AI-Based Modeling and Simulation for Enhancing Systems Thinking in High School Ecosystem Education: An Empirical Study

Systems thinking is a higher-order cognitive ability essential for analyzing ecosystem structures and understanding organism–environment interactions. Traditional biology instruction often limits students’ perception of dynamic ecosystem processes and provides insufficient support for developing systems thinking. To address these challenges, this study constructed an interdisciplinary instructional model integrating AI-supported modeling and simulation with biological knowledge, data science, and computational thinking. A quasi-experimental design was implemented with 168 Grade 11 students from four parallel classes in two public senior high schools, with an experimental group receiving AI-based modeling and simulation instruction and a control group receiving traditional lecture-based teaching supplemented by basic simulation software. Data were collected through a self-developed systems thinking scale, an ecosystem concept test, student artifacts, and semi-structured interviews. Results indicated that students in the experimental group significantly outperformed those in the control group in overall systems thinking and across four dimensions—element identification, structural analysis, dynamic prediction, and feedback regulation. Moreover, the experimental group demonstrated deeper conceptual understanding, stronger interdisciplinary learning interest, and improved problem-solving abilities. These findings suggest that AI-supported modeling and simulation provides an effective approach for cultivating systems thinking in high school biology education and offers empirical evidence supporting the integration of intelligent technologies into science teaching.