Augmented accounting education using machine learning and decision-making in higher education

Introduction Accounting education in higher education faces the challenge of adapting to digitalized environments, which requires strengthening financial interpretation and decision-making through the use of emerging technologies such as machine learning. In this context, the objective of the study is to analyze the influence of augmented accounting education using machine learning on financial interpretation and decision-making among students at the Catholic University of Santa María. Method A quantitative, applied approach was adopted, with a non-experimental and cross-sectional design. The sample consisted of 261 students selected through non-probabilistic convenience sampling. A structured questionnaire of 20 items with a Likert scale was used, validated through reliability analysis, as well as convergent and discriminant validity. Data processing was carried out using structural equation modeling. Results The findings show that financial interpretation has the greatest significant effect on decision-making, followed by augmented accounting education. In contrast, digital competencies in accounting do not show a significant influence. The acceptance of hypotheses H1 and H2 is confirmed, while hypothesis H3 is rejected. Conclusion Technological integration strengthens accounting learning; however, the analytical capacity to interpret financial information constitutes the determining factor in decision-making, highlighting the need to integrate cognitive and technological skills in professional training.