teacher experiences

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.

This study aims to explore the experiences of special education teachers in implementing differentiated instruction (DI) within mixed-ability classrooms. This qualitative research utilized a phenomenological approach to gain in-depth insights into the participants' experiences. Data were collected through semi-structured interviews with 25 special education teachers from various schools in a metropolitan area. Participants were selected through purposive sampling to ensure they had significant experience with DI. The interviews, each lasting 60 to 90 minutes, were transcribed verbatim and analyzed using thematic analysis. The data collection continued until theoretical saturation was achieved, ensuring a comprehensive understanding of the teachers' experiences. The analysis revealed three main themes: challenges in differentiated instruction, strategies for effective differentiation, and perceived effectiveness and outcomes. Teachers reported significant challenges, including resource limitations, managing student diversity, assessment difficulties, increased workload, and issues with parental involvement. To address these challenges, teachers employed strategies such as flexible grouping, curriculum adaptation, professional development, collaborative planning, and enhancing student engagement. The perceived effectiveness of DI included improved student progress, positive changes in classroom dynamics, increased teacher satisfaction, and greater inclusivity. Differentiated instruction is a vital approach in mixed-ability classrooms, promoting equitable learning opportunities for all students. Despite the challenges, the strategic implementation of DI leads to significant positive outcomes in student performance and classroom inclusivity. Schools should support teachers through professional development, adequate resources, and collaborative opportunities to enhance the effectiveness of DI. Further research should focus on larger, diverse samples and mixed-methods approaches to provide a more comprehensive understanding of DI practices.