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Artificial Intelligence Technologies for Sustainable Development
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Artificial Intelligence Technologies for Sustainable Development

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

Deadline for manuscript submissions: 31 May 2026 | Viewed by 797

Special Issue Editors

Dr. Alessia Amelio

E-Mail Website
Guest Editor
Department InGeo, University "G. d'Annunzio" Chieti-Pescara, 65127 Pescara, Italy
Interests: artificial intelligence; pattern recognition; data analysis; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. David Perpetuini

E-Mail Website
Guest Editor
Department InGeo, University "G. d'Annunzio" Chieti-Pescara, 65127 Pescara, Italy
Interests: artificial intelligence; bioengineering; signal processing; human–computer interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the context of disruptive technologies, Artificial Intelligence (AI) is playing an increasingly vital role in shaping the future of digital innovation, offering transformative capabilities that mimic human reasoning, learning, and interaction. With the rapid advancement of generative AI, such as large language models, AI is revolutionizing how we interact with machines and process information. These developments call for responsible and ethical frameworks to ensure AI systems are aligned with human values and societal well-being.

This Special Issue welcomes contributions on how AI can be harnessed to support the United Nations’ 2030 Sustainable Development Goals (SDGs), which aim to address urgent global challenges including poverty, hunger, health, education, clean energy, climate action, and sustainable ecosystems. AI technologies—ranging from machine learning and deep learning to robotics, natural language processing, and optimization—offer powerful tools for enhancing decision making, improving efficiency, and enabling data-driven solutions that promote environmental, economic, and social sustainability.

We invite original research and survey papers, as well as practical application papers, that demonstrate how AI can contribute to sustainable development across sectors. Contributions may include innovative methodologies, case studies, and interdisciplinary approaches that highlight AI’s potential to foster inclusive growth, resilience, and long-term sustainability in line with global development priorities.

Dr. Alessia Amelio
Dr. David Perpetuini
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. 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 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

  • sustainable AI
  • responsible AI
  • healthcare innovation
  • education technology
  • human-centric AI
  • environmental monitoring
  • digital public services
  • ethical AI systems

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Published Papers (2 papers)

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25 pages, 462 KB  
Article
ARIA: An AI-Supported Adaptive Augmented Reality Framework for Cultural Heritage
by Markos Konstantakis and Eleftheria Iakovaki
Information 2026, 17(1), 90; https://doi.org/10.3390/info17010090 - 15 Jan 2026
Viewed by 118
Abstract
Artificial Intelligence (AI) is increasingly reshaping how cultural heritage institutions design and deliver digital visitor experiences, particularly through adaptive Augmented Reality (AR) applications. However, most existing AR deployments in museums and galleries remain static, rule-based, and insufficiently responsive to visitors’ contextual, behavioral, and [...] Read more.
Artificial Intelligence (AI) is increasingly reshaping how cultural heritage institutions design and deliver digital visitor experiences, particularly through adaptive Augmented Reality (AR) applications. However, most existing AR deployments in museums and galleries remain static, rule-based, and insufficiently responsive to visitors’ contextual, behavioral, and emotional diversity. This paper presents ARIA (Augmented Reality for Interpreting Artefacts), a conceptual and architectural framework for AI-supported, adaptive AR experiences in cultural heritage settings. ARIA is designed to address current limitations in personalization, affect-awareness, and ethical governance by integrating multimodal context sensing, lightweight affect recognition, and AI-driven content personalization within a unified system architecture. The framework combines Retrieval-Augmented Generation (RAG) for controlled, knowledge-grounded narrative adaptation, continuous user modeling, and interoperable Digital Asset Management (DAM), while embedding Human-Centered Design (HCD) and Fairness, Accountability, Transparency, and Ethics (FATE) principles at its core. Emphasis is placed on accountable personalization, privacy-preserving data handling, and curatorial oversight of narrative variation. ARIA is positioned as a design-oriented contribution rather than a fully implemented system. Its architecture, data flows, and adaptive logic are articulated through representative museum use-case scenarios and a structured formative validation process including expert walkthrough evaluation and feasibility analysis, providing a foundation for future prototyping and empirical evaluation. The framework aims to support the development of scalable, ethically grounded, and emotionally responsive AR experiences for next-generation digital museology. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Sustainable Development)
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22 pages, 530 KB  
Article
Using Multi-Criteria Decision-Making for Evaluating SDGs’ Implementation on Higher Education Institutions: A Framework
by Maria Kaloutsa, Katerina Kabassi and Aristotelis Martinis
Information 2025, 16(12), 1083; https://doi.org/10.3390/info16121083 - 6 Dec 2025
Viewed by 358
Abstract
This paper proposes a framework for evaluating the sustainability of higher education institutions (HEIs) using a combination of Analytic Hierarchy Process (AHP) and TOPSIS. This approach aims to align higher education institutions’ activities with the Sustainable Development Goals (SDGs) set by the United [...] Read more.
This paper proposes a framework for evaluating the sustainability of higher education institutions (HEIs) using a combination of Analytic Hierarchy Process (AHP) and TOPSIS. This approach aims to align higher education institutions’ activities with the Sustainable Development Goals (SDGs) set by the United Nations (UN). It addresses shortcomings in existing evaluation systems, such as a lack of transparency and insufficient consideration of institutional diversity. The framework uses a comprehensive set of 34 indicators, divided into policy-based and data-driven categories, to measure alignment with all 17 SDGs. AHP is applied to determine the relative importance of each criterion, ensuring a balanced evaluation based on expert input. The TOPSIS method was then used to rank universities based on their proximity to an ideal performance level. The framework is noted for its flexibility, transparency, and ability to generate practical recommendations, although challenges such as reliance on expert judgment and data limitations are acknowledged. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Sustainable Development)
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