"Artificial Intelligence in Education: A Review"

"This simulator models the dynamics of Artificial Intelligence adoption in education across three core domains \u2014 administration, instruction, and learning \u2014 based on the findings of a comprehensive review paper by Chen et al. (2020). It reproduces the observed growth trend of AI-in-education research publications from 2010 to 2019 (as shown in Figure 1 of the paper) using logistic growth and Bass technology diffusion models, and projects forward to 2030 under five policy scenarios ranging from slow institutional adoption to accelerated, domain-focused AI integration. The simulator generates synthetic time-series data for AI adoption rates, student learning outcomes, instructor efficiency scores, and curriculum personalization indices, allowing researchers and educators to compare scenarios and visualize S-curve adoption dynamics. Researchers can use it to benchmark adoption policy impacts, educators can explore how domain-specific AI investments translate to learning improvements, and engineers can validate diffusion model parameters against real publication data."