Teacher Career Commitment in the Age of AI: Change Fatigue, Self-Efficacy and Workload in South Africa

Artificial intelligence (AI) is entering schools amid ongoing educational reforms, increasing workloads, and growing concerns about teacher retention. This study examines whether AI use, perceived AI opportunities and risks, change fatigue, teacher self-efficacy, and workload stress are associated with career commitment among South African lower-secondary teachers. Using TALIS 2024 public-use data, the analysis applied final teacher weights and 100 Fay balanced repeated replication weights to account for the complex survey design. Career commitment was assessed through overall job satisfaction and intention to leave teaching within five years. The analytic samples comprised 758 teachers for job satisfaction and 518 teachers younger than 50 for intended exit. Teachers experiencing greater change fatigue reported lower job satisfaction and were more likely to intend to leave teaching within five years. Higher teacher self-efficacy was associated with greater job satisfaction and lower intended exit, while workload stress was associated with lower job satisfaction. Neither AI use nor perceptions of its opportunities and risks showed a clear association with career commitment. The findings suggest that the context in which technological change occurs may matter more for teacher commitment than AI adoption alone. Sustainable AI integration should therefore combine infrastructure and professional learning with manageable workloads, coherent reform sequencing, teacher participation, and protected implementation time.