The integration of generative artificial intelligence (GenAI) into higher education has created new possibilities for supporting complex academic tasks, yet its adoption for outcome-based education (OBE) curriculum development remains underexplored, particularly within Islamic higher education contexts. This study examines the roles of AI literacy, perceived usefulness, and trust in shaping lecturers' acceptance of GenAI for OBE-based curriculum development. Drawing on an extended Technology Acceptance Model (TAM) framework, the study proposes a parallel mediation model in which AI literacy serves as an antecedent that influences behavioral intention both directly and indirectly through perceived usefulness and trust as distinct mediating mechanisms. A quantitative cross-sectional survey was conducted with 248 lecturers from Indonesian Islamic higher education institutions who were involved in curriculum development activities. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results revealed that AI literacy significantly and positively influenced perceived usefulness (β = 0.562, p < 0.001) and trust (β = 0.527, p < 0.001). Both perceived usefulness (β = 0.353, p < 0.001) and trust (β = 0.328, p < 0.001) significantly predicted acceptance, while AI literacy also exerted a significant direct effect (β = 0.238, p < 0.001). Mediation analysis confirmed that perceived usefulness and trust function as complementary partial mediators, with the total indirect effect (β = 0.371, p < 0.001) accounting for 65.5% of AI literacy's total effect on acceptance. The model explained 47.2% of the variance in lecturers' acceptance. These findings contribute theoretically by extending TAM with AI literacy as a multidimensional antecedent and by empirically validating parallel cognitive and relational pathways to GenAI acceptance in a task-specific professional context. Practically, the study provides evidence-based recommendations for Islamic higher education institutions seeking to promote responsible GenAI adoption through comprehensive AI literacy development, simultaneous attention to utility demonstration and trust-building, and institutionalized human-in-the-loop verification protocols.
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