The global biodiversity crisis necessitates innovative educational strategies to foster environmental stewardship and scientific literacy. This article outlines a series of AI-integrated, inquiry-based learning activities designed to teach plant conservation by leveraging computational thinking (CT) and advanced tools such as Python, geospatial mapping libraries, and machine learning frameworks. Rooted in constructionism, these activities enable students to actively engage in coding, data analysis, and ecological modeling, making abstract scientific concepts tangible and relevant. From identifying local plant species using AI tools to predicting habitat health and clustering ecological risk zones, students gain hands-on experience with real-world conservation challenges. Designed to align with the Next Generation Science Standards (NGSS), these activities equip students with transferable skills in data literacy, critical thinking, and problem-solving, inspiring them to explore STEM careers and tackle global environmental challenges.
Kitcharoenpanya et al. (2026) studied this question.