AI-Powered Approaches for Sustainable Environmental Education in the Digital Age: A Study of Chongqing International Kindergarten

Muhammad Arif; Aneta Ismail; Sadia Irfan

doi:10.55151/ijeedu.v7i1.184

Authors

Muhammad Arif Faculty of Education, Southwest University, Beibei District, Chongqing 400715, China https://orcid.org/0000-0002-2737-5086
Aneta Ismail College of Nationalities and History, Southwest University, Beibei District, Chongqing 400715, China
Sadia Irfan Faculty of Education, Southwest University, Beibei District, Chongqing 400715, China

DOI:

https://doi.org/10.55151/ijeedu.v7i1.184

Keywords:

Artificial intelligence (AI), Environmental Awareness, Ecological Intelligence, Transformative Learning, Virtual Environments

Abstract

The integration of technology into education has garnered significant interest, particularly in its potential to support environmental sustainability. This exploratory research investigates the role of artificial intelligence (AI) in early childhood education, with a focus on its implementation at Chongqing International Kindergarten to teach sustainability concepts. Guided by Mezirow’s Theory of Transformative Learning and Goleman’s concept of Ecological Intelligence, the study explores how AI-powered tools—including virtual ecosystems, real-time feedback systems, and eco-conscious behavior tracking mechanisms—enhance critical reflection, foster ecological intelligence, and promote environmentally responsible behaviors among young learners. A qualitative case study approach was employed, incorporating classroom observations, educator interviews, and pre-and post-assessments to evaluate engagement, environmental awareness, and behavioral changes. The findings reveal that AI tools significantly enhance environmental literacy, helping children understand the consequences of their actions and encouraging them to adopt sustainable practices. Interactive and personalized learning experiences AI provides stimulate critical thinking, transform sustainability-related values, and foster a deeper understanding of ecological interconnections. Students demonstrated improved awareness of sustainability concepts, such as resource conservation and biodiversity, alongside increased engagement in eco-friendly behaviors, including recycling and energy conservation. This research highlights the transformative potential of AI in early education, demonstrating its capacity to influence children's attitudes and behaviors toward environmental responsibility. Integrating AI-driven educational tools into sustainability curricula is crucial for cultivating a generation capable of making informed environmental decisions. The study concludes by recommending further exploration of AI's role in early education to optimize its impact on fostering long-term ecological intelligence and transformative learning.

References

[1] S. Moya and M. Camacho, “Leveraging AI-powered mobile learning: A pedagogically informed framework,” Comput. Educ. Artif. Intell., vol. 7, p. 100276, 2024, doi: 10.1016/j.caeai.2024.100276.

[2] R. Gruetzemacher and J. Whittlestone, “The transformative potential of artificial intelligence,” Futures, vol. 135, p. 102884, 2022, doi: 10.1016/j.futures.2021.102884.

[3] J. Southworth et al., “Developing a model for AI Across the curriculum: Transforming the higher education landscape via innovation in AI literacy,” Comput. Educ. Artif. Intell., vol. 4, p. 100127, 2023, doi: 10.1016/j.caeai.2023.100127.

[4] D. Goleman, Ecological intelligence : knowing the hidden impacts of what we buy. Crown Currency, 2009.

[5] H. Khosravi et al., “Explainable Artificial Intelligence in education,” Comput. Educ. Artif. Intell., vol. 3, p. 100074, 2022, doi: 10.1016/j.caeai.2022.100074.

[6] I. Celik, “Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education,” Comput. Human Behav., vol. 138, p. 107468, 2023, doi: 10.1016/j.chb.2022.107468.

[7] G. J. Hwang, H. Xie, B. W. Wah, and D. Gašević, “Vision, challenges, roles and research issues of Artificial Intelligence in Education,” Computers and Education: Artificial Intelligence, vol. 1. Elsevier, p. 100001, 2020, doi: 10.1016/j.caeai.2020.100001.

[8] A. Abulibdeh, E. Zaidan, and R. Abulibdeh, “Navigating the confluence of artificial intelligence and education for sustainable development in the era of industry 4.0: Challenges, opportunities, and ethical dimensions,” J. Clean. Prod., vol. 437, p. 140527, 2024, doi: 10.1016/j.jclepro.2023.140527.

[9] M. Javaid, A. Haleem, R. P. Singh, R. Suman, and E. S. Gonzalez, “Understanding the adoption of Industry 4.0 technologies in improving environmental sustainability,” Sustain. Oper. Comput., vol. 3, pp. 203–217, 2022, doi: 10.1016/j.susoc.2022.01.008.

[10] A. Alam and A. Mohanty, “Integrated constructive robotics in education (ICRE) model: a paradigmatic framework for transformative learning in educational ecosystem,” Cogent Educ., vol. 11, no. 1, p. 2324487, 2024, doi: 10.1080/2331186X.2024.2324487.

[11] Y. Shang, Y. Xie, and W. Chen, “Addressing resource scarcity and fostering sustainability through environmental education in Asia,” Resour. Policy, vol. 90, p. 104823, 2024, doi: 10.1016/j.resourpol.2024.104823.

[12] Á. Zsóka, Z. M. Szerényi, A. Széchy, and T. Kocsis, “Greening due to environmental education? Environmental knowledge, attitudes, consumer behavior and everyday pro-environmental activities of Hungarian high school and university students,” J. Clean. Prod., vol. 48, pp. 126–138, 2013, doi: 10.1016/j.jclepro.2012.11.030.

[13] S. C. Gagnon Thompson and M. A. Barton, “Eco-centric and anthropocentric attitudes toward the environment,” J. Environ. Psychol., vol. 14, no. 2, pp. 149–157, 1994, doi: 10.1016/S0272-4944(05)80168-9.

[14] S. Otto and P. Pensini, “Nature-based environmental education of children: Environmental knowledge and connectedness to nature, together, are related to ecological behaviour,” Glob. Environ. Chang., vol. 47, pp. 88–94, 2017, doi: 10.1016/j.gloenvcha.2017.09.009.

[15] M. Y. Yusliza et al., “An investigation of pro-environmental behaviour and sustainable development in Malaysia,” Sustain., vol. 12, no. 17, p. 7083, 2020, doi: 10.3390/su12177083.

[16] S. de Freitas and T. Neumann, “The use of ‘exploratory learning’ for supporting immersive learning in virtual environments,” Comput. Educ., vol. 52, no. 2, pp. 343–352, 2009, doi: 10.1016/j.compedu.2008.09.010.

[17] C. P. Dai and F. Ke, “Educational applications of artificial intelligence in simulation-based learning: A systematic mapping review,” Comput. Educ. Artif. Intell., vol. 3, p. 100087, 2022, doi: 10.1016/j.caeai.2022.100087.

[18] L. Saini, P. Kumar, Rajeev, Tejas, and H. Upadhyay, “Virtually Going Green: The Role of Artificial Intelligence in Reducing Pollution and Toxicity,” in Artificial Intelligence Techniques for Sustainable Development, CRC Press, 2024, pp. 336–358.

[19] T. A. Mikropoulos and A. Natsis, “Educational virtual environments: A ten-year review of empirical research (1999-2009),” Comput. Educ., vol. 56, no. 3, pp. 769–780, 2011, doi: 10.1016/j.compedu.2010.10.020.

[20] S. Alexander, “Virtual Reality in Education: Defining Researchable Issues,” EMI. Educ. Media Int., vol. 31, no. 4, pp. 214–220, 1994, doi: 10.1080/0952398940310402.

[21] J. Su and W. Yang, “Artificial intelligence in early childhood education: A scoping review,” Comput. Educ. Artif. Intell., vol. 3, p. 100049, 2022, doi: 10.1016/j.caeai.2022.100049.

[22] W. Yang, “Artificial Intelligence education for young children: Why, what, and how in curriculum design and implementation,” Comput. Educ. Artif. Intell., vol. 3, p. 100061, 2022, doi: 10.1016/j.caeai.2022.100061.

[23] J. Su and Y. Zhong, “Artificial Intelligence (AI) in early childhood education: Curriculum design and future directions,” Comput. Educ. Artif. Intell., vol. 3, p. 100072, 2022, doi: 10.1016/j.caeai.2022.100072.

[24] J. Su, D. T. K. Ng, and S. K. W. Chu, “Artificial Intelligence (AI) Literacy in Early Childhood Education: The Challenges and Opportunities,” Comput. Educ. Artif. Intell., vol. 4, p. 100124, 2023, doi: 10.1016/j.caeai.2023.100124.

[25] A. Razak et al., “Reigniting the power of artificial intelligence in education sector for the educators and students competence,” in Artificial Intelligence and Machine Learning in Smart City Planning, Elsevier, 2023, pp. 103–116.

[26] C. W. Okonkwo and A. Ade-Ibijola, “Chatbots applications in education: A systematic review,” Comput. Educ. Artif. Intell., vol. 2, p. 100033, 2021, doi: 10.1016/j.caeai.2021.100033.

[27] M. Shoaib, N. Sayed, J. Singh, J. Shafi, S. Khan, and F. Ali, “AI student success predictor: Enhancing personalized learning in campus management systems,” Comput. Human Behav., vol. 158, p. 108301, 2024, doi: 10.1016/j.chb.2024.108301.

[28] T. Alqahtani et al., “The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research,” Res. Soc. Adm. Pharm., vol. 19, no. 8, pp. 1236–1242, 2023, doi: 10.1016/j.sapharm.2023.05.016.

[29] I. Lauriola, A. Lavelli, and F. Aiolli, “An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools,” Neurocomputing, vol. 470, pp. 443–456, 2022, doi: 10.1016/j.neucom.2021.05.103.

[30] D. Khurana, A. Koli, K. Khatter, and S. Singh, “Natural language processing: state of the art, current trends and challenges,” Multimed. Tools Appl., vol. 82, no. 3, pp. 3713–3744, 2023, doi: 10.1007/s11042-022-13428-4.

[31] C. Friedman, T. C. Rindflesch, and M. Corn, “Natural language processing: State of the art and prospects for significant progress, a workshop sponsored by the National Library of Medicine,” J. Biomed. Inform., vol. 46, no. 5, pp. 765–773, 2013, doi: 10.1016/j.jbi.2013.06.004.

[32] M. A. Hasan, N. F. M. Noor, S. S. B. A. Rahman, and M. M. Rahman, “The transition from intelligent to affective tutoring system: A review and open issues,” IEEE Access, vol. 8, pp. 204612–204638, 2020, doi: 10.1109/ACCESS.2020.3036990.

[33] M. Taub, R. Azevedo, R. Rajendran, E. B. Cloude, G. Biswas, and M. J. Price, “How are students’ emotions related to the accuracy of cognitive and metacognitive processes during learning with an intelligent tutoring system?,” Learn. Instr., vol. 72, p. 101200, 2021, doi: 10.1016/j.learninstruc.2019.04.001.

[34] S. Balakrishnan, S. P. Dakua, W. El Ansari, O. Aboumarzouk, and A. Al Ansari, “Novel applications of deep learning in surgical training,” in Artificial Intelligence, Big Data, Blockchain and 5G for the Digital Transformation of the Healthcare Industry: a Movement Toward more Resilient and Inclusive Societies, Elsevier, 2023, pp. 301–320.

[35] K. Zhang and A. B. Aslan, “AI technologies for education: Recent research & future directions,” Comput. Educ. Artif. Intell., vol. 2, p. 100025, 2021, doi: 10.1016/j.caeai.2021.100025.

[36] O. Ali, P. A. Murray, M. Momin, Y. K. Dwivedi, and T. Malik, “The effects of artificial intelligence applications in educational settings: Challenges and strategies,” Technol. Forecast. Soc. Change, vol. 199, p. 123076, 2024, doi: 10.1016/j.techfore.2023.123076.

[37] S. Rizvi, J. Waite, and S. Sentance, “Artificial Intelligence teaching and learning in K-12 from 2019 to 2022: A systematic literature review,” Comput. Educ. Artif. Intell., vol. 4, p. 100145, 2023, doi: 10.1016/j.caeai.2023.100145.

[38] X. Tan, G. Cheng, and M. H. Ling, “Artificial intelligence in teaching and teacher professional development: A systematic review,” Comput. Educ. Artif. Intell., vol. 8, p. 100355, 2025, doi: 10.1016/j.caeai.2024.100355.

[39] J. Mezirow, “Transformative learning: Theory to practice,” New Dir. adult Contin. Educ., vol. 1997, no. 74, pp. 5–12, 1997.

[40] S. Bisht, Ranjana, and S. Roy, “Optimizing role assignment for scaling innovations through AI in agricultural frameworks: An effective approach,” Adv. Agrochem, 2024, doi: 10.1016/j.aac.2024.07.004.

[41] A. Paulauskaite-Taraseviciene, I. Lagzdinyte-Budnike, L. Gaiziuniene, V. Sukacke, and L. Daniuseviciute-Brazaite, “Assessing Education for Sustainable Development in Engineering Study Programs: A Case of AI Ecosystem Creation,” Sustain., vol. 14, no. 3, p. 1702, 2022, doi: 10.3390/su14031702.

[42] K. Sahil, P. Mehta, S. Kumar Bhardwaj, and L. K. Dhaliwal, “Development of mitigation strategies for the climate change using artificial intelligence to attain sustainability,” in Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence, Elsevier, 2022, pp. 421–448.

[43] N. Kumari and S. Pandey, “Application of artificial intelligence in environmental sustainability and climate change,” in Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence, Elsevier, 2022, pp. 293–316.

[44] M. A. Habila, M. Ouladsmane, and Z. A. Alothman, “Role of artificial intelligence in environmental sustainability,” in Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence, Elsevier, 2022, pp. 449–469.

[45] W. Strielkowski, V. Grebennikova, A. Lisovskiy, G. Rakhimova, and T. Vasileva, “AI-driven adaptive learning for sustainable educational transformation,” Sustain. Dev., 2024, doi: 10.1002/sd.3221.

[46] S. Bosse and U. Engel, “Real-time human-in-the-loop simulation with mobile agents, chat bots, and crowd sensing for smart cities,” Sensors (Switzerland), vol. 19, no. 20, p. 4356, 2019, doi: 10.3390/s19204356.

[47] K. S. Suryanarayana, V. S. P. Kandi, G. Pavani, A. S. Rao, S. Rout, and T. S. R. Krishna, “Artificial Intelligence Enhanced Digital Learning for the Sustainability of Education Management System,” J. High Technol. Manag. Res., vol. 35, no. 2, p. 100495, 2024.

[48] M. Adnan et al., “Human inventions and its environmental challenges, especially artificial intelligence: New challenges require new thinking,” Environ. Challenges, vol. 16, p. 100976, 2024, doi: 10.1016/j.envc.2024.100976.

[49] F. Ouyang, T. A. Dinh, and W. Xu, “A Systematic Review of AI-Driven Educational Assessment in STEM Education,” J. STEM Educ. Res., vol. 6, no. 3, pp. 408–426, 2023, doi: 10.1007/s41979-023-00112-x.

[50] Y. Yang, W. Sun, D. Sun, and S. Z. Salas-Pilco, “Navigating the AI-Enhanced STEM education landscape: a decade of insights, trends, and opportunities,” Res. Sci. Technol. Educ., pp. 1–25, 2024, doi: 10.1080/02635143.2024.2370764.

[51] M. Akdeniz and F. Özdinç, “Maya: An artificial intelligence based smart toy for pre-school children,” Int. J. Child-Computer Interact., vol. 29, p. 100347, 2021, doi: 10.1016/j.ijcci.2021.100347.

[52] J. W. Creswell, Eduational Research: Planning, and evaluating quantitative research, vol. 7. Upper Saddle River, New Jersey: Prentice Hall, 2008.

[53] D. Glisczinski, “Thematic Analysis,” J. Transform. Educ., vol. 16, no. 3, p. 175, 2018, doi: 10.1177/1541344618777367.

[54] A. Castleberry and A. Nolen, “Thematic analysis of qualitative research data: Is it as easy as it sounds?,” Curr. Pharm. Teach. Learn., vol. 10, no. 6, pp. 807–815, 2018, doi: 10.1016/j.cptl.2018.03.019.

[55] J. Roschelle, Learning in interactive environments: Prior knowledge and new experience. Citeseer, 1997.

[56] L. Markauskaite et al., “Rethinking the entwinement between artificial intelligence and human learning: What capabilities do learners need for a world with AI?,” Comput. Educ. Artif. Intell., vol. 3, p. 100056, 2022, doi: 10.1016/j.caeai.2022.100056.

[57] M. Fullan and J. Quinn, The Drivers: Transforming Learning for Students, Schools, and Systems. Corwin Press, 2023.

[58] E. F. Moran, People and nature: An introduction to human ecological relations. John Wiley & Sons, 2016.

[59] A. Zabaniotou, “A systemic approach to resilience and ecological sustainability during the COVID-19 pandemic: Human, societal, and ecological health as a system-wide emergent property in the Anthropocene,” Glob. Transitions, vol. 2, pp. 116–126, 2020, doi: 10.1016/j.glt.2020.06.002.

[60] G. Gigerenzer and P. M. Todd, Ecological Rationality: Intelligence in the World. OUP USA, 1999.

[61] J. Blake, S. Sterling, and I. Goodson, “Transformative learning for a sustainable future: An exploration of pedagogies for change at an alternative college,” Sustain., vol. 5, no. 12, pp. 5347–5372, 2013, doi: 10.3390/su5125347.

[62] H. Lotz-Sisitka, A. E. J. Wals, D. Kronlid, and D. McGarry, “Transformative, transgressive social learning: Rethinking higher education pedagogy in times of systemic global dysfunction,” Curr. Opin. Environ. Sustain., vol. 16, pp. 73–80, 2015, doi: 10.1016/j.cosust.2015.07.018.

[63] R. Robina-Ramírez and J. A. Medina-Merodio, “Transforming students’ environmental attitudes in schools through external communities,” J. Clean. Prod., vol. 232, pp. 629–638, 2019, doi: 10.1016/j.jclepro.2019.05.391.

[64] C. Mele, T. Russo Spena, V. Kaartemo, and M. L. Marzullo, “Smart nudging: How cognitive technologies enable choice architectures for value co-creation,” J. Bus. Res., vol. 129, pp. 949–960, 2021, doi: 10.1016/j.jbusres.2020.09.004.

[65] J. Kay, “Foundations for Human-AI teaming for self-regulated learning with explainable AI (XAI),” Comput. Human Behav., vol. 147, p. 107848, 2023, doi: 10.1016/j.chb.2023.107848.

[66] S. A. Chauncey and H. P. McKenna, “A framework and exemplars for ethical and responsible use of AI Chatbot technology to support teaching and learning,” Comput. Educ. Artif. Intell., vol. 5, p. 100182, 2023, doi: 10.1016/j.caeai.2023.100182.

[67] S. Aslan et al., “What is the impact of a multi-modal pedagogical conversational AI system on parents’ concerns about technology use by young children?,” Br. J. Educ. Technol., vol. 55, no. 4, pp. 1625–1650, 2024, doi: 10.1111/bjet.13399.

[68] A. M. Bettayeb, M. Abu Talib, A. Z. Sobhe Altayasinah, and F. Dakalbab, “Exploring the impact of ChatGPT: conversational AI in education,” in Frontiers in Education, 2024, vol. 9, p. 1379796, doi: 10.3389/feduc.2024.1379796.

[69] S. Aslan et al., “Immersive multi-modal pedagogical conversational artificial intelligence for early childhood education: An exploratory case study in the wild,” Comput. Educ. Artif. Intell., vol. 6, p. 100220, 2024, doi: 10.1016/j.caeai.2024.100220.

[70] M. D. Lytras, A. C. Serban, A. Alkhaldi, T. Aldosemani, and S. Malik, “Transformative Learning for Future Higher Education: The Artificial Intelligence-enabled Learning Revolution 2035,” in Digital Transformation in Higher Education, Part B, Emerald Publishing Limited, 2024, pp. 231–241.

[71] J. Brunstein and J. King, “Organizing reflection to address collective dilemmas: Engaging students and professors with sustainable development in higher education,” J. Clean. Prod., vol. 203, pp. 153–163, 2018, doi: 10.1016/j.jclepro.2018.08.136.

[72] J. Moore, “Is Higher Education Ready for Transformative Learning?: A Question Explored in the Study of Sustainability,” J. Transform. Educ., vol. 3, no. 1, pp. 76–91, 2005, doi: 10.1177/1541344604270862.

[73] I. Thomas, “Critical thinking, transformative learning, sustainable education, and problem-based learning in universities,” J. Transform. Educ., vol. 7, no. 3, pp. 245–264, 2009, doi: 10.1177/1541344610385753.

[74] M. S. Reed, E. D. G. Fraser, and A. J. Dougill, “An adaptive learning process for developing and applying sustainability indicators with local communities,” Ecol. Econ., vol. 59, no. 4, pp. 406–418, 2006, doi: 10.1016/j.ecolecon.2005.11.008.

[75] D. Mhlanga, “Open AI in Education, the Responsible and Ethical Use of ChatGPT Towards Lifelong Learning,” in SSRN Electronic Journal, Springer, 2023, pp. 387–409.

[76] A. Haleem, M. Javaid, M. A. Qadri, and R. Suman, “Understanding the role of digital technologies in education: A review,” Sustain. Oper. Comput., vol. 3, pp. 275–285, 2022, doi: 10.1016/j.susoc.2022.05.004.