Date
Publisher
arXiv
Artificial Intelligence AI has emerged as a transformative innovation in inclusive science education for disabled learners in rural New Mexico. Using a mixed method design that combined multiple linear regression and an Artificial Neural Network ANN model, this study examined 120 students in grades 6 to 10 and 15 instructors across four rural schools. The AI-based learning intervention predicted student performance with high accuracy R2 equals 0.92, and p less than 0.05. Experimental results showed a 32 percent improvement in science concept retention, a 27 percent increase in laboratory performance, and a 42 percent rise in student engagement following the intervention. These findings demonstrate that AI-driven pedagogy can serve as a transformative equalizer, improving engagement, comprehension, and accessibility for disabled learners. The study concludes that AI is a promising tool for achieving equitable science education in underserved rural settings.
Study at a glance
- Question: How does an AI-driven, game-based learning platform affect science learning and engagement for rural disabled students?
- Setting and data: Four rural New Mexico schools, 120 disabled students in grades 6–10 and 15 science instructors, over an 8-week intervention.
- Method: Mixed methods combining an experimental pre- and post-test intervention, multiple linear regression, an artificial neural network, observations, and surveys.
Why it matters
Rural schools often face teacher shortages, limited laboratory resources, and poor broadband, making it difficult to deliver equitable science education to students with disabilities.
Key findings
- Following an 8-week AI intervention, students improved science concept retention by 32%, laboratory performance by 27%, and classroom engagement by 42%.
- Weekly AI interaction time and engagement rate were significant positive predictors of learning gains, explaining 92% of the variance in a regression model.
- Neural network analysis suggested that learning gains were amplified for the lowest-performing students when combined with high engagement.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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