Leveraging Artificial Intelligence - Driven Automata for Improved Dropout Prediction in Schools

Abstract

This work investigates the use of Artificial Intelligence - driven automata to predict student dropout risks, emphasizing how artificial intelligence can enhance educational sustainability. By integrating automata theory with cutting-edge machine learning techniques, the study develops a predictive framework to identify students at high risk of dropping out. The research includes a thorough review of automata theory, Artificial Intelligence applications in education, and current dropout prevention strategies. Using automata-based algorithms, we analyze educational datasets to model student behavior and risk factors. Preliminary findings indicate that these models can accurately forecast dropout probabilities, facilitating timely interventions to boost student retention and promote a more equitable educational system. Additionally, this work aligns with broader sustainability goals by supporting improved educational outcomes, which are crucial for advancing green economies amidst climate change. The results highlight the potential of automata-based AI in dropout prevention and offer insights for future research on integrating AI solutions in educational settings to address climate-related challenges.