Self-efficacy in AI-assisted L2 academic writing: scale development and validation

The rise of generative artificial intelligence (AI) has fundamentally reshaped the landscape of second language (L2) academic writing, introducing novel demands on learners’ competencies and psychological resources. While self-efficacy is a well-established predictor of writing success, existing measurement scales predate the AI era and fail to capture learners’ confidence in navigating the unique human-AI collaborative process. This study addresses this gap by reporting on the development and validation of the Self-Efficacy in AI-Assisted L2 Academic Writing Scale (SE-AI-L2AWS). Following rigorous scale development procedures, data were collected from 680 Chinese university EFL students. The sample was randomly split for an Exploratory Factor Analysis (EFA; n = 340) and a Confirmatory Factor Analysis (CFA; n = 340). The EFA revealed a clear three-factor structure, which was subsequently confirmed by the CFA, demonstrating good model fit. The three emergent factors were: (1) AI-Assisted Process Regulation Efficacy, (2) AI-Assisted Language Efficacy, and (3) Ethical and Integrity Efficacy. The final 11-item scale demonstrated high internal consistency (α = .94) and robust evidence of convergent, discriminant, and concurrent validity through correlations with established measures of L2 writing self-efficacy and writing anxiety. The SE-AI-L2AWS provides a potentially reliable and valid instrument for researchers and educators to assess L2 learners’ confidence in using AI for academic writing. This tool could facilitate diagnostic assessment, guide pedagogical interventions, and advance research on the motivational dynamics of writing in the age of AI.