Integrating Artificial Intelligence into Environmental Education: A Rule-Based Expert System for Hornbill Conservation Awareness

Hornbills are unique birds inhabiting Southeast Asian tropical rainforests, such as in Malaysia, where they have an important role in maintaining forest biodiversity while acting as forest regulators and seed dispersers. Their numbers have, however, plummeted because of deforestation, habitat destruction, illegal poaching and hunting, among others. Field identification and field observation of species by experts is time-consuming and restricted because they cannot quantitatively estimate hornbill populations. The growing domain of artificial intelligence (AI) technologies offers new possibilities for more advanced wildlife tracking and conservation awareness through automation and smart data management. The article explains the design and implementation of a rule-based expert system with AI incorporation in environmental education on hornbill species and their conservation status. A rule-based system integrated with image classification is utilized to categorize ten widely spread species of hornbills in Malaysia and classify them under the respective International Union for Conservation of Nature (IUCN) Red List categories. A machine learning framework trained using hornbill images was incorporated into an easily accessible interface to ensure highest usability and accuracy. The evaluation confirmed that the system generated consistent recognition results with confidence levels above 90% for all species. Apart from its technical contribution, the system serves as an interactive learning platform that bridges artificial intelligence with biodiversity conservation. The system enables users to observe the ways AI models recognize species while being exposed to ecological and conservation information. This integration of technology and environmental education engenders critical thinking, interdisciplinarity, and Malaysian wildlife awareness by the public. The study confirms that AI-driven expert systems are effective means of conservation learning and awareness for maintaining national and international sustainability goals through technology-facilitated learning.

Expert system; Artificial intelligence

1. Abdullah, S. A., & Hezri, A. A. (2008). From forest landscape to agricultural landscape in the developing tropical country of Malaysia: pattern, process, and their significance on policy. Environmental Management, 42(5), 907-917. [Google Scholar] [Crossref]

2. Aik, Y. C., & Perumal, B. (2017). Distribution of Hornbills and important Hornbill landscapes setting site conservation priorities for Peninsular Malaysia. In 7th International Hornbill Conference. [Google Scholar] [Crossref]

3. Alamgir, M., Campbell, M. J., Sloan, S., Engert, J., Word, J., & Laurance, W. F. (2020). Emerging challenges for sustainable development and forest conservation in Sarawak, Borneo. PloS one, 15(3), e0229614. [Google Scholar] [Crossref]

4. Arif, M., Ismail, A., & Irfan, S. (2025). AI-powered approaches for sustainable environmental education in the digital age: A study of Chongqing International Kindergarten. International Journal of Environment, Engineering and Education, 7(1), 35-47. [Google Scholar] [Crossref]

5. Beastall, C., Shepherd, C. R., Hadiprakarsa, Y., & Martyr, D. (2016). Trade in the Helmeted Hornbill Rhinoplax vigil: the ‘ivory hornbill’. Bird Conservation International, 26(2), 137-146. [Google Scholar] [Crossref]

6. BirdLife International. 2022. State of the World’s Birds: 2022 Annual Update. Retrieved from https://www.birdlife.org/papers-reports/state-of-the-worlds-birds-2022/ [Google Scholar] [Crossref]

7. Collar, N. J. (2015). Helmeted Hornbills Rhinoplax vigil and the ivory trade: the crisis that came out of nowhere. BirdingASIA, 24, 12-17. [Google Scholar] [Crossref]

8. Das, S., Anowar, S., & Chakraborty, S. (2024). The integration of AI technology into environmental education. Life as basic science: An overview and prospects for the future, 1, 223-247. [Google Scholar] [Crossref]

9. Gaveau, D. L., Sloan, S., Molidena, E., Yaen, H., Sheil, D., Abram, N. K., ... & Meijaard, E. (2014). Four decades of forest persistence, clearance and logging on Borneo. PloS one, 9(7), e101654. [Google Scholar] [Crossref]

10. Jaafar, W. S. W. M., Said, N. F. S., Abdul Maulud, K. N., Uning, R., Latif, M. T., Muhmad Kamarulzaman, A. M., ... & Takriff, M. S. (2020). Carbon emissions from oil palm induced forest and peatland conversion in sabah and Sarawak, Malaysia. Forests, 11(12), 1285. [Google Scholar] [Crossref]

11. Kaur, R., Ramli, R., Ancrenaz, M., Hassan, H. E. L. S. O. N., Ahmad, E., Ratag, M., ... & Rajak, A. (2020). Estimating the availability of potential hornbill nest trees in a secondary forest patch in Kinabatangan, Sabah. Forktail, 36(1), 56-62. [Google Scholar] [Crossref]

12. Konya, A., & Nematzadeh, P. (2024). Recent applications of AI to environmental disciplines: A review. Science of The Total Environment, 906, 167705. [Google Scholar] [Crossref]

13. Lowan-Trudeau, G. (2023). Digital technologies and environmental education. The Journal of Environmental Education, 54(1), 1-7. [Google Scholar] [Crossref]

14. Misni, A., Rasam, A. R. A., & Buyadi, S. N. A. (2017). Spatial analysis of habitat conservation for hornbills: a case study of Royal Belum-Temengor forest complex in Perak State Park, Malaysia. Pertanika Journal of Social Science and Humanities, 25, 11-20. [Google Scholar] [Crossref]

15. Naniwadekar, R., Rathore, A., Shukla, U., Chaplod, S., & Datta, A. (2019). How far do Asian forest hornbills disperse seeds?. Acta Oecologica, 101, 103482. [Google Scholar] [Crossref]

16. Norouzzadeh, M. S., Nguyen, A., Kosmala, M., Swanson, A., Palmer, M. S., Packer, C., & Clune, J. (2018). Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proceedings of the National Academy of Sciences, 115(25), E5716-E5725. [Google Scholar] [Crossref]

17. Rrustemaj, M. (2025). Artificial Intelligence (AI) in Education. International Journal of Research and Innovation in Social Science, 9(5), 867-876. [Google Scholar] [Crossref]

18. Sachyani, D., & Gal, A. (2025). Artificial Intelligence Tools in Environmental Education: Facilitating Creative Learning about Complex Interaction in Nature. European Journal of Educational Research, 14(2). [Google Scholar] [Crossref]

19. The IUCN Red List of Threatened Species (no date) IUCN Red List of Threatened Species. Available at: https://www.iucnredlist.org/ (Accessed: 2 October 2025). [Google Scholar] [Crossref]

20. Ifenthaler, D., Majumdar, R., Gorissen, P., Judge, M., M., Mishra, S., Raffaghelli, J., & Shimada, A. (2024). Artificial intelligence in education: Implications for policymakers, researchers, and practitioners. Technology, Knowledge and Learning, 29(4), 1693-1710. [Google Scholar] [Crossref]

21. Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, 124167. [Google Scholar] [Crossref]

22. Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review. Smart Learning Environments, 11(1), 28. [Google Scholar] [Crossref]

• The Role of Artificial Intelligence in Revolutionizing Library Services in Nairobi: Ethical Implications and Future Trends in User Interaction
• ESPYREAL: A Mobile Based Multi-Currency Identifier for Visually Impaired Individuals Using Convolutional Neural Network
• Comparative Analysis of AI-Driven IoT-Based Smart Agriculture Platforms with Blockchain-Enabled Marketplaces
• AI-Based Dish Recommender System for Reducing Fruit Waste through Spoilage Detection and Ripeness Assessment
• SEA-TALK: An AI-Powered Voice Translator and Southeast Asian Dialects Recognition