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

Year 2024, Volume: 25 Issue: 4, 1 - 15, 01.10.2024

Younes Aziz Bachiri , Hicham Mouncif Belaid Bouikhalene Radoine Hamzaoui

https://doi.org/10.17718/tojde.1335062

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.

Keywords

Artificial Intelligence, Automatic Speech Recognition, Reading Assessment, Teaching at the Right Level (TaRL), Moroccan Education System, e-learning

References

• Albudoor, N., & Pe, ña E. D. (2022). Identifying Language Disorder in Bilingual Children Using Automatic Speech Recognition. Journal of Speech, Language, and Hearing Research, 65(7), 2648–2661. https://doi.org/10.1044/2022_JSLHR-21-00667
• Aldarmaki, H., Ullah, A., Ram, S., & Zaki, N. (2022). Unsupervised Automatic Speech Recognition: A review. Speech Communication, 139, 76–91. https://doi.org/10.1016/j.specom.2022.02.005
• Azevedo, J. P., Hasan, A., Goldemberg, D., Iqbal, S. A., & Geven, K. (2020). Simulating the Potential Impacts of COVID-19 School Closures on Schooling and Learning Outcomes: A Set of Global Estimates. World Bank, Washington, DC. https://doi.org/10.1596/1813-9450-9284
• Bachiri, Y., & Mouncif, H. (2020). Applicable strategy to choose and deploy a MOOC platform with multilingual AQG feature. 2020 21st International Arab Conference on Information Technology (ACIT), 1–6. https://doi.org/10.1109/ACIT50332.2020.9300051
• Bachiri, Y., & Mouncif, H. (2022). Increasing Student Engagement in Lessons and Assessing MOOC Participants Through Artificial Intelligence. In M. Fakir, M. Baslam, & R. El Ayachi (Eds.), Business Intelligence (pp. 135–145). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-06458-6_11