Adoption of AI-coding assistants in programming education: exploring trust and learning motivation through an extended technology acceptance model

Abstract The integration of artificial intelligence-based coding assistants (AI-CAs) into computing education introduces both opportunities and challenges, particularly for programming tasks. The effective adoption of these tools requires attention to students’ trust, learning motivation, and perception of AI-CAs’ reliability. Therefore, this study explores how undergraduate computer science students perceive and accept AI-CAs for programming tasks. The study analyzed computer science students’ perceptions of using AI-CAs, collecting responses from 311 students across different semesters in Mexico. This study extended the technology acceptance model (TAM) by incorporating six external factors: three from learning motivation (learning motivation, achievement goals, and subjective norms) and three trust-related factors (trust, doubt, and insecurity). The data analysis was performed using structural equation modeling (SEM). The results show that certain factors, such as learning interest, achievement goals, and trust, significantly enhance students’ perceived usefulness and ease of use, thereby increasing their intention to adopt AI-CAs for programming tasks. On the contrary, subjective norms, doubt, and insecurity showed limited influence on acceptance. Overall, this research validates the TAM in the context of AI-CAs in programming education and provides actionable insights for educators aiming to leverage AI tools effectively in computing education. It also offers fresh perspectives on adopting AI-CAs in programming education and reveals new perspectives on factors influencing students’ engagement with AI-CAs.