Teacher Acceptance of AI-Generated Educational Simulations: A Fuzzy DEMATEL Causal Analysis
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Generative artificial intelligence has transformed education, enabling teachers to create personalized educational materials and interactive content that supports active student learning. A relatively new capability is the creation of interactive simulations with the assistance of large language models. The benefits of dynamic simulations in improving learning outcomes have been highlighted through numerous studies. However, the development of such simulations was a time-consuming and costly undertaking, requiring specialized programming knowledge and technical skills. With the advent of large language models, the creation of such simulations has been democratized for both teachers and learners. Teachers can now create interactive simulations tailored to students’ needs through appropriate prompts, with reduced implementation time and cost. In this paper, the Pedagogy First–Structured Prompt–Iterative Refinement–Validation methodology is presented, which constitutes the basis for developing such interactive simulations, accompanied by practical examples drawn from Physics and Mathematics. To investigate the factors influencing the acceptance of AI-generated simulations by teachers, a Fuzzy DEMATEL analysis was applied with six experts, evaluating six criteria based on the Technology Acceptance Model. The Fuzzy DEMATEL analysis showed that prompt engineering skills and development time are the main causal factors, while perceived usefulness is the central outcome. These findings have implications for teacher professional development and the integration of AI-generated simulations into educational practice, underscoring prompt engineering training as a prerequisite for effective technology adoption.
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