AI tool use, self-efficacy, foreign language enjoyment, and willingness to communicate: a moderated serial mediation model among Chinese EFL learners

Abstract

Introduction The rapid proliferation of artificial intelligence (AI) tools in higher education has transformed the landscape of foreign language instruction, yet the mechanisms through which AI tool use shapes learners' affective and communicative engagement remain poorly understood. Drawing on social cognitive theory (SCT) and control-value theory (CVT), this study proposes and tests a moderated serial mediation model in which AI tool use predicts willingness to communicate (WTC) sequentially through AI self-efficacy and foreign language enjoyment (FLE), with foreign language anxiety (FLA) moderating the FLE-to-WTC link. Methods Survey data were collected from 420 EFL undergraduates at two Chinese universities (Zhejiang and Shaanxi Provinces). Covariance-based structural equation modeling (CBSEM) with maximum likelihood robust (MLR) estimation in Mplus 8.8 was used. Results The model revealed excellent fit. AI tool use significantly predicted AI self-efficacy (β = 0.42, p < 0.001), which predicted FLE (β = 0.38, p < 0.001) and WTC directly (β = 0.21, p = 0.001); FLE predicted WTC (β = 0.33, p < 0.001). Bootstrapped mediation analyses (10,000 replications) confirmed a simple indirect effect via self-efficacy [β = 0.091, 95% CI (0.042, 0.152)] and a serial indirect effect via self-efficacy and enjoyment [β = 0.053, 95% CI (0.021, 0.098)]. FLA moderated the FLE-to-WTC pathway (β = −0.19, p = 0.002), and the index of moderated mediation was significant [IMM = −0.017, 95% CI (−0.038, −0.004)]. Discussion These findings advance theoretical understanding of how AI tools cultivate positive affective trajectories and carry implications for emotionally responsive EFL pedagogy.