关键词: 成就目标理论, 技术身份认同, 技术接受度, AI工具使用, AI素养

Abstract:

[Purpose/Significance] This study addresses the "motivation black box" problem. By integrating achievement goal theory and technology acceptance models, it aims to construct a four-dimensional "motivation-identity-cognition-engagement" theoretical framework to analyze the driving mechanisms underlying AI teaching assistant usage behavior. [Method/Process] A questionnaire survey was utilized in this study. The Chaoxing Learning platform served as the research context, and college students who use AI teaching assistants constitute the research subjects. The chain mediating effect between technical identity recognition and technical acceptance was tested using the structural equation modeling (SEM). The significance of the pathways was verified via the Bootstrap sampling method. Data analysis was performed using SPSS 26.0 and Smart PLS 3.3.9 software. [Results/Conclusions] Key findings reveal that within the learning environment integrating Chaoxing's online courses with AI teaching assistants, achievement goal orientations demonstrated significant divergence, with mastery-approach goals (MAP) emerging as the sole significant driver - other goal orientations showed no statistically reliable predictive effects. Crucially, MAP significantly promoted dependent (β=0.308), critical (β=0.262), and exploratory (β=0.244) usage behaviors through the "technology identity recognition → technology acceptance" chain-mediation pathway. Furthermore, technology identity recognition exhibited dual mediation dominance in behavior formation, as this chain-mediation pathway accounted for more than 50% of total effects across all three usage behaviors, particularly for dependent and exploratory usage. Notably, technology identity recognition demonstrated the strongest mediation effect specifically on dependent behaviors (β=0.418). Further analysis indicates MAP's total effect on technology identity recognition substantially exceeded its direct effect on technology acceptance. This critical finding aligns with Deci and Ryan's self-determination theory, confirming that intrinsic motivation (exemplified by MAP) facilitates deeper skill internalization. Specifically, students focused on competence development showed greater tendency to integrate AI skills into their self-concept (e.g., perceiving themselves as "technology-proficient learners") rather than viewing them merely as external tools - a mechanism that empirically explains why traditional technical training that emphasizes operational skills often fails to foster sustained usage. Most significantly, this research provides important implications for educators in guiding students' use of AI teaching assistants: they should prioritize cultivating students' mastery-approach goals (MAP) through instructional design that strengthens students' pursuit of knowledge. Such an approach enhances the effectiveness of AI tools in teaching while simultaneously offering direction for the Chaoxing Learning Platform to optimize its AI teaching assistant features. Specifically, the platform should enhance personalized learning support tailored to the needs of MAP-oriented users, thereby better aligning with students' intrinsic learning motivations.

Key words: achievement goal theory, technical identity, technical acceptance, use of AI tools, AI literacy