An AI-Driven Multimethod Framework for Inclusive Education: A Cross-Regional Survey Analysis
Keywords:
Artificial Intelligence, Inclusive Education, Learning Analytics, Survey Analysis, Educational Data Mining, AI in Higher EducationAbstract
The integration of Artificial Intelligence (AI) into educational science offers transformative opportunities to advance inclusive education, particularly for students with visual impairments. This study presents an AI-driven analysis of surveys conducted across European and Georgian higher education institutions, drawing on responses from students, faculty, medical professionals, and psychologists. Using a mixed-methods design, the research combines traditional statistical analysis with advanced AI methodologies, including machine learning algorithms, natural language processing (NLP), and predictive modeling. The findings reveal nuanced patterns of needs, challenges, and institutional practices, and the paper provides evidence-based recommendations for policymakers and educators. This work demonstrates the capacity of AI methodologies to substantially enhance the rigor, accuracy, and practical applicability of educational research on disability inclusion. This study not only applies AI techniques but also conceptualizes a unified multimethod analytical framework for inclusive education research.
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