Toward an AI-driven Islamic eco-campus model: bridging digital transformation and sustainability in Higher Education
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Abstract
In today's digital era, the use of Artificial Intelligence (AI) has become a tool to assist human work, including in achieving sustainability performance in higher education. This study aims to describe the implementation, strategies, supporting factors, and inhibiting factors of AI use in supporting sustainability performance in higher education. This qualitative study took the case of a State Islamic University (UIN) A, which was ranked among the top 10 in the UI GreenMetric awards. Data were obtained through interviews, observations, and documentation. Semi-structured interviews were conducted with 10 informants, both online and offline, in mid-2025. Observations were obtained during campus visits. Documentation was obtained from the campus website, UI GreenMetric, and documents obtained from the green campus management. The research results show that UIN A has used AI to support its sustainability performance, especially in energy, transportation, infrastructure, waste management, and education. Commitment from the leadership, solid team support, and financial support are important supporting factors in the success of sustainability performance on campus. Although there are still obstacles such as limited human resource competency in AI, data security, and limited integration of AI and existing systems on campus. This research provides a theoretical contribution in the development of the triple bottom line theory which is integrated with Islamic values, thus becoming the quadrant bottom line theory. The results of this study can also provide empirical evidence of campus support for sustainability issues. However, there are still many improvements and enhancements that need to be made by green campus management to provide maximum support for the Sustainability Development Goals (SDGs).
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References
Ahmad, T., Zhu, H., Zhang, D., Tariq, R., Bassam, A., Ullah, F., AlGhamdi, A. S., & Alshamrani, S. S. (2022). Energetics Systems and artificial intelligence: Applications of industry 4.0. Energy Reports, 8, 334–361. https://doi.org/10.1016/j.egyr.2021.11.256
Batki, R. A., & Phudinawala, H. (2024). A Comparative Study on How AI is Partnering with Nature for a Sustainable Future. International Journal For Multidisciplinary Research, 6(3), 1–9. https://doi.org/10.36948/ijfmr.2024.v06i03.19193
Bukhari, S. A. A., Hashim, F., Amran, A. Bin, & Hyder, K. (2020). Green banking and Islam: Two sides of the same coin. Journal of Islamic Marketing, 11(4), 977–1000. https://doi.org/10.1108/JIMA-09-2018-0154
Cath, C. (2018). Governing artificial intelligence: Ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133). https://doi.org/10.1098/rsta.2018.0080
de Cámara, E. S., Fernández, I., & Castillo-Eguskitza, N. (2021). A holistic approach to integrate and evaluate sustainable development in higher education. The case study of the university of the Basque Country. Sustainability (Switzerland), 13(1), 1–19. https://doi.org/10.3390/su13010392
Eslava-Zapata, R., Sánchez-Castillo, V., & Juaneda-Ayensa, E. (2024). Key players in renewable energy and artificial intelligence research. EAI Endorsed Transactions on Energy Web, 11, 1–10. https://doi.org/10.4108/ew.5182
Feng, Z., Ge, M., & Meng, Q. (2024). Enhancing Energy Efficiency in Green Buildings through Artificial Intelligence 2 . Current Situation of Energy Management in Green Building. 4(8), 21–30.
Francisco, M., & Linnér, B. O. (2023). AI and the governance of sustainable development. An idea analysis of the European Union, the United Nations, and the World Economic Forum. Environmental Science and Policy, 150(September). https://doi.org/10.1016/j.envsci.2023.103590
Hammer, J., & Pivo, G. (2016). The Triple Bottom Line and Sustainable Economic Development Theory and Practice. https://doi.org/10.1177/0891242416674808
Katsamakas, E., Pavlov, O. V., & Saklad, R. (2019). Artificial Intelligence and the Transformation of Higher Education Institutions: A System Approach. Sustainability (Switzerland), 11(1), 1–14. http://scioteca.caf.com/bitstream/handle/123456789/1091/RED2017-Eng-8ene.pdf?sequence=12&isAllowed=y%0Ahttp://dx.doi.org/10.1016/j.regsciurbeco.2008.06.005%0Ahttps://www.researchgate.net/publication/305320484_SISTEM_PEMBETUNGAN_TERPUSAT_STRATEGI_MELESTARI
Leechman, G., McCulla, N., & Field, L. (2019). Local school governance and school leadership: practices, processes and pillars. International Journal of Educational Management, 33(7), 1641–1652. https://doi.org/10.1108/IJEM-12-2018-0401
Loc, H. H., Irvine, K., Suwanarit, A., & Vallikul, P. (2020). Sustainability and Law. In Sustainability and Law (Issue July). https://doi.org/10.1007/978-3-030-42630-9
Mauro, G. (2024). The New Power Couple: Artificial Intelligence and Renewable Energy. Journal of Strategic Innovation and Sustainability, 19(3), 98–115. https://doi.org/10.33423/jsis.v19i3.7374
Miles, M. B., Huberman, A. M., & Saldana, J. (2014). Qualitative Data Analysis: A Methods Sourcebook. Nursing Standard (Royal College of Nursing (Great Britain) : 1987), 3. https://doi.org/10.7748/ns.30.25.33.s40
Onwusinkwue, S., Osasona, F., Ahmad, I., Ahmad, I., Anyanwu, A. C., Dawodu, S. O., Obi, O. C., & Hamdan, A. (2024). Artificial intelligence ( AI ) in renewable energy : A review of predictive maintenance and energy optimization.
Soodan, V., Rana, A., Jain, A., & Sharma, D. (2024). AI CHATBOT ADOPTION IN ACADEMIA: TASK FIT, USEFULNESS, AND COLLEGIAL TIES. Journal of Information Technology Education: Innovations in Practice, 23. https://doi.org/10.28945/5260
Stone, A. (2023). Student Perceptions of Academic Integrity: A Qualitative Study of Understanding, Consequences, and Impact. Journal of Academic Ethics, 21(3), 357–375. https://doi.org/10.1007/s10805-022-09461-5
Strzelecki, A., Cicha, K., Rizun, M., & Rutecka, P. (2024). Acceptance and use of ChatGPT in the academic community. Education and Information Technologies. https://doi.org/10.1007/s10639-024-12765-1
Wang, C. (2024). Art Innovation or Plagiarism? Chinese Students’ Attitudes Toward AI Painting Technology and Influencing Factors. IEEE Access, 12, 85795–85805. https://doi.org/10.1109/ACCESS.2024.3412176
Wen, X., Shen, Q., Wang, S., & Zhang, H. (2024). Leveraging AI and Machine Learning Models for Enhanced Efficiency in Renewable Energy Systems. Applied and Computational Engineering, 96(1), 107–112. https://doi.org/10.54254/2755-2721/96/20241416