Effectiveness of AI-Supported Instruction on Academic Achievement of Students with Learning Disabilities
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Objective: This study aimed to investigate the effectiveness of AI-supported instructional approaches in enhancing academic achievement in reading, writing, and mathematics among primary-school students with learning disabilities.
Methods and Materials: A quasi-experimental pre-test/post-test control-group design was employed. Students with learning disabilities were assigned to an experimental group receiving AI-supported instruction and a control group receiving conventional teaching methods. The AI-supported system provided adaptive learning, real-time feedback, and individualized content adjustments. Academic achievement was assessed using standardized tests in reading, writing, and mathematics before and after the intervention. Data were analyzed using ANCOVA to control for baseline differences and evaluate post-intervention outcomes.
Findings: The experimental group showed significant improvements across all domains from pre-test (M = 66.45–72.40) to Post-test I (M = 108.40–113.20) and maintained moderate-to-high scores at Post-test II (M = 100.80–110.60). Repeated-measures ANOVA indicated significant main effects of time and group × time interactions for reading, writing, and mathematics (p < 0.001), with large effect sizes (η²_partial = 0.45–0.65, Cohen’s d ≈ 1.2–1.5). The control group showed minimal change over the same period.
Conclusion: AI-supported instruction significantly enhances academic achievement in students with learning disabilities. Adaptive learning technologies can effectively address individual learning needs, improve skill acquisition, and provide sustainable educational benefits.
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