Designing a flipped AI-chatbot learning module to support students’ environmental literacy development: A Fuzzy Delphi Method
Xiaoyu Wang, Xiang Li
TL;DR
This study creates a flipped AI chatbot learning module to improve students' environmental literacy through personalized and interactive learning.
Contribution
The novel contribution is the FACL module, which integrates AI chatbots and flipped learning to enhance environmental education.
Findings
The FACL module promotes personalized and student-centered learning through AI chatbot interactions.
The module addresses traditional teaching challenges like low motivation and inadequate preparation.
It offers a scalable framework for environmental science and educational technology.
Abstract
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…
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Taxonomy
TopicsAI in Service Interactions · Educational Games and Gamification · Delphi Technique in Research