Chapter 21: AI-Enhanced Experiential Learning for Nature-related Financial Disclosures

Given the growing interest in artificial intelligence (AI) across various domains, AI-based education is becoming increasingly important. This study presents AI-based educational solutions aimed at enhancing the understanding of the Taskforce on Nature-related Financial Disclosures (TNFD) framework, which addresses physical, transition, and systemic risks that increasingly impact business environments. The proposed methods provide students with hands-on, experiential learning opportunities through three projects: flood risk assessment using satellite imagery and Random Forest classification, biodiversity risk monitoring using neural networks with images from camera traps, and systemic risk analysis via public sentiment after natural disasters using natural language processing (NLP) techniques like topic modeling. By using these AI tools, the study aligns with experiential learning theories to promote active learning in business education, even for students with limited programming skills. These approaches are designed not only to increase AI literacy but also to equip future business leaders with the ability to understand and address the challenges posed by nature-related risks. The contribution of this work lies in bridging the gap between theory and practice, enhancing practical AI competency that will be crucial for managing nature-related financial risks in an evolving regulatory landscape.