ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING INTEGRATION IN ACCOUNTING EDUCATION: PREDICTING THE ANALYTICAL COMPETENCIES OF ACCOUNTANCY STUDENTS
William Deyne C. Alejandro
Accountancy Department
Western Mindanao State University
Zamboanga City, Philippines
Abstract. The combination of Artificial Intelligence (AI) and Machine Learning (ML) has changed the face of accounting education, increasing students’ technological and analytical capacities. This study investigated the level of integration of AI and ML in accounting education and its impact on the analytical skills of the accountancy students with respect to the analysis of financial data and critical thinking. The results were acquired using a validated questionnaire using a quantitative descriptive-correlational study design from accountancy students. Data was analyzed using descriptive statistics and multiple regression analysis. The results indicate that the perception of AI and ML integration in accounting education is strong especially in the usage of students and ethical and responsible use of AI technology. Regression study also demonstrated that the integration of AI and ML had a considerable favorable influence on students’ analytical skills, particularly in the examination of financial data and critical thinking. The findings indicate that increased exposure to AI-enabled learning environments boosts students’ skills in interpreting financial data, solving complicated accounting problems and making evidence-based decisions while promoting responsible usage of AI. The study concludes that the integration of AI and ML in accounting courses efficiently develops the analytical, technological and ethical competences necessary from future accountants based on the Technology Acceptance Model. The study suggests that instructional strategies and curriculum innovation should be enhanced through AI to prepare graduates for the changing digital accounting profession.
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Source: International Journal of Applied Mathematics
ISSN printed version: 1311-1728
ISSN on-line version: 1314-8060
Year: 2025
Volume: 38
Issue: 2
References
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