sustainability-logo

Journal Browser

Journal Browser

AI-Enhanced Sustainable Education: From Affective Computing to Intelligent Learning Ecosystems

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

Deadline for manuscript submissions: 31 December 2026 | Viewed by 409

Special Issue Editors


E-Mail Website
Guest Editor

E-Mail Website
Guest Editor
Department of Electrical Engineering, YuanZe University, Taoyuan, Taiwan
Interests: AI-driven educational technology; personalized programming education; full-stack system development; machine learning models in education; service-oriented architecture (SOA)

E-Mail Website
Guest Editor
Wind Power Program, MingHsing University of Science and Technology, Hsinchu, Taiwan
Interests: Industry 4.0; smart manufacturing; robotics; UAVs; generative AI; LLMs; big data analytics; biomedical informatics and systems biology

Special Issue Information

Dear Colleagues,

As we face global challenges that threaten our way of life, education is more important than ever. UNESCO’s Education for Sustainable Development (ESD) is a key response to these urgent issues. The ESD for 2030 program specifically emphasizes policy guidance, technical support, and innovation networks to achieve the SDGs. However, traditional teaching methods alone cannot meet these growing demands. In the digital age, Artificial Intelligence (AI) has become a critical force that cannot be ignored. This Special Issue proposes that AI-Enhanced Sustainable Education is the essential foundation needed to achieve these global goals. We explore how intelligent systems can turn abstract sustainability concepts into practical, scalable educational practices.

To support UNESCO's call for data-driven policy and equity, we invite research on learning analytics for early intervention and reducing dropout rates. To foster innovation, we seek contributions using Generative AI and immersive environments to enhance content accessibility. Furthermore, to support psychological well-being, we emphasize affective computing and intelligent pedagogical agents for social-emotional learning. Crucially, we address the need for ethical AI and privacy protection to build trustworthy systems. Finally, to ensure long-term resilience, we welcome studies on full-stack architectures and AI-empowered teacher professional development. This Special Issue aims to move from isolated tools to comprehensive Intelligent Learning Ecosystems that actively advance the global sustainability agenda.

This Special Issue encompasses a broad range of cutting-edge research areas, including the following:

  • Affective computing and emotion-aware educational technologies for social-emotional learning and psychological well-being;
  • AI-driven personalized learning systems and adaptive learning platforms;
  • Intelligent pedagogical agents and conversational AI in education;
  • Generative AI and large language models (LLMs) for educational innovation;
  • Learning analytics for early intervention and reducing dropout rates;
  • Full-stack intelligent educational systems and service-oriented architectures;
  • Interdisciplinary integration of AI, education, and emerging technologies;
  • Gamification, game-based learning, and immersive AI environments;
  • Ethical AI, algorithmic fairness, and privacy in sustainable digital ecosystems;
  • Empowering teachers with AI for sustainable pedagogical transformation.

Prof. Dr. Hao-Chiang Koong Lin
Dr. Chun-Hsiung Tseng
Dr. Yung-Hao Wong
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. 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

  • AI-enhanced sustainable education
  • education for sustainable development (ESD)
  • learning analytics
  • generative ai and LLMs
  • affective computing
  • intelligent learning ecosystems
  • SDGs

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.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

27 pages, 1019 KB  
Article
A Data-Driven Framework for Game-Based Nutrition Education: Supporting Sustainable Learning and Healthy Behaviors
by Qian Wang, Khachakrit Liamthaisong and Jantima Polpinij
Sustainability 2026, 18(10), 4797; https://doi.org/10.3390/su18104797 - 11 May 2026
Viewed by 80
Abstract
Creating effective computer-assisted learning (CAL) environments for children remains challenging, particularly in sustaining motivation, engagement, and meaningful learning outcomes. While educational games are widely used to address these challenges, many studies rely on post hoc evaluation rather than incorporating data-driven insights into the [...] Read more.
Creating effective computer-assisted learning (CAL) environments for children remains challenging, particularly in sustaining motivation, engagement, and meaningful learning outcomes. While educational games are widely used to address these challenges, many studies rely on post hoc evaluation rather than incorporating data-driven insights into the design process. This study presents an exploratory design framework that uses clustering of educational game reviews and sentiment-informed stakeholder insights as design drivers to guide the development of a dual-format nutrition-focused learning environment. The framework integrates established learning analytics techniques, including clustering, behavioral analysis, and sentiment analysis, with pedagogical approaches such as user-centered design, gamification, and interactive learning. An illustrative evaluation was conducted using multiple data sources, including the China Nutrition and Health Survey (CHNS), 1500 educational game reviews, and a classroom-based implementation involving 120 s-grade students. All participants engaged with both board-based and computer-based formats. The analysis identified three design-relevant themes—content engagement, visual appeal, and motivational mechanisms—which were used to inform the development of the learning environments. The results suggest improvements in knowledge-related outcomes, along with observable patterns of learner engagement across interaction formats. The dual-format design was associated with specific engagement patterns, including socially mediated interaction and individual participation. These findings are interpreted as indicative rather than causal. From an educational sustainability perspective, the findings are considered in terms of engagement continuity, outcome consistency, and design adaptability. Full article
Show Figures

Figure 1

Back to TopTop