Human-AI collaboration role ambiguity and university teachers' professional identity: the mediating role of meaningful work.

INTRODUCTION: Generative artificial intelligence (GenAI) is changing not only how university teachers perform tasks but also how professional responsibilities are divided between human actors and intelligent systems. Drawing on role theory, meaningful work scholarship, and research on professional identity, this study examined whether meaningful work mediates the longitudinal relationship between role ambiguity in human - AI collaboration and university teachers' professional identity. METHODS: A three-wave survey of university teachers was conducted at four-week intervals; 1,074 complete matched cases formed the final analytical sample. Role ambiguity, meaningful work, and professional identity were repeatedly measured, and the analyses included formal common-method checks, reliability and validity analyses at every wave, longitudinal measurement-invariance tests, baseline-adjusted hierarchical regressions, and a temporally ordered bootstrap mediation analysis. RESULTS: Time 1 role ambiguity was associated with lower Time 2 meaningful work (β = -0.193, p < 0.001; ΔR 2 = 0.031) and lower Time 3 professional identity (β = -0.154, p < 0.001; ΔR 2 = 0.018). Time 2 meaningful work was positively associated with Time 3 professional identity (β = 0.219, p < 0.001; ΔR 2 = 0.017). The longitudinal indirect effect was B = -0.040 (BootSE = 0.006, 95% CI [-0.052, -0.028]), with a remaining direct association of B = -0.111, consistent with partial mediation. These incremental effects were statistically reliable but small. DISCUSSION: The results suggest that GenAI use frequency alone does not capture the professional consequences of AI-supported academic work; clarity about who does what, who decides, and who remains accountable also matters.