Career adaptability under AI-related employment pressure: a multi-method study of relational structure and configurational pathways.

Objective: This study examines the factors, relational structure, and exploratory configurational associations related to career adaptability among university students in the school-to-work transition under artificial intelligence-related employment pressure. Methods: Grounded primarily in Social Cognitive Career Theory and supplemented by Career Construction Theory, this study draws on questionnaire data from 564 Chinese university students and integrates structural equation modelling, mediation analysis, multigroup analysis, necessary condition analysis, and fuzzy-set qualitative comparative analysis. Results: Career adaptability was directly associated with multidimensional cognitive, behavioural, and contextual factors, as well as with indirect pathways involving outcome expectations and goal reengagement intention. However, the serial indirect effects were generally weak, indicating that career adaptability was not explained primarily by a single sequential cognition-action pathway. The overall relational structure was largely stable across undergraduate and postgraduate groups, with differences in the strength of only a few paths. Necessary condition analysis identified no single necessary condition with a substantial practical effect, whereas the exploratory configurational analysis identified multiple combinations of conditions associated with higher career adaptability, suggesting substitution and compensation among different resources. Artificial intelligence threat perception was not associated exclusively with poorer adaptation; under specific configurations of conditions, it coexisted with higher career adaptability. Conclusion: Career adaptability in an employment context shaped by artificial intelligence is not determined by a single factor; rather, it represents a complex adaptive structure jointly associated with multiple cognitive, behavioural, and contextual resources. By combining the explanation of the relationship between career cognition and action offered by Social Cognitive Career Theory with the multi-resource perspective on adaptation provided by Career Construction Theory, this study advances understanding of career adaptability in contexts where artificial intelligence-related pressure and opportunities for collaboration coexist. The findings suggest that career education and employment support in universities should not be confined to training in a single skill. Instead, they should integrate capability beliefs, career judgements, technology-related practice, goal adjustment, and external support to provide diverse career preparation pathways for students with different resource profiles.