Achievement Goal Motivation Influences Self-Efficacy in AI-Supported Collaborative Programming

Abstract

This study designed an AI-integrated collaborative problem-based learning (CPBL) course aimed to bolster programming self-efficacy among undergraduate and to elucidate the underlying psychological mechanisms that drive self-efficacy. A total of 179 Taiwanese undergraduates participated in an 18-week intervention that integrated AI toolkits, CPBL frameworks, and instructional scaffolding to facilitate interdisciplinary application. To achieve our goals, two instruments were developed to measure programming-specific achievement goals and mindsets. The results indicated that the AI-supported CPBL environment effectively fostered a growth mindset, mastery experiences, and self-efficacy in programming. Mediation model analysis revealed that growth mindset and mastery experience functioned as dual mediators linking achievement goal orientation to self-efficacy. Notably, mastery goals exerted a markedly stronger influence on self-efficacy compared to performance goals. The findings highlight the necessity of prioritizing mastery-oriented pedagogy in the design of AI-integrated programming curricula.