In recent years, the introduction of Information and Communications Technologies (ICTs) into several aspects of daily life has become more relevant, particularly in higher education. This phenomenon has resulted in a digital integration that involves the infrastructure of higher education institutions as well as the materials used in the teaching process. Therefore, a study was conducted to investigate the factors that determine usage intention of Virtual Learning Objects (VLOs) by college students in finance and accounting programs in the city of Medellín. This quantitative research employed 86 self-administered questionnaires that enabled us to identify a positive association between attitude toward usage, subjective norms, and the intention to employ this type of content as a learning strategy. In conclusion, models like this need to be developed to understand the acceptance of new technologies in learning processes from different perspectives, and even more so considering that most of the existing literature regarding the subject has been focused on qualitative studies.

Interactive learning objects (ILOs) are tools that can enhance and promote teaching in various fields of knowledge. For instance, GeoGebra is free educational software designed for teaching and learning mathematics. It is interactive mathematical software that can help students better understand accounting concepts and visualize complex financial relationships, making it easier to learn abstract concepts. Additionally, it can be used to explore what-if financial scenarios, which can help students develop their decision-making skills. The purpose of this article is to investigate the impact of GeoGebra on the development of mathematical skills among accounting science students. It contributes to the literature by demonstrating the effectiveness of teaching financial concepts through the application of five linear functions and their mathematical relationships in GeoGebra. This approach bridges the gap between differential calculus knowledge and the ability to achieve a geometric understanding and interpretation of the lines that can be created with this dynamic software. It also enables the interpretation of financial statement concepts based on the variations of the lines. GeoGebra, mathematical software, will feature a comprehensive income statement (CIS) as one of its relevant financial statements in financial accounting. Starting with an equation containing 15 variables, 2 are selected to represent the abscissa and ordinate axes, while the remaining 13 are considered parameters that can be adjusted by the user in the OIA windows. By changing these values, the equation will produce a straight line. It is worth noting that using OIA can be an effective strategy for enhancing one’s understanding of the basic sciences. The study emphasizes the significance of accuracy in financial management, the interplay among variables, and the utilization of technological tools to aid in comprehending these concepts.

The advent of machine learning has transformed the landscape of finance, enabling groundbreaking advances in data analysis, prediction, risk management, fraud detection, market analysis, and crisis management. However, despite these remarkable achievements, there remain significant research gaps. This study aims to examine the research trends surrounding the utilization of machine learning in the financial sector. A bibliometric analysis, which employs the parameters set forth in the international declaration PRISMA-2020, is proposed as an exploratory methodology to address this objective. The principal findings include a growing interest in machine learning in finance, with a particular focus on the period between 2021 and 2023. In addition, authors, influential journals, and emerging concepts are identified. The thematic evolution reflects a shift from an approach based on foreign exchange derivatives to one based on deep learning, opening opportunities in risk management, financial contracts, and forecasting. The research focuses on leading and consolidated concepts such as classification, fraud detection, smart contracts, big data, pointing out future directions and strengthening the basis for a robust and enriching research agenda in the constant financial and technological evolution. This study uniquely analyzes the evolving landscape of machine learning in finance using PRISMA-2020. The study identifies emerging trends, influential contributors, and a shift to deep learning, highlighting key concepts such as classification, fraud detection, smart contracts, and big data. This insight contributes to a forward-thinking research agenda in the ever-changing realm of finance and technology.