درخت حوزه‌های تخصصی

This study aims to examine the relationship between self-advocacy skills, assistive technology use, and self-esteem among adults with physical disabilities. A cross-sectional design was employed, with data collected from 244 adults with physical disabilities. Participants completed validated self-report measures assessing self-esteem, self-advocacy skills, and assistive technology use. Pearson correlation coefficients were calculated to explore the relationships between the variables. A linear regression analysis was conducted to determine the extent to which self-advocacy skills and assistive technology use predict self-esteem. All analyses were performed using SPSS version 27. The results indicated significant positive correlations between self-esteem and both self-advocacy skills (r = .62, p < .01) and assistive technology use (r = .58, p < .01). Self-advocacy skills and assistive technology use were found to be significant predictors of self-esteem, accounting for 49% of the variance (R² = .49, F(2, 241) = 115.63, p < .001). The regression analysis revealed that self-advocacy skills (B = 0.34, β = .42, p < .001) and assistive technology use (B = 0.28, β = .36, p < .001) both positively influence self-esteem. The study concludes that self-advocacy skills and assistive technology use are crucial determinants of self-esteem in adults with physical disabilities. These findings highlight the importance of providing support and training in self-advocacy and access to appropriate assistive technology to enhance psychological well-being and autonomy in this population. Future research should explore these relationships longitudinally and across diverse disability groups.

This study aims to explore teachers' narratives on implementing AI-based learning tools for students with severe disabilities. The objective is to understand the challenges faced, the impacts on students, and the experiences of teachers using these technologies in special education settings. A qualitative research design was employed, using semi-structured interviews to gather data from 16 certified special education teachers with experience in using AI-based learning tools. Participants were selected through purposive sampling to ensure a diverse range of perspectives. Interviews were transcribed and subjected to thematic analysis to identify key themes and patterns in the data. Three main themes emerged from the analysis: implementation challenges, the impact on students, and teacher experiences. Teachers reported significant technical difficulties, inadequate training, and limited administrative support as major challenges. Despite these issues, AI tools had a positive impact on student engagement, motivation, learning outcomes, social interaction, and behavioral changes. Teachers experienced a range of emotions, from initial skepticism to eventual acceptance, and highlighted the importance of peer support and professional growth. The study underscores the transformative potential of AI in special education, while also identifying critical areas that need attention for successful implementation. The integration of AI-based learning tools in special education offers significant benefits for students with severe disabilities, including enhanced engagement, improved learning outcomes, and better social interactions. However, successful implementation requires addressing technical issues, providing comprehensive training, securing administrative support, and fostering a culture of feedback and reflection. Future research should focus on long-term impacts, comparative effectiveness of different AI tools, and the development of effective training programs. Educational institutions must prioritize these areas to maximize the positive impact of AI technologies in special education.