The rapid penetration of renewable energy sources such as solar photovoltaic (PV) and wind power into modern power grids introduces significant operational challenges, including intermittency, voltage instability, harmonic distortion, and fault vulnerability. Conventional control strategies are often insufficient for handling dynamic grid disturbances and nonlinear system behavior. This study proposes an Artificial Intelligence (AI)-enhanced control framework for grid-connected renewable energy systems to enable adaptive control, predictive fault detection, and resilient operation. The proposed architecture integrates machine learning-based fault classification, adaptive inverter control, and real-time grid condition monitoring. A hybrid dataset composed of simulated grid disturbances and real operational parameters is used to train and validate the AI model. Results demonstrate improved fault detection accuracy, reduced system recovery time, enhanced voltage stability, and improved power quality under dynamic grid conditions. The proposed AI-driven framework enhances grid reliability, supports high renewable penetration, and contributes to resilient and sustainable energy infrastructure.

This research investigates the impact of an AI adaptive language assessment system, when combined with special education principles, on neurodiverse students in TESOL contexts. Although adaptive systems have been extensively debated in language learning, there has been remarkably little attention paid to students with autism spectrum disorder, dyslexia, or ADHD. To fill this research void, the study employed a sequential explanatory mixed-methods approach. In the quantitative component, 120 students were included in a 12-week quasi-experimental design comparing the impact of AI adaptive assessment with traditional testing modes. The data included standardized English proficiency test scores, test anxiety, engagement, and psychometric statistics using Item Response Theory and differential item functioning. The results demonstrated greater proficiency achievement, reduced anxiety, and increased engagement among students using the adaptive system. Reliability coefficients were high, and subgroup analysis revealed little measurement bias. In the qualitative component, teacher interviews shed light on usability and integration in the classroom. In general, the results of this study indicate that by combining adaptive assessment with organized special education principles, students with diverse cognitive abilities can be treated equitably and meaningfully in language assessment, while also offering a roadmap for future research on transparency and long-term implementation.