Evaluating the effectiveness of AI-Based adaptive learning for reading development and self-regulated learning among pupils with dyslexia in Abuja, Nigeria

Children with dyslexia often experience persistent challenges in reading, motivation, and self-regulation within traditional classroom settings that rely on uniform instructional approaches. With the growing integration of artificial intelligence in education, AI-based adaptive learning systems have emerged as innovative tools capable of personalizing instruction and supporting diverse learning needs. This study investigated the effect of AI-based adaptive learning systems on reading performance, motivation, and self-regulation among primary school students with dyslexia in Abuja, Nigeria. A quasi-experimental pretest–posttest control group design was adopted, with a sample of 60 Primary 5 pupils drawn from selected inclusive and special education schools in the Abuja Municipal Area. Data were collected using a Reading Performance Test, Motivation toward Reading Scale, and Self-Regulation in Reading Questionnaire, and analyzed using descriptive and inferential statistics, including independent samples t-test. Findings revealed that students exposed to AI-based adaptive learning demonstrated significantly higher improvements across all variables compared to those receiving conventional instruction. The experimental group recorded substantial gains in reading performance (mean gain = 24.20), motivation (mean gain = 1.50), and self-regulation (mean gain = 1.60), compared to the control group. Statistical analysis confirmed that these differences were significant (p < 0.05). These findings align with Cognitive Load Theory, Self-Determination Theory, and Vygotsky’s Zone of Proximal Development. The study concludes that AI-based adaptive learning systems offer an effective approach for improving both academic and psychological outcomes among students with dyslexia. It recommends the integration of AI-driven instructional tools in primary education, alongside teacher training and policy support, to promote inclusive, personalized, and effective learning environments.