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The Human Element in AI-Driven Environmental Education

Teacher Adaptability and Personalization in Promoting Eco-Friendly Behaviors

Bibliographic Data

ID22006275
AuthorsJiaxing Wang (0000-0002-5496-9330, Sichuan University), Shijie Zhai (0009-0007-0527-6806, Japan Real Estate Institute, corresponding author), Yixuan Wang (0000-0002-5084-2455, Japan Real Estate Institute), Hai-yan Hua (Chengdu University of Technology), Yuyan Luo (0000-0003-0810-9679, Chengdu University of Technology)
Year2025
Volume15
Issue4
Publication date2025-10-01
Open AccessYes
TypeARTICLE
VenueSAGE Open (JOURNAL)
Journal identifiersISSN: 2158-2440 • E-ISSN: 2158-2440
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/21582440251403646
OpenAlexW7113897752
LanguageEN
References cited58

The ecological crisis deeply impacts the globe. Despite increasing interest in environmental education (EE), there is limited research on how AI technologies influence students’ adoption of eco-friendly behaviors, highlighting a critical research gap. This research aims to explore how psychological and educational factors including adoption of AI in teaching contribute to eco-friendly behaviors. Utilizing the Model of Goal-Directed Behavior alongside theories of class adaptability, this study introduces the AI-Driven Green Behavior Model (AIGB) to elucidate how these elements interact. The study analyzes data from comments on China’s Mooc online education platform and enhances its findings with insights from interviews. Employing Big Data, Structural Equation Modeling, and grounded theory in its analysis, the research underscores the vital role of adaptability and personalization in environmental education. It further underscores the irreplaceable role of human educators in the adoption of AI in teaching

Adaptability · Environmental education · Grounded theory · Personalization · Structural equation modeling · E-Learning and COVID-19 · Environmental Education and Sustainability · Sustainability in Higher Education

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