Automated Reading Fluency Assessment: A Multilayer Educational Framework for Scalable Literacy Evaluation

Reading fluency is a key component of literacy development and an important indicator of reading comprehension and academic achievement. However, traditional fluency assessment methods are often time-consuming, difficult to scale, and dependent on subjective evaluation procedures, limiting their use in large-scale educational contexts. This study proposes a multilayer educational framework for automated reading fluency assessment that integrates audio processing, fluency indicator computation, and interpretive modeling within a structured assessment system.The proposed framework conceptualizes fluency assessment as a multilayer process that transforms oral reading audio into interpretable educational outputs through successive stages of acoustic processing, indicator computation, interpretive modeling, and feedback generation. This architecture allows fluency to be evaluated as a multidimensional construct, including reading rate, temporal continuity, pausing patterns, and rhythmic features, rather than relying on single metrics such as words per minute.The system is designed to support classroom assessment, large-scale literacy evaluation, and formative feedback processes by providing scalable, consistent, and interpretable fluency assessments. The study outlines the conceptual foundations of the framework, describes its system architecture, and discusses its potential applications in educational assessment and literacy development monitoring.The findings suggest that multilayer automated assessment systems can contribute to more efficient, scalable, and pedagogically meaningful approaches to reading fluency assessment, supporting data-informed decision-making in educational contexts.