Advancing Sustainable Education Through AI and Technological Breakthroughs

Dear Colleagues,

We are pleased to invite you to submit proposals for a Special Issue titled “Advancing Sustainable Education Through AI and Technological Breakthroughs”. Sustainable education is central to achieving the United Nations Sustainable Development Goals (SDGs), particularly Quality Education (SDG 4). Sustainability education fosters a learning environment that values diversity, creativity, and active participation, empowering learners to address current and future social, environmental, and economic challenges. As emphasised by Sterling and Orr (2001), sustainable education not only focuses on content but also transforms the processes and contexts of learning. Integrating artificial intelligence (AI) and technological advancements into education is essential for developing the competencies required for sustainable development (Kamalov et al., 2023).

This Special Issue explores the synergy between AI technologies and sustainable education by presenting research on AI-driven tools such as Intelligent Tutoring Systems (ITSs) (Carbonell, 1970; Vanlehn, 2011) and Technology-Enhanced Learning (TEL) platforms (Kirkwood & Price, 2013). Both ITS and TEL systems play vital roles in personalising education by modelling students’ psychological states, such as motivation, emotion, and cognition (Garcia & Pintrich, 2023), while considering their prior knowledge and skills. These systems monitor progress, provide real-time feedback (Atif et al., 2020), and enhance learning outcomes (Ma et al., 2014).

Recent advancements in AI and machine learning have empowered ITS and TEL systems to offer more sophisticated and personalised learning experiences. AI-driven personalised learning systems use algorithms to create tailored experiences based on student data (Bhutoria, 2022), while tools like Natural Language Processing (NLP) enable chatbots to offer real-time feedback and interventions (Atif et al., 2021; Labadze et al., 2023). Machine learning enhances learning analytics and performance prediction, allowing educators to make data-driven decisions. To ensure transparency and trust, Explainable AI (XAI) is crucial in helping educators and students understand how AI systems arrive at their recommendations or predictions (Khosravi et al., 2022). Additionally, AI also supports student engagement and dropout prevention through predictive analytics, fostering inclusivity and sustainability in education (Pedro et al., 2019). Data mining and learning analytics derive valuable insights for policy and curriculum development (Ifenthaler et al., 2021; Ng et al., 2023). Integrating XAI into these systems ensures that educators can interpret AI-driven insights and make informed, accountable decisions that promote equity and fairness in learning environments.

Together, ITS and TEL methods, supported by AI, have the potential to revolutionise education into a sustainable, inclusive, and equitable system. This Special Issue invites contributions on the development, implementation, and impact of AI-driven educational technologies, focusing on educational quality, accessibility, and resource management to support sustainable development goals.

We invite submissions on the following themes:

• AI-driven personalised learning systems that tailor educational experiences based on student data.
• The design and effectiveness of ITSs in supporting diverse learning needs, including adaptive learning.
• AI tools that enhance student engagement and reduce dropout rates through predictive analytics and emotional recognition.
• The importance of transparency and interpretability in AI systems, including Explainable AI (XAI).
• The use of big data and machine learning for learning analytics and educational outcome analysis to inform policy decisions.
• Challenges and solutions for integrating AI into educational curricula, addressing issues of technological literacy, curriculum adaptation, and funding.
• Human- and teacher-centred AI tools that support personalised instruction and manage educator workloads.
• Case studies showcasing successful AI implementations in educational institutions.
• Theoretical frameworks and standardised approaches for integrating AI in sustainable education.

This Special Issue provides a platform for researchers, practitioners, and policymakers to share insights and advancements. By leveraging AI and technological breakthroughs, we can transform education into a more inclusive, efficient, and sustainable system that meets the needs of todays and future learners.

We encourage you to contribute and help shape the future of sustainable education through AI and technology. Your insights and research will play a crucial role in advancing educational practices that are both innovative and sustainable.

We look forward to your valuable contributions.

References:

• Atif, A., Jha, M., Richards, D., & Bilgin, A. A. (2021). Artificial Intelligence (AI)-enabled remote learning and teaching using pedagogical conversational agents and learning analytics. In S. Caballe, S. N. Demetriadis, E. Gomez-Sanchez, P. M. Papadopoulos, & A. Weinberger (Eds.), Intelligent systems and learning data analytics in online education (pp. 3-29). Academic Press. https://doi.org/10.1016/B978-0-12-823410-5.00013-9
• Atif, A., Richards, D., Liu, D., & Bilgin, A. A. (2020). Perceived benefits and barriers of a prototype early alert system to detect engagement and support ‘at-risk’ students: The teacher perspective. Computers & Education, 156, 103954. https://doi.org/10.1016/j.compedu.2020.103954
• Bhutoria, A. (2022). Personalised education and Artificial Intelligence in the United States, China, and India: A systematic review using a human-in-the-loop model. Computers and Education: Artificial Intelligence, 3, 100068. https://doi.org/10.1016/j.caeai.2022.100068
• Carbonell, J. R. (1970). AI in CAI: An Artificial Intelligence approach to Computer-Assisted Instruction. IEEE Transactions on Man-Machine Systems, 11(4), pp. 190-202. https://doi.org/10.1109/TMMS.1970.299942
• Garcia, T., & Pintrich, P. R. (2023). Regulating motivation and cognition in the classroom: The role of self-schemas and self-regulatory strategies. In B. J. Zimmerman & D. H. Schunk (Eds.), Self-regulation of learning and performance (e-ed., pp. 27-47). Routledge. https://doi.org/10.4324/9780203763353
• Ifenthaler, D., Gibson, D., Prasse, D., Shimada, A., & Yamada, M. (2021). Putting learning back into learning analytics: actions for policymakers, researchers, and practitioners. Educational Technology Research and Development, 69, pp. 2131-2150. https://doi.org/10.1007/s11423-020-09909-8
• Kamalov, F., Santandreu Calonge, D., & Gurrib, I. (2023). New era of Artificial Intelligence in education: Towards a sustainable multifaceted revolution. Sustainability, 15(16), 12451. https://doi.org/10.3390/su151612451
• Kirkwood, A., & Price, L. (2013). Technology-enhanced learning and teaching in higher education: what is ‘enhanced’ and how do we know? A critical literature review. Learning, Media and Technology, 39(1), pp. 6-36. https://doi.org/10.1080/17439884.2013.770404
• Khosravi, H., Buckingham Shum, S., Chen, G., Conati, C., Tsai, Y. S., Kay, J., Knight, S., Martinez-Maldonado, R., Sadiq, S., & Gasevic, D. (2022). Explainable Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 3, 100074. https://doi.org/10.1016/j.caeai.2022.100074
• Labadze, L., Grigolia, M., & Machaidze, L. (2023). Role of AI chatbots in education: systematic literature review. International Journal of Educational Technology in Higher Education, 20(1), 56. https://doi.org/10.1186/s41239-023-00426-1
• Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: A meta-analysis. Journal of Educational Psychology, 106(4), pp. 901-918.
• Ng, D. T. K., Lee, M., Tan, R. J. Y., Hu, X., Downie, J. S., & Chu, S. K. W. (2023). A review of AI teaching and learning from 2000 to 2020. Education and Information Technologies, 28(7), pp. 8445-8501. https://doi.org/10.1007/s10639-022-11491-w
• Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. https://hdl.handle.net/20.500.12799/6533
• Sterling, S., & Orr, D. (2001). Sustainable education: Re-visioning learning and change, 6. Green Books for the Schumacher Society Totnes.
• VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), pp. 197-221. https://doi.org/10.1080/00461520.2011.611369

Dr. Amara Atif
Dr. Florence Mwagwabi
Dr. Marcella Papini
Guest Editors

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• sustainable education
• artificial intelligence in education
• intelligent tutoring systems (ITS)
• technology-enhanced learning (TEL)
• learning analytics
• personalised learning
• adaptive learning
• explainable AI (XAI)
• student engagement
• AI-driven curriculum design