The Effects of Universal Design for Learning and AI-AT on the Engagement and Academic Outcomes of Students with Disabilities
Keywords:
AI; engagement; inclusive; outcomes; people with disabilities; UDLAbstract
Challenges to inclusive educational practice in Indonesia?remain significant in addressing the responsive learning needs of students with special needs. The partial adaptive learning, the differing preparedness of educators, and their limited use of AI-based assistive technology reveal a mismatch between what is being done with respect to inclusivity goals. Specifically, it is our goal to empirically evaluate the validity and strength of UDL and AI-AT effectiveness and impact on student engagement and academic outcomes for students with disabilities. This study employed a quasi-experimental pretest–posttest design on students with disabilities in an inclusive elementary school in Central Java (purposive sampling; control n=69, experimental n=74). Data were collected through pretest–posttest tests, interaction observations, and AI-AT usage logs. Analysis was conducted using descriptive statistics and ANCOVA, followed by PLS-SEM to examine the relationship between UDL–engagement–academic outcomes and AI-AT moderation. Findings revealed that the combination of UDL, AI-AT, and Engagement as a single?model can predict academic success for students with disabilities in an inclusive educational setting (R2=0.830). The strongest predictor is AI-AT (? = 5.67, p < 0.001), followed by UDL, which shows significant predictive strength (? = 3.27, p < 0.001). UDL and AI-AT significantly contribute to learning involvement and academic outcomes in inclusive schools. The integration justifies the development of technology-enhanced inclusive pedagogical practices based on empirical evidence. The integration of UDL and AI into learning models practically has implications for active student engagement and forms the basis for measurement indices needed in inclusive learning assessments.
https://doi.org/10.26803/ijlter.25.3.24
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