Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.7
3.3
Journals
Sustainability
Special Issues
Generative Artificial Intelligence (GAI) and AI-Generated Content (AIGC) for Sustainable...
sustainability-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sustainability Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Generative Artificial Intelligence (GAI) and AI-Generated Content (AIGC) for Sustainable Futures: Innovations in Education, Culture, and Society

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: 30 June 2026 | Viewed by 3925

Special Issue Editor

Prof. Dr. Hao-Chiang Koong Lin

E-Mail Website
Guest Editor
Department of Information and Learning Technology, National University of Tainan, Tainan, Taiwan
Interests: affective computing; artificial intelligence; digital learning; educational technology; metaverse; digital arts
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the transformative potential of Generative Artificial Intelligence (GAI) and AI-Generated Content (AIGC) in driving sustainability across education, culture, and social innovation. As GAI and AIGC technologies rapidly evolve, they offer new pathways for promoting Sustainable Development Goals (SDGs), enabling personalized learning, fostering creative industries, and enhancing public awareness of environmental and social challenges. With this, we invite interdisciplinary contributions that address theoretical frameworks, empirical studies, case applications, ethical implications, and future trends at the intersection of generative technologies and sustainability. Topics of interest include, but are not limited to the following: AI-assisted environmental storytelling, digital cultural heritage preservation, personalized sustainability education, creative content for climate action, and AI-driven social impact initiatives.

This Special Issue will provide a timely and essential collection of research that moves beyond the technical aspects of GAI and AIGC to explore their profound societal impact and their capacity to accelerate progress towards a sustainable future. It will serve as a vital resource for academics, practitioners, and policymakers seeking to understand and harness these technologies responsibly for the greater good, while also critically examining the limitations and ethical implications of AI in creative and social contexts.

Prof. Dr. Hao-Chiang Koong Lin
Guest Editor

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

  • generative artificial intelligence (GAI)
  • AI-generated content (AIGC)
  • sustainability
  • sustainable development goals (SDGs)
  • personalized learning
  • creative industries
  • environmental storytelling

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 (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

29 pages, 11583 KB  
Article
The MTA-TPACK Dynamic Collaboration Spiral: Making Pedagogical Thinking Visible in Human–AI Scientific Visualization for Sustainable Teacher Innovation
by Hung-Cheng Chen and Lung-Hsiang Wong
Sustainability 2026, 18(6), 2718; https://doi.org/10.3390/su18062718 - 11 Mar 2026
Viewed by 542
Abstract
Generative AI (GenAI) challenges traditional technology integration frameworks by introducing agentic systems that actively participate in meaning-making, requiring educators to shift from tool operation to cognitive orchestration. This study introduces the MTA–TPACK Dynamic Collaboration Spiral, a theoretical framework that integrates Meta-Task Awareness (MTA) [...] Read more.
Generative AI (GenAI) challenges traditional technology integration frameworks by introducing agentic systems that actively participate in meaning-making, requiring educators to shift from tool operation to cognitive orchestration. This study introduces the MTA–TPACK Dynamic Collaboration Spiral, a theoretical framework that integrates Meta-Task Awareness (MTA) to explain how static knowledge resources are dynamically activated during human–AI collaboration. We empirically illustrate this framework through a two-phase scientific visualization task concerning typhoon–terrain interactions, utilizing Midjourney for human-led orchestration and GPT-4o for closed-loop refinement. The results demonstrate that successful integration requires translating abstract disciplinary knowledge into precise, AI-intelligible visual constraints rather than relying solely on technical prompting skills. Furthermore, we document how evaluation practices evolve from simple error correction to structured, AI-assisted diagnosis. The resulting visual artifacts embody Visible Pedagogical Thinking (VPT)—externalized cognitive constructs that make expert reasoning inspectable and reusable. By foregrounding evaluation-centered task design, this study provides a transferable, theoretically grounded account of how human–AI collaboration can remain pedagogically meaningful. The model contributes to sustainable pedagogical innovation by offering a roadmap for strengthening teachers’ long-term epistemic agency in AI-mediated design environments. Full article
(This article belongs to the Special Issue Generative Artificial Intelligence (GAI) and AI-Generated Content (AIGC) for Sustainable Futures: Innovations in Education, Culture, and Society)
►▼ Show Figures

Figure 1

26 pages, 758 KB  
Article
Writing Is Coding for Sustainable Futures: Reimagining Poetic Expression Through Human–AI Dialogues in Environmental Storytelling and Digital Cultural Heritage
by Hao-Chiang Koong Lin, Ruei-Shan Lu and Tao-Hua Wang
Sustainability 2025, 17(15), 7020; https://doi.org/10.3390/su17157020 - 1 Aug 2025
Cited by 1 | Viewed by 2224
Abstract
In the era of generative artificial intelligence, writing has evolved into a programmable practice capable of generating sustainable narratives and preserving cultural heritage through poetic prompts. This study proposes “Writing Is Coding ” as a paradigm for sustainability education, exploring how students engage [...] Read more.
In the era of generative artificial intelligence, writing has evolved into a programmable practice capable of generating sustainable narratives and preserving cultural heritage through poetic prompts. This study proposes “Writing Is Coding ” as a paradigm for sustainability education, exploring how students engage with AI-mediated multimodal creation to address environmental challenges. Using grounded theory methodology with 57 twelfth-grade students from technology-integrated high schools, we analyzed their experiences creating environmental stories and digital cultural artifacts using MidJourney, Kling, and Sora. Data collection involved classroom observations, semi-structured interviews, and reflective journals, analyzed through systematic coding procedures (κ = 0.82). Five central themes emerged: writing as algorithmic design for sustainability (89.5%), emotional scaffolding for environmental awareness (78.9%), aesthetics of imperfection in cultural preservation (71.9%), collaborative dynamics in sustainable creativity (84.2%), and pedagogical value of prompt literacy (91.2%). Findings indicate that AI deepens environmental consciousness and reframes writing as a computational process for addressing global issues. This research contributes a theoretical framework integrating expressive writing with algorithmic thinking in AI-assisted sustainability education, aligned with SDGs 4, 11, and 13. Full article
(This article belongs to the Special Issue Generative Artificial Intelligence (GAI) and AI-Generated Content (AIGC) for Sustainable Futures: Innovations in Education, Culture, and Society)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop