Multimodal Technology Reliance Predicts English-Chinese Interpreting Outcomes: Survey Evidence from Non-English Major Undergraduates in China

Abstract

Multimodal technologies are increasingly embedded in interpreter training, yet learner perceptions of such technologies and their relationship with interpreting learning outcomes remain underexplored. This study surveyed 312 non-English major undergraduates (above CET-4, STEAM backgrounds) from multiple universities in Beijing who completed a compulsory English-Chinese interpreting course incorporating multimodal technology resources including video captioning, audio playback, speech recognition, and parallel multimodal materials. Using structural equation modeling, a partial mediation model was examined in which multimodal technology reliance (MTR) predicts interpreting learning outcomes (ILO) both directly and indirectly through learning engagement (LE). Results showed that MTR had a significant total effect on ILO (β = .37, p < .001), with a direct path (β = .16, p < .05) and an indirect path via LE (β = .21, p < .01) that accounted for 56.8% of the total effect. The model demonstrated good fit: CFI = .93, RMSEA = .067, SRMR = .048, chi2/df = 2.38. Common method bias was assessed using Harman’s single-factor test and an unmeasured latent method construct (ULMC) approach. The first unrotated factor accounted for 44.2% of the total variance (below the 50% threshold), and the ULMC analysis showed that all substantive loadings remained significant with minimal change (most |Δλ| < .10), together indicating that common method bias was not a serious concern. These findings suggest that fostering purposeful multimodal technology use may enhance interpreting learning in part by strengthening learner engagement, offering empirical guidance for designing technology-enhanced interpreting curricula that prioritize engagement as a core mechanism.