The relationship between patterns of artificial intelligence use, academic resilience, and burnout among graduate students in special education departments at Saudi universities

Abstract

Introduction – Higher education institutions face increasing challenges in maintaining the psychological well-being of graduate students amid intensive academic pressures and rapid digital transformation. This study investigated the relationships between patterns of artificial intelligence (AI) application use, academic resilience, and burnout among graduate students in special education departments at Saudi universities, and determined the predictive capacity of these variables for burnout. Methods – A cross-sectional correlational descriptive design was employed. Data were collected from 367 graduate students (207 males, 160 females) using the Maslach Burnout Inventory (MBI-SS), the Brief Resilience Scale (BRS), and a developed scale for AI application usage patterns. Results – Results revealed low levels of AI application use and academic resilience, in contrast to high levels of burnout. Significant negative correlations were found between AI usage patterns and burtenout (r = −0.541, p < 0.001), and between academic resilience and burnout (r = −0.437, p < 0.001). AI application usage patterns explained 34.1% of the variance in burnout (R2 = 0.341, f2 = 0.52, a large effect size), while academic resilience explained 19.1% (R2 = 0.191, f2 = 0.24, medium effect). Discussion – These findings highlight the potential of technological competence as a psychological resource associated with reduced burnout. Structured AI training programs, institutional resilience interventions, and optimized research workloads are recommended in alignment with Saudi Vision 2030.