INTEGRATING AI-BASED SPEECH RECOGNITION TECHNOLOGY TO ENHANCE READING ASSESSMENTS WITHIN MOROCCO’S TaRL PROGRAM

Abstract

This study examined the integration of artificial intelligence-powered speech recognition technology within early reading assessments in Morocco’s Teaching at the Right Level (TaRL) program. The purpose was to evaluate the effectiveness of an automated speech recognition tool compared to traditional paper- based assessments in improving reading skills among 100 Moroccan first to third-graders. The mixed- method approach combined pre-post standardized reading tests with qualitative feedback. Results showed students receiving the AI-enabled speech recognition assessments demonstrated significant gains in reading achievement compared to peers assessed via traditional methods. Qualitative findings revealed benefits of instant feedback and enhanced engagement provided by the speech recognition tool. This study contributes timely empirical evidence on adopting learning technologies, specifically AI-driven automated speech assessment instruments, to enhance foundational literacy development within under-resourced education systems implementing student-centered pedagogical techniques like TaRL. It provides valuable insights and guidance for integrating innovative speech analysis tools within localized teaching and learning frameworks to strengthen early reading instruction and monitoring.