Exploring the Key Factors Influencing College Students’ Willingness to Use AI Coding Assistant Tools: An Expanded Technology Acceptance Model

Abstract

The application of artificial intelligence (AI) in programming assistance has garnered researchers’ attention for its potential to reduce learning costs for users, increase work efficiency, and decrease repetitive coding tasks. However, given the novelty of AI Coding Assistant Tools (AICATs), user acceptance is currently limited, and the factors influencing this phenomenon are unclear. This study proposes an expanded model based on the Technology Acceptance Model (TAM) that incorporates the characteristics of AICAT users to explore the key factors affecting college students’ willingness to use AICATs. Utilizing a survey methodology, 303 Chinese participants completed the questionnaire. Factor analysis and Structural Equation Modeling (SEM) results indicate that users’ dependence worry (DW) about AICATs positively affects perceived risk (PR), which in turn negatively impacts perceived usefulness (PU) and perceived ease of use (PEOU), thus reducing user willingness to use. Dependence concerns also negatively impact perceived trust (PT), while PT positively affects PU and PEOU, thereby enhancing willingness to use. Additionally, a user’s self-efficacy (SE) negatively impacts DW and positively affects PEOU. This study discusses the potential significance of these findings and offers suggestions for AICAT developers to foster and promote widespread use.