ARTIFICIAL INTELLIGENCE

This study examines the influence of Artificial intelligence on jobs: unemployment and displacement. It captures the implication of Artificial intelligence on the workforce as a whole. The Literature review of the study was segmented into three(3) sections namely; Conceptual review, Theoretical review, and Empirical review. Major Statistical tools of analysis used includes; data visualizations using histograms and the multinomial logistic regression. All tests done were conducted at the 0.05 level of significance. Major findings show that python, AI algorithms, certifications, problem solving, mathematics, and the other skills have a significant effect on getting an AI-related job. The study concludes that the impact AI has on jobs, is more on job loss as regards low-skilled workers, however AI has the ability to complement human workers, which will in turn lead o increased efficiency and productivity. Also as regards job displacement, it will affect mainly low-skilled and routine jobs.

The main purpose of this study was to examine the impact of artificial intelligence on the accounting profession. It examines the usefulness of artificial intelligence to the accounting profession. The findings indicate that artificial intelligence and the accounting profession are positively correlated, and that AI will have an impact on the accounting profession in the future.The accounting profession's adoption of artificial intelligence has improved the quality of financial information, relevance, faithful representation, efficiency, and corporate governance information. However, it is advised that a comparison of the use of the accounting profession in other fields and in other accounting professions could offer some insights into institutional and cultural factors that influence the decision to use artificial intelligence. Additionally, the use of AI technology can help improve the quality of their asset base and lower leverage ratios by reducing debt. A survey research design was used in the study. A total of 50 questionnaires were distributed equally among penultimate, final-year students and faculty members working in the accounting department of the University of Benin in Benin City, Edo State, as the primary method of data collection. Regression analysis was used to formulate and test five hypotheses.The analysis's findings led to the acceptance ofthe alternative hypotheses and the rejection ofthe five null hypotheses. Thus, it was determined that artificial intelligence significantly affects how the accounting profession is perceived. According to the study, the accounting profession should implement stronger artificial intelligence procedures in order to enhance the caliber of their financial reporting and, consequently, their overall worth.

The Purpose of this study was to assess the influence of generative artificial intelligence on business education students' academic performance, in university of benin, benin city, Edo state. The total population for this study is one hundred and twenty (120) Business Education students from 100 level to 400 level of the 2022/2023 set, from the department of vocational and Technical Education, Faculty of Education, University of Benin, Benin City and a sample size of One hundred and sixteen (116) was used, using the proportionate stratified random sampling techniques. Five research questions were raised to guide the study, with a 0.05 significant level. A descriptive survey design was adopted. A questionnaire containing thirty-three (33) items was the instrument used in obtaining responses from the respondents. The instrument was validated by experts, using the T-Retest method. Its reliability co-efficient was 0.89. The data collected were analyzed using frequency counts, mean and standard deviation, while independent sample T-test will be used to address the research Hypothesis. The analysis of the data revealed the current state of student's exposure and experience with artificial intelligence in the realm of education in improving business education student’s academic performance. The findings also underscore the awareness of business education students on various generative artificial intelligence and how it is being used to improve their academic performance. It was evident that AI has a substantial impact on various facets of business education students learning, including access to learning materials, customization of learning experience, collaboration, instant
feedback, and the development of critical thinking and problem solving skills. The study also identified some key challenges such as laziness, lack of originality, examination malpractices, overdependence on technology, the ability to reduce classroom attendance and developing Erosion of Human Creativity. This research underscores the potential of generative AI in education and highlights areas for improvement in addressing the challenges that arise with its implication. The following recommendations were made based on this finding; Authorities in Business Education department should recognize the challenges associated with the integration of AI in Business Education. Business Education department should invest in technical support and provision of materials needed for administering generative AI to business education students. Educators in business education department should receive trainings to adapt to new AI-based teaching methods, and a balance should be struck between traditional teaching and AI-based methods to ease the transition to harness the full potential of generative AI among business education students, Business Education department should focus on creating more customized learning platforms and environments. This involves developing AI-driven tools and platforms that cater to individual learning needs and preferences, ultimately a more personalized and effective learning experience for each student in business education.

This study investigates the impact of Artificial Intelligence (AI) on audit efficiency within the private sector. The rapid advancement of AI technologies has transformed traditional auditing processes by enhancing data accuracy, speed, and decision-making. The objectives of this research are to examine the effect of AI on audit efficiency, evaluate the challenges auditors face in adopting AI-driven tools, ascertain the implications of AI integration on the future roles and skills required of auditors, and determine how AI supports auditors’ professional judgment and decision-making during audits. The study adopts a quantitative research approach through the administration of structured questionnaires to auditors in selected private organizations. The data collected were analysed using descriptive and inferential statistical tools. Findings reveal that the adoption of AI significantly improves audit efficiency by automating repetitive tasks, reducing human error, and enabling real-time data analysis. However, the study also identifies key challenges, including high implementation costs, lack of technical expertise, data security concerns, and resistance to technological change. Furthermore, the integration of AI necessitates the acquisition of advanced digital and analytical skills among auditors to remain relevant in the evolving audit environment. The study concludes that while AI serves as a strategic tool for improving audit quality and efficiency, adequate training and organizational support are essential for its effective implementation.

This study investigates the impact of Artificial Intelligence (AI) on entrepreneurship education among students of the University of Benin, Benin City, Nigeria. It focuses on how selected AI tools ChatGPT, Grammarly, Quillbot, and Meta AI affect students’ learning experiences, creativity, and innovative capacities within entrepreneurship courses. A cross sectional survey research design was adopted, and data were collected from one hundred (100) undergraduate students in the Department of ntrepreneurship through a structured questionnaire. Descriptive and inferential statistical techniques, including correlation and regression analyses, were employed to test the study’s hypotheses at a 5% level of significance. Findings revealed that AI tools significantly enhance entrepreneurship education by improving students’ understanding of business concepts, fostering innovative thinking, and strengthening communication and writing skills. However, challenges such as poor digital infrastructure, high data costs, limited awareness, and inadequate institutional support were identified as major barriers to effective AI integration. The study concludes that while AI technologies hold transformative potential for entrepreneurship education, their effective utilization requires strategic curriculum integration, digital capacity building, and infrastructural investment. It recommends that universities incorporate AI literacy into entrepreneurship programs, provide training for educators, and create enabling environments for students to explore AI tools responsibly and productively.

This study explores the extent to which AI-driven tools such as machine learning, natural language processing, and data analytics enhance auditors’ ability to detect anomalies, assess risks, and provide deeper insights into financial statements. AI’s capacity to process vast datasets in real time reduces human error, strengthens fraud detection, and enables auditors to focus on judgment-intensive tasks, thereby improving audit quality. Moreover, automation of repetitive audit procedures accelerates workflow, minimizes costs, and enhances overall efficiency. However, the adoption of AI also raises concerns about data security, auditor independence, ethical implications, and the need for continuous skill development. This paper argues that while AI does not replace professional skepticism and human judgment, it serves as a powerful enabler that reshapes auditing practices toward greater reliability, transparency, and efficiency. The findings contribute to ongoing debates on the future of auditing and provide practical insights for regulators, practitioners, and stakeholders.

This research explores the application of Artificial Intelligence (AI) and Machine Learning (ML) for the predictive maintenance of transmission lines, specifically targeting fault detection, failure prediction, and maintenance optimization. Synthetic data was used to simulate parameters such as current, voltage, and temperature. Data preprocessing techniques, including cleaning and normalization, were performed. A supervised learning approach, the Random Forest Classifier, was applied using Python to mimic real-world fault scenarios. Model performance was evaluated using standard metrics: accuracy, precision, recall, and F1-score.The findings demonstrate that AI-based predictive maintenance has the potential to improve power system reliability and efficiency by reducing downtime and optimizing maintenance scheduling. The study also addresses key challenges, such as data availability and model generalization, proposing solutions like data augmentation and hybrid model design. Ultimately, this research provides a framework for developing scalable, data-driven predictive maintenance systems, advancing smart grid
technologies and sustainable power system management.

Cloud computing has revolutionized the way businesses manage their IT infrastructure, offering scalable, cost-effective, and flexible solutions. However, as organizations migrate to the cloud, cybersecurity becomes a critical concern. This paper explores how cloud computing enhances cybersecurity for businesses by leveraging advanced security mechanisms such as encryption, multi-factor authentication, artificial intelligence (AI)-driven threat detection, and automated compliance management. Cloud service providers (CSPs) offer robust security frameworks, including real-time monitoring, distributed denial-of service (DDoS) protection, and secure access controls, reducing the risk of cyber threats. Additionally, cloud-based security facilitates disaster recovery, data loss prevention, and regulatory compliance, strengthening overall business resilience. While cloud computing introduces new security challenges, implementing best practices and leveraging CSP security measures can significantly enhance an organization's cybersecurity posture. This study highlights the benefits, challenges, and future trends of cloud computing in
securing business operations against evolving cyber threats. To this purpose, this project designs and implements an encryption and multi-factor authentication system for cloud computing environments using a two-factor authentication approach: first-factor authentication via user ID/email and password, and second-factor authentication via OTP sent to user email.
The system is developed using HTML, CSS, JavaScript, and VueJS for the front-end, Laravel and PHP for the backend, and MySQL for the database.

This research explores the application of Artificial Intelligence (AI) and Machine Learning (ML) for the predictive maintenance of transmission lines, specifically targeting fault detection, failure prediction, and maintenance optimization.
Synthetic data was used to simulate parameters such as current, voltage, and temperature. Data preprocessing techniques, including cleaning and normalization, were performed. A supervised learning approach, the Random Forest Classifier, was
applied using Python to mimic real-world fault scenarios. Model performance was evaluated using standard metrics: accuracy, precision, recall, and F1-score.The findings demonstrate that AI-based predictive maintenance has the potential to improve power system reliability and efficiency by reducing downtime and optimizing maintenance scheduling. The study also addresses key challenges, such as data availability and model generalization, proposing solutions like data augmentation and
hybrid model design. Ultimately, this research provides a framework for developing scalable, data-driven predictive maintenance systems, advancing smart grid technologies and sustainable power system management

The board objective of this study is to examine the role of artificial intelligence on firm financial performance. Specifically, this study investigated the effects chatbot applications, robotic process automation and AI application on firm financial performance. The study used a primary data collected from 50 employees of the commercial banks within Ugbowo, Benin city, Edo State. Various statistical and econometric tool were applied to analyze the data. The results revealed that chatbot applications have a positive and statistically significant impact on organization performance. Robotic process automation and AI application in decision making have a positive but statistically insignificant impact on organization performance Based on the findings, the study recommended that businesses should consider increasing their investment in chatbot technologies, Organizations should reassess the effectiveness of their RPA strategies and business should explore other AI areas like predictive analytics, customer insights, or process automation