Artificial Intelligence Use and Cognitive Resource Allocation: Nonlinear Associations with Mental Workload and Perceived Performance.

This study investigated how pre-service teachers' frequency of artificial intelligence (AI) tool use is associated with their perceived workload and perceived task performance in AI-supported pedagogical design tasks, grounded in mental workload theory. Using a quantitative cross-sectional survey design, data were collected online from 464 pre-service teachers through a structured questionnaire covering AI use profiles, purposes of AI use, perceived usefulness of AI functionalities, and NASA-TLX-based workload ratings. Perceived workload was assessed using the raw NASA-TLX after participants completed an AI-supported lesson planning task involving the design of an instructional activity and teaching materials. Descriptive findings showed that AI use was widespread, with most participants reporting weekly or daily engagement. One-way ANOVA results indicated significant differences across AI use-frequency groups on all NASA-TLX dimensions. Infrequent users reported higher mental demand, temporal demand, effort, and frustration, as well as lower perceived task performance. However, polynomial trend analyses showed that the association was not strictly linear across all dimensions. Some workload dimensions were lowest among occasional users, whereas frustration decreased and perceived task performance increased as AI use frequency increased. These findings suggest that the workload implications of AI engagement may vary depending on the specific dimension of task demand considered.