Enhancing English Speaking Motivation among Chinese University Students: The Role of Immediate Feedback in AI-Driven Chatbots

Abstract

The rapid advancement of artificial intelligence (AI) technology has introduced innovative opportunities to university English speaking instruction. Its personalized and immediate feedback capabilities provide new avenues for enhancing students’ learning motivation. However, traditional English speaking instruction faces limitations such as insufficient practice opportunities and delayed feedback, often resulting in low student motivation and suboptimal learning outcomes. While existing studies suggest that AI-driven Chatbots can boost learning motivation through immediate feedback, the specific mechanisms underlying this effect remain underexplored within the context of Chinese university English teaching. This study seeks to verify the effectiveness of AI-driven Chatbots in improving Chinese university students’ English speaking learning motivation, with a focus on the role of the immediate feedback mechanism, and to propose teaching strategies informed by constructivist theory, while highlighting its theoretical and contextual significance. The research adopted a one-group pretest-posttest experimental design, involving 175 non-English major undergraduate students from Chinese universities (valid sample of 118). The AI-driven Chatbots "Doubao" was implemented over a two-week intervention period. Data were gathered via pre- and post-intervention motivation questionnaires and a post-intervention feedback perception questionnaire. Analytical approaches included paired sample t-tests, Spearman correlation analysis, and linear regression analysis to ensure robust and reliable findings. Findings revealed significant increases in students’ intrinsic motivation (Cohen’s d = 0.45) and extrinsic motivation (Cohen’s d = 0.41) post-intervention (p < 0.001). Immediate feedback perception showed a significant positive correlation with both intrinsic motivation (r = 0.602, p < 0.001) and extrinsic motivation (r = 0.531, p < 0.001). Regression analysis further confirmed its predictive role in motivation enhancement (intrinsic motivation R² = 0.237, extrinsic motivation R² = 0.178). These results indicate that AI tools bolster students’ sense of competence and autonomy through "scaffolding" support. The study recommends that educators incorporate AI tools into teaching practices, establish an "AI+peer" collaborative model, design contextualized speaking inquiry activities, and leverage AI feedback to foster reflective practice.