Data-Driven Insights: Using AI to Analyze Student Performance in English Language Courses

This study uses SPSS for quantitative analysis to examine the viability of using artificial intelligence (AI) in assessing the growth and performance of ESL students. Results may assist instructors in enhancing their methodologies by clarifying essential elements of student success. The online survey primarily focused on the demographics, study habits, resource availability, motivation, and self-reported performance of English language course students. Descriptive and inferential statistics, augmented by artificial intelligence algorithms for pattern and correlation identification, were used to analyze the survey data. Key factors influencing student accomplishment, as shown by studies, including motivation and access to learning resources. AI-driven analyses have shown complex interconnections across factors, including the impact of demographic disparities on performance and the correlation between intrinsic motivation and resource availability. The study revealed that using AI insights to develop personalized courses significantly enhanced student performance. Educators may develop more effective customized treatments for children by using AI and SPSS for data analysis. This study establishes a foundation for further research to expand upon these findings and explore other dimensions of AI in education by illustrating AI's potential to transform teaching methodologies and enhance student outcomes in English language courses.