The advent of Artificial Intelligence (AI) has transformed Learning Management Systems (LMSs), enabled personalized adaptation and facilitated distance education. This study employs a bibliometric analysis based on PRISMA-2020 to examine the integration of AI in LMSs from an educational perspective. Despite the rapid progress observed in this field, the literature reveals gaps in the effectiveness and acceptance of virtual assistants in educational contexts. Therefore, the objective of this study is to examine research trends on the use of AI in LMSs. The results indicate a quadratic polynomial growth of 99.42%, with the years 2021 and 2015 representing the most significant growth. Thematic references include authors such as Li J and Cavus N, the journal Lecture Notes in Computer Science, and countries such as China and India. The thematic evolution can be observed from topics such as regression analysis to LMS and e-learning. The terms e-learning, ontology, and ant colony optimization are highlighted in the thematic clusters. A temporal analysis reveals that suggestions such as a Cartesian plane and a league table offer a detailed view of the evolution of key terms. This analysis reveals that emerging and growing words such as Learning Style and Learning Management Systems are worthy of further investigation. The development of a future research agenda emerges as a key need to address gaps.

Women’s financial literacy and financial inclusion have gained prominence in recent years. Despite progress, knowledge and access to finance remain common barriers for women, especially in emerging economies. Globally, domestic and economic violence has been recognized as a relevant social concern from a gender perspective. In this context, financial literacy and financial inclusion are considered to play a key role in reducing violence against women by empowering them with the necessary knowledge to manage their financial resources and make informed decisions. This study aims to evaluate the determinants that influence Peruvian female university students’ financial literacy and financial inclusion. To this end, a theoretical behavioral model is proposed, and a survey is applied to 427 female university students. The results are analyzed using a Partial Least Squares Structural Equation Model (PLS-SEM). The results validate all the proposed hypotheses and highlight significant relationships between financial literacy and women’s financial inclusion. A relevant relationship between financial attitude and financial behavior is also observed, as well as the influence of financial behavior and financial self-efficacy on financial literacy. The results also reveal that women feel capable of making important financial decisions for themselves and consider that financial literacy could help reduce gender-based violence. Based on these findings, theoretical and practical implications are raised. It highlights the proposal of a theoretical model based on antecedents, statistically validated in a sample of women in Peru, which lays the foundation for understanding financial literacy and financial inclusion in the Latin American region.

This paper presents an effective method for performing audio steganography, which would help in improving the security of information transmission. Audio steganography is one of the techniques for hiding secret messages within an audio file to maintain communication secrecy from unwanted listeners. Most of these conventional methods have several drawbacks related to distortion, detectability, and inefficiency. To mitigate these issues, a new scheme is presented which combines the techniques of image interpolation with LSB encoding. It selects non-seed pixels to allow reversibility and diminish distortion in medical images. Our technique also embeds a fragile watermarking scheme to identify any breach during transmission to recover data securely and reliably. A magic rectangle has also been used for encryption to enhance data security. This paper proposes a robust, low-distortion audio steganography technique that provides high data integrity with undetectability and will have wide applications in sectors like e-healthcare, corporate data security, and forensic applications. In the future, this approach will be refined for broader audio formats and overall system robustness.

With the rapid development of artificial intelligence (AI) technology, its application in the field of auditing has gained increasing attention. This paper explores the application of AI technology in audit risk assessment and control (ARAC), aiming to improve audit efficiency and effectiveness. First, the paper introduces the basic concepts of AI technology and its application background in the auditing field. Then, it provides a detailed analysis of the specific applications of AI technology in audit risk assessment and control, including data analysis, risk prediction, automated auditing, continuous monitoring, intelligent decision support, and compliance checks. Finally, the paper discusses the challenges and opportunities of AI technology in audit risk assessment and control, as well as future research directions.