Example of a Scale of Linguistic Variables.

China’s rapid economic growth has exacerbated environmental degradation, posing severe risks to public health and sustainable development. However, current environmental education in higher education remains predominantly teacher-centered, resulting in low engagement and inadequate development of Environmental Literacy (EL). Correspondingly, this study addresses these challenges by designing a Flipped AI-Chatbot Learning (FACL) module that is designed to support the development of students’ EL through an innovative integration of generative Artificial Intelligence (AI) and Flipped Learning (FL). Using the Fuzzy Delphi Method (FDM), this research gathered the consensus of 12 experts to develop a comprehensive instructional framework grounded in educational theories. In particular, the FACL module combines pre-class AI chatbot interactions with in-class active learning strategies to promote personalized, student-centered learning. Accordingly, the results identify key instructional objectives, strategies, and evaluation mechanisms, specifying module elements intended to address traditional teaching challenges. This includes low motivation, limited interaction, and inadequate pre-class preparation. Concurrently, this study contributes to environmental science, educational technology, and information literacy by providing a scalable and interdisciplinary framework that aligns theoretical innovation with practical application. Overall, the FACL module advances environmental education while potentially cultivating critical thinking and pro-environmental behaviors, with the intention of preparing students to address complex sustainability challenges in real-world contexts.