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Artificial Intelligence in accounting and finance education: Enhancing teaching and learning for business transformation and economic growth
Conference paper   Open access

Artificial Intelligence in accounting and finance education: Enhancing teaching and learning for business transformation and economic growth

Rengalwar Srinivasan, Abinaya Nagamuthu, Manikandan Sathyamoorthy and Prabuvengatesh -
National Conference 2026 – Artificial Intelligence for Business Transformation and Economic Growth (Port Moresby, Papua New Guinea, 11/03/2026–12/03/2026)
06/2026
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Abstract

Artificial Intelligence finance education ai-enabled learning educational technology curriculum innovation digital transformation student engagement teaching effectiveness accounting education learning analytics ai-based assessment intelligent tutoring systems workforce readiness digital skills development business transformation higher education
There are three main types of modifications that can be introduced concerning artificial intelligence in accounting and finance. First, AI creates intelligent learning environments which facilitate personalized and adaptive learning. Secondly, AI enables automation of the assessment process to provide timely feedback. Thirdly, AI gives learners an opportunity to analyze information regarding their learning process in real time. However, despite the development outlined above, some educational institutions continue to use conventional methods of teaching since those do not foster development of digital skills. For this reason, this paper will focus on the role of AI technologies in education, accounting and finance, and their effectiveness and outcomes. For the analysis of the topic under consideration, quantitative and qualitative methods for collection of empirical evidence were employed. Therefore, questionnaires were distributed among 132 students and 22 lecturers from IBSUniversity and Port Moresby Business College. All collected data was analyzed using descriptive statistics and reliability of scales was confirmed through correlation analysis and regressions. As a result, the presence of high levels of AI awareness was identified among both students and teachers. Moreover, chatbots turned out to be the most popular tool. IBSUniv.j.bus.res 75 Cronbach Alpha test has shown that all scales used in the research turned out to be reliable. In addition, the research has revealed that AI learning factors demonstrate considerable predictive validity in respect of the efficacy of teaching. The most important predicting factor is students' interaction with AI technologies. Barriers to using AI technologies include insufficient training, technical issues and ethical concerns.

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