming the landscape of art education, fostering a new era of creativity and learning with a focus on sustainability. By optimizing resources and reducing the reliance on physical materials, AI-supported art education enhances sustainability, broadens accessibility, and [...] Read more.
The rapid advancement of technology is transforming the landscape of art education, fostering a new era of creativity and learning with a focus on sustainability. By optimizing resources and reducing the reliance on physical materials, AI-supported art education enhances sustainability, broadens accessibility, and lowers environmental impacts. Despite some research on the application of smart tools in art education, there remains a gap in robust evidence supporting their effectiveness and long-term impact. This study undertakes an in-depth examination of the intersection of sustainable technologies, pedagogical theories, and assessment methods within visual art education. By reviewing 685 research articles from the past decade, we ultimately filtered them down to 36 completely relevant studies that illuminate the technological advancements in teaching visual art. Our analysis focuses on emerging trends, the theoretical frameworks underpinning learning, hardware platforms, application categories, and dependent variables used to assess the impact on sustainability. Our findings indicate that the use of technology in art education is still in its nascent stages, yet it holds significant potential for sustainable development. These insights are crucial for developers and educators, offering guidance on creating user-friendly, interactive, and sustainable art education programs that enhance student comprehension and engagement beyond the current offerings. Full article
(This article belongs to the Special Issue Artificial Intelligence in Education and Sustainable Development)
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28 pages, 12949 KiB  
Article
The Exploration of Predictors for Peruvian Teachers’ Life Satisfaction through an Ensemble of Feature Selection Methods and Machine Learning
by Luis Alberto Holgado-Apaza, Nelly Jacqueline Ulloa-Gallardo, Ruth Nataly Aragon-Navarrete, Raidith Riva-Ruiz, Naomi Karina Odagawa-Aragon, Danger David Castellon-Apaza, Edgar E. Carpio-Vargas, Fredy Heric Villasante-Saravia, Teresa P. Alvarez-Rozas and Marleny Quispe-Layme
Sustainability 2024, 16(17), 7532; https://doi.org/10.3390/su16177532 - 30 Aug 2024
Viewed by 1184
Abstract
Teacher life satisfaction is crucial for their well-being and the educational success of their students, both essential elements for sustainable development. This study identifies the most relevant predictors of life satisfaction among Peruvian teachers using machine learning. We analyzed data from the National [...] Read more.
Teacher life satisfaction is crucial for their well-being and the educational success of their students, both essential elements for sustainable development. This study identifies the most relevant predictors of life satisfaction among Peruvian teachers using machine learning. We analyzed data from the National Survey of Teachers of Public Basic Education Institutions (ENDO-2020) conducted by the Ministry of Education of Peru, using filtering methods (mutual information, analysis of variance, chi-square, and Spearman’s correlation coefficient) along with embedded methods (Classification and Regression Trees—CART; Random Forest; Gradient Boosting; XGBoost; LightGBM; and CatBoost). Subsequently, we generated machine learning models with Random Forest; XGBoost; Gradient Boosting; Decision Trees—CART; CatBoost; LightGBM; Support Vector Machine; and Multilayer Perceptron. The results reveal that the main predictors of life satisfaction are satisfaction with health, employment in an educational institution, the living conditions that can be provided for their family, and conditions for performing their teaching duties, as well as age, the degree of confidence in the Ministry of Education and the Local Management Unit (UGEL), participation in continuous training programs, reflection on the outcomes of their teaching practice, work–life balance, and the number of hours dedicated to lesson preparation and administrative tasks. Among the algorithms used, LightGBM and Random Forest achieved the best results in terms of accuracy (0.68), precision (0.55), F1-Score (0.55), Cohen’s kappa (0.42), and Jaccard Score (0.41) for LightGBM, and accuracy (0.67), precision (0.54), F1-Score (0.55), Cohen’s kappa (0.41), and Jaccard Score (0.41). These results have important implications for educational management and public policy implementation. By identifying dissatisfied teachers, strategies can be developed to improve their well-being and, consequently, the quality of education, contributing to the sustainability of the educational system. Algorithms such as LightGBM and Random Forest can be valuable tools for educational management, enabling the identification of areas for improvement and optimizing decision-making. Full article
(This article belongs to the Special Issue Artificial Intelligence in Education and Sustainable Development)
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