Data-Driven Decision-Making in Education: Leveraging AI for School Improvement

In the ever-evolving landscape of education, the integration of Artificial Intelligence (AI) stands as a transformative force, poised to revolutionize traditional approaches to decision-making. This paper addresses the imperative of harnessing AI to inform data-driven strategies for school improvement. Our comprehensive framework navigates the multifaceted realm of educational data, offering stakeholders a sophisticated toolset for insightful decision-making. By leveraging AI algorithms, we empower educators and administrators to decipher intricate patterns within student performance data, assess teaching methodologies, and optimize resource allocation. This model not only responds to the growing importance of data in education but also aligns with the broader movement towards smart, adaptive educational systems. Through an extensive literature survey, we contextualize our work within the existing body of knowledge, emphasizing the transformative potential of AI in educational settings. Our proposed model intricately intertwines data collection, preprocessing, analysis, and visualization, employing machine learning techniques to predict student outcomes and refine teaching strategies. Ethical considerations underscore our commitment to responsible AI usage, ensuring data privacy and safeguarding against biases. The implementation model details the practical deployment of our framework, emphasizing scalability and real-world applicability. The results section showcases tangible improvements in student outcomes and institutional efficiency through the application of our model to diverse educational datasets. In conclusion, our research underscores the capacity of AI-driven decision-making to catalyze substantial advancements in education, marking a pivotal moment in the ongoing journey towards data-driven educational excellence.