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

Deadline for manuscript submissions: 25 August 2026 | Viewed by 5234

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Special Issue Editors

Dr. Chunfang Zhou

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Guest Editor

Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
Interests: human-AI co-creative learning experience; creativity studies; sustainability in education, AI and education; STEM education; pedagogical design; problem- and project-based learning; teacher education and professional development
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Connie Svabo

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Guest Editor

Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
Interests: AI-enhanced experiential learning; creative learning environments; spatial experience design in education; human-AI collaboration for sustainability; transdisciplinary approaches to learning in complex socio-ecological systems; distributed and embodied cognition in education
Special Issues, Collections and Topics in MDPI journals

Dr. Serhii Petrovych

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Guest Editor

Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
Interests: the use of AI in STEM education, project-based and problem-based learning in STEM; learning with microcontrollers and virtual labs; ICT in competency-based learning.

Special Issue Information

Dear Colleagues,

The integration of artificial intelligence (AI) into education presents a unique opportunity to advance both creativity and sustainability in teaching and learning, fostering educational systems and cultures that are better equipped to meet the challenges of a changing world. As we face escalating environmental challenges, cultivating a generation of learners equipped with sustainable and creative thinking practices is imperative. This Special Issue titled "AI for Sustainable and Creative Learning in Education" seeks to explore how AI technologies can facilitate sustainable educational frameworks and pedagogies, thereby contributing to the broader discourse on sustainability in education across disciplines and contexts.

The primary aim of this Special Issue is to highlight innovative applications of AI that promote sustainable education, aligning with the mission of the journal to address interdisciplinary approaches to sustainability challenges. In addition to technological and pedagogical innovation, we encourage work that engages with theoretical perspectives on human and more-than-human learning systems. Relevant perspectives include media ecology, ecology of mind, distributed ontologies, embodied cognition, and the archaeology of mind. Such approaches can illuminate how AI may support the evolution of thinking and cognitive development in complex socio-ecological contexts. We welcome contributions that examine the effectiveness of AI-driven tools in fostering learning, enhancing curricular sustainability, and improving educational access and equity while attending to ethical, cultural, and relational dimensions of AI in education.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

Adaptive learning technologies for sustainability
AI-enhanced curriculum design
Human-AI co-creative learning experiences
Impacts of AI in education
AI literacy development
AI and professional development
AI ethics in education
AI and caring for inclusive education
AI, education, and social-technology system
AI in distributed and embodied cognition
AI in the design and mediation of learning environments
AI for spatially and materially situated learning
Evolutionary approaches to thinking and learning in human-AI-environment relations

We look forward to receiving your contributions.

Dr. Chunfang Zhou
Prof. Dr. Connie Svabo
Dr. Serhii Petrovych
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
sustainability
creativity
human-AI interaction
AI literacy
education
teaching
learning
pedagogical design
professional development
ecology of mind
distributed ontologies
embodied cognition
cognitive evolution
learning ecologies
learning environments
educational technologies

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Published Papers (7 papers)

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Research

24 pages, 318 KB

Open AccessArticle

“I’m Not as Good as AI”: The Impact of Generative AI Use on Learning Anxiety and Self-Efficacy

by Tao Jiang and Yan Xu

Sustainability 2026, 18(8), 3869; https://doi.org/10.3390/su18083869 - 14 Apr 2026

Viewed by 529

Abstract

This study investigates whether metacognitive prompting for responsible generative AI (GenAI) use can enhance students’ psychological sustainability in AI-assisted learning. Using a face-to-face classroom experiment (N = 148; 74 prompting, 74 control), we examined how metacognitive prompts embedded in a GenAI-assisted academic task influence learning anxiety and academic self-efficacy, and whether anxiety mediates the effect on self-efficacy. Manipulation checks indicated that the prompting condition produced significantly higher metacognitive engagement than the control condition (t(146) = 7.50, p < 0.001, d = 1.23). Hypothesis tests showed that metacognitive prompting reduced learning anxiety (b = −0.68, p < 0.001) and increased academic self-efficacy (b = 0.40, p = 0.008). Learning anxiety was negatively associated with self-efficacy (b = −0.42, p < 0.001). Mediation analyses using bootstrap confidence intervals revealed a significant indirect effect of prompting on self-efficacy via reduced anxiety (ab = 0.26, 95% CI [0.12, 0.43]), indicating partial mediation. These findings suggest that responsible GenAI use can be supported through instructional design. Brief metacognitive prompts may help students regulate AI use, reduce learning anxiety, and maintain academic self-efficacy. More broadly, the study contributes to sustainable education and educational technology research by showing that classroom scaffolds can support student agency and well-being in AI-assisted learning. Full article

(This article belongs to the Special Issue AI for Sustainable and Creative Learning in Education)

25 pages, 458 KB

Open AccessArticle

Integrating Creative Problem Solving and Generative AI in Animation Education: Advancing Sustainability-Related Competencies in Higher Education

by Jui-Hsiang Lee

Sustainability 2026, 18(8), 3858; https://doi.org/10.3390/su18083858 - 14 Apr 2026

Viewed by 562

Abstract

This study examines how integrating Creative Problem Solving (CPS) and generative artificial intelligence (GenAI) within animation storytelling education can foster sustainability-related competencies in higher education. A twelve-week mixed-methods action research design was implemented in a “Storytelling and Scriptwriting” course at a university of technology in northern Taiwan (N = 60). The intervention design combined a CPS-aligned instructional sequence, six scaffolded assignments (including a text-to-image resemiotization task), pre–post CPS cognition and affect scales, CPS-dimensioned assignment self-assessments, reflective journals, and expert evaluations of final story prototypes using the Consensual Assessment Technique. Quantitative results showed significant gains in students’ CPS-related narrative cognition and affective resilience (p < 0.001), as well as consistently high self-reported engagement across CPS dimensions for all assignments, particularly for the text-to-image and personal narrative tasks. Expert ratings indicated high levels of originality, narrative coherence, emotional impact, and social relevance in final prototypes, while qualitative data highlighted reduced “blank page” anxiety, greater willingness to revise, and more collaborative, systems-oriented narrative reasoning. The findings suggest that a CPS- and GenAI-supported teaching model can function as a cognitive bridge for heterogeneous cohorts, positioning GenAI as a conditional amplifier embedded within a reflective CPS framework and helping to translate abstract sustainability-related competencies—such as anticipatory, normative, strategic, and interpersonal competencies—into concrete creative media practices. Full article

(This article belongs to the Special Issue AI for Sustainable and Creative Learning in Education)

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25 pages, 470 KB

Open AccessArticle

Digital Experiential Learning Ecosystems and Perceived Sustainability Outcomes: A Partial Mediation Model of Learning Engagement

by Kholoud Maswadi, Yonis Gulzar, Tahir Hakim and Mohammad Shuaib Mir

Sustainability 2026, 18(8), 3738; https://doi.org/10.3390/su18083738 - 9 Apr 2026

Viewed by 528

Abstract

The rapid adoption of immersive and adaptive digital technologies is redefining sustainability education, but the mechanisms by which these technologies support perceived sustainability outcomes remain unclear. This paper models the Digital Experiential Learning Ecosystem (DELE), including simulation, AR/VR, gamification, AI personalization, and collaborative digital platforms, as a higher-order construct. It discusses its role in Perceived Sustainability Outcomes through learning engagement. Basing the study on the Stimulus-Organism-Response framework, the study hypothesizes that the digital ecosystem design can be viewed as an environmental stimulus, engagement as the organismic processing state, and Perceived Sustainability Outcomes as the developmental response. The results, obtained using Partial Least Squares Structural Equation Modeling (PLS-SEM), indicate that DELE is positively associated with learning engagement and Perceived Sustainability Outcomes. Learning engagement is found to be the leading mechanism through which digital experiential environments are converted into perceived sustainability outcomes, but a smaller yet significant direct structural relationship also remains. These findings indicate that digital transformation within the education sector creates sustainable value not only through technological sophistication but also through carefully planned engagement-based learning environments that support systems thinking, applied problem-solving, and adaptive readiness to work in multifaceted environments. The research also advances the body of research on sustainability education by developing a model of digital learning as an integrated ecosystem and by explaining the psychological and structural processes of perceived sustainability outcomes. Full article

(This article belongs to the Special Issue AI for Sustainable and Creative Learning in Education)

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19 pages, 10048 KB

Open AccessArticle

How AI-Assisted Decision-Making Paradigms and Explainability Shape Human-AI Collaboration

by Yingying Wang, Qin Ni, Tingjiang Wei, Haoxin Xu, Lu Liu and Liang He

Sustainability 2026, 18(7), 3516; https://doi.org/10.3390/su18073516 - 3 Apr 2026

Viewed by 470

Abstract

The increasing integration of artificial intelligence (AI) in educational decision-making raises a critical question: how to design AI systems that can effectively support teachers while maintaining an appropriate level of trust. Addressing this question requires not only continuous improvements in the technical capabilities of AI systems but also an examination from a human-AI interaction perspective of how different system designs influence users’ cognitive performance and affective responses, thereby providing guidance for system optimization and design. Therefore, this study conducted a randomized controlled experiment with 120 pre-service teachers to investigate how AI-assisted decision-making paradigms and AI explainability jointly influence teachers’ task performance and trust in AI, and whether these effects transfer to subsequent independent tasks. The results indicate that the effect of explanatory interface on task performance is context dependent and yields an immediate positive impact. Under the concurrent paradigm, the explanatory interface of the AI system significantly improves immediate task performance, whereas no significant effect is observed under the sequential paradigm. Moreover, this improvement is confined to the task execution stage and does not transfer to subsequent independent tasks. In contrast, the effect of explanatory interface on trust exhibits a delayed and negative pattern. The explanatory interface has no significant impact on situational trust, while it exerts a negative effect on learned trust and suppresses the natural development of both cognitive trust and emotional trust. In addition, different AI-assisted decision-making paradigms exhibit distinct patterns of influence on task performance and trust. Although the concurrent paradigm performs worse than the sequential paradigm in terms of immediate task performance, it is more effective in promoting users’ emotional trust. Overall, these findings extend the theoretical understanding of the mechanisms of explainability in human-AI interaction and provide empirical evidence for the joint design of explainable AI systems and human-AI collaboration paradigms. Full article

(This article belongs to the Special Issue AI for Sustainable and Creative Learning in Education)

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37 pages, 688 KB

Open AccessArticle

The Role of Generative Artificial Intelligence in Advancing Sustainable and Environmentally Responsible Teaching Practices Among Postgraduate Students

by Azhar Saleh Abdulhadi Al-Shamrani, Reem Ebraheem Saleh Alhomayani and Asem Mohammed Ibrahim

Sustainability 2026, 18(5), 2450; https://doi.org/10.3390/su18052450 - 3 Mar 2026

Viewed by 619

Abstract

Generative Artificial Intelligence (GAI) is rapidly reshaping pedagogical practices and offering new opportunities to advance sustainability within higher education. This study investigates the extent to which postgraduate students utilize GAI to support Sustainable and Environmentally Responsible Teaching Practices (SERTPs), and examines whether this use varies across demographic, academic, and technological characteristics. A descriptive quantitative design was employed, involving 310 postgraduate students from the College of Education at King Khalid University. Data were collected using a validated and highly reliable instrument measuring five dimensions of GAI-supported sustainable teaching. Descriptive and inferential analyses, including t tests, one-way ANOVA, and LSD post hoc comparisons, were conducted. The findings reveal that postgraduate students demonstrate a moderate overall level of GAI use in advancing SERTPs, with the highest engagement occurring in the promotion of sustainable educational practices. Significant differences were only found in relation to students’ levels of technology use and students’ levels of GAI use, indicating that frequent and sophisticated engagement with AI tools is the strongest predictor of sustainable teaching practices. No significant differences emerged across gender, age, academic department, program level, or specialization. The study highlights the need for targeted training and institutional strategies that enhance students’ AI proficiency, thereby enabling GAI to serve as a catalyst for environmentally responsible and sustainable teaching practices in higher education. Full article

(This article belongs to the Special Issue AI for Sustainable and Creative Learning in Education)

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41 pages, 2707 KB

Open AccessArticle

Prompt Engineering and Multimodal Tasks in AI-Supported EFL Education: A Mixed Methods Study

by Debopriyo Roy, George F. Fragulis and Adya Surbhi

Sustainability 2026, 18(5), 2415; https://doi.org/10.3390/su18052415 - 2 Mar 2026

Viewed by 949

Abstract

The rapid integration of artificial intelligence (AI) into higher education is reshaping how learners develop academic, linguistic, and research competencies. This mixed-methods study examines how second-year EFL computer science students employ prompt engineering techniques across four task domains—research summarization, academic video note-taking, style transformation, and concept mapping—within a smart learning environment. Sixty-nine students completed a structured survey requiring AI-assisted draft generation followed by student-led revision. Quantitative analyses included descriptive statistics, chi-square tests, Cramer’s V, t-tests, ANOVA, Kruskal–Wallis tests, and three text-similarity measures (cosine, Jaccard, and Levenshtein). Qualitative evidence was drawn from students’ revised outputs and reflective responses. Results indicate that students consistently preserved semantic meaning while significantly rephrasing AI-generated text, demonstrating moderate conceptual alignment but substantial lexical and structural transformation. Frequent AI users said they were better at searching and revising, but the type of prompt didn’t have much of an effect on how deep the revision was or how well they learned. Iterative prompting and revision emerged as central drivers of metacognitive growth, academic language development, and sustainable learning behaviors. Across tasks, students viewed AI prompts as effective scaffolds for organizing information and synthesizing multimodal input, though reliance varied by learner. The findings underscore that sustainable AI use in EFL technical education depends not on AI output alone, but on structured prompting, iterative human revision, and critical engagement—practices that cultivate autonomy, digital literacy, and long-term academic resilience. Full article

(This article belongs to the Special Issue AI for Sustainable and Creative Learning in Education)

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15 pages, 1226 KB

Open AccessArticle

Knowledge Graphs as Cognitive Scaffolding for Sustainable Engineering Education: A Quasi-Experimental Study in Structural Geology

by Xiaoling Tang, Jinlong Ni, Yuanku Meng, Qiao Chen and Liping Zhang

Sustainability 2026, 18(2), 736; https://doi.org/10.3390/su18020736 - 10 Jan 2026

Viewed by 480

Abstract

The transition to Outcome-Based Education (OBE) in engineering demands instructional tools that bridge theoretical knowledge and practical engineering competencies. However, traditional Learning Management Systems (LMS) primarily function as static resource repositories, lacking the semantic structure necessary to support deep learning and precise competency tracking. To address this, this study developed a three-layer domain Knowledge Graph (KG) for Structural Geology and integrated it into the ChaoXing LMS (a widely used Learning Management System in Chinese higher education). A semester-long quasi-experimental study (N = 84) was conducted to evaluate its impact on student performance and specific graduation attribute achievement compared to a conventional folder-based approach. Empirical results demonstrate that the KG-integrated group significantly outperformed the control group (p < 0.01, Cohen’s d = 0.74). Notably, while performance on rote memorization tasks was similar, the experimental group showed marked improvement in identifying and solving complex engineering problems. LMS log analysis confirmed a strong positive correlation (r = 0.68) between graph navigation depth and academic success. KG effectively bridged the gap between theoretical knowledge and practical engineering applications (e.g., geohazard analysis). This research confirms that explicit semantic visualization acts as vital cognitive scaffolding, effectively enhancing higher-order thinking and ensuring the rigorous alignment of instruction with engineering accreditation standards. Ultimately, this approach promotes sustainable learning capabilities and prepares future engineers to address complex, interdisciplinary challenges in sustainable development. Full article

(This article belongs to the Special Issue AI for Sustainable and Creative Learning in Education)

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