Perceived Usefulness, Trust, and the Effectiveness of Accounting Learning Using ChatGPT

Abstract

Artificial Intelligence (AI), particularly ChatGPT, has increasingly been integrated into higher education to support teaching and learning activities. However, the effectiveness of AI-assisted learning depends not only on the availability of the technology but also on students' perceptions of its usefulness and their trust in the technology. This study examines the effects of perceived usefulness and trust on the effectiveness of AI-assisted accounting learning among undergraduate accounting students in Indonesia. A quantitative research design was employed using a survey of 100 undergraduate accounting students who had experience using ChatGPT for learning purposes. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that both perceived usefulness and trust have positive and significant effects on accounting learning effectiveness, with the proposed model explaining 61.4% of the variance (R² = 0.614). The novelty of this study lies in the development of a measurement of AI-assisted accounting learning effectiveness by integrating the Technology Acceptance Model (TAM) and Trust Theory within the context of generative AI. The findings provide practical implications for higher education institutions and AI developers in designing AI-assisted learning environments that enhance students' perceived usefulness, trust, and accounting learning effectiveness.