ARTIFICIAL INTELLIGENCE

The rapid development of artificial intelligence (AI), especially generative technologies that can create literary, artistic, musical, and audiovisual works with minimal or no human involvement, has posed significant challenges to the fundamental principles of copyright law. Traditional copyright frameworks are based on human creativity, originality, and identifiable authorship. However, the rise of AI-generated content in the digital age or era has muddled these concepts and introduced complex legal dilemmas surrounding copyright validity, authorship attribution, and ownership rights. These issues are increasingly pertinent worldwide and are particularly critical in Nigeria, where the digital innovation landscape is rapidly evolving alongside the implementation of the Copyright Act 2022, which does not explicitly address fully autonomous AI-generated works. This dissertation aims to explore the legal framework governing copyright ownership of AIgenerated content in the digital age, with a distinct emphasis on the Nigerian legal context. The research evaluates whether existing provisions under the Nigerian Copyright Act 2022 sufficiently tackle problems related to authorship, originality, and ownership regarding AI-generated works. The study's objectives include uncovering conceptual and doctrinal deficiencies in the current legal structure, assessing the operational efficacy of copyright protection and enforcement mechanisms, and analyzing the broader legal, socio-economic, and policy implications of creativity driven by AI. Additionally, the research intends to glean insights from international and comparative legal systems to suggest potential strategies for future legal and policy reforms in Nigeria. To meet these aims, the research employs a doctrinal legal methodology that involves a comprehensive analysis of primary legal sources, which include international copyright treaties, regional agreements, national laws, and relevant judicial rulings. It scrutinizes key international agreements like the Berne Convention, the TRIPS Agreement, and the WIPO Internet Treaties to outline the global normative standards for copyright protection, while also critically assessing the Nigerian Copyright Act 2022 as the chief or main domestic legal text. Furthermore, secondary sources such as academic books, peer-reviewed journal articles, policy documents, and reports from international and regional organizations are utilized to provide scholarly and contextual insights on the matter. The study additionally adopts a comparative legal perspective by looking at how various jurisdictions, including the United States, the United Kingdom, the European Union, and certain Asian and African nations, have tackled the copyright challenges associated with AI-generated content. This comparative examination facilitates an assessment of the diverse legal responses to authorship and ownership of AI-generated works and helps identify best practices that could be relevant to Nigeria. An analytical and evaluative method is employed to assess the sufficiency of the current legal and institutional frameworks, particularly in relation to enforcement issues in the digital landscape. In summary, the dissertation offers a thorough legal analysis of AI-generated content and copyright ownership, concentrating on the effectiveness of current legal frameworks and methodologies. It establishes a groundwork for informed legal reform by identifying critical issues and providing insights aimed at achieving a balanced copyright system that fosters technological innovation while protecting intellectual property rights in the digital age.

This study investigates the influence of Artificial Intelligence (AI) on library services, focusing specifically on academic libraries. With the rapid advancement of technology, libraries are increasingly adopting AI tools to improve their operations and enhance the user experience. The research centers on the John Harris Library at the University of Benin, exploring the current state and extent of AI integration in its services. AI technologies, such as machine learning, natural language processing, and data analytics, are transforming traditional library functions by automating routine tasks like cataloging and organizing resources. This allows librarians to focus on more complex intellectual work. Additionally, AI enables personalized services through analyzing user behavior and preferences, improving information retrieval and recommendations. The study also highlights AI’s role in digitizing and preserving rare and fragile materials, thus safeguarding cultural heritage for future generations. However, alongside these benefits, the research addresses important challenges including data privacy concerns, ethical implications, potential job displacement among library staff, and the need for ongoing professional training. The findings emphasize the necessity of responsible AI adoption to balance technological progress with ethical responsibilities, ensuring that library services remain effective, inclusive, and trustworthy. This study contributes valuable insights for library professionals, academic institutions, and policymakers aiming to harness AI’s potential while managing its risks within the library setting

This study focused on the uses and abuses of Artificial Intelligence (AI) tools among final year students in Faculty of Agriculture, University of Benin, Edo State, Nigeria. The specific objectives were to: describe the socioeconomic characteristics of final year students in faculty of agriculture; identify the AI tools that the respondents were aware of; identify the interest in the use of AI tools among the respondents; ascertain the use and frequency of use of AI tools among the respondents; identify the purpose of use of AI tools by the respondents; identify the perceived abuses of AI tools by the respondents and examine the constraints in the use of AI tools by the respondents. A multi-stage sampling procedure was used for a simple random sampling of 145 final year students in faculty of agriculture for the study. Primary data were collected through the use of structured questionnaire. Collected data were analyzed using descriptive statistics such as: frequency counts, simple percentages and mean scores, as well as inferential statistics such as Pearson Product Moment Correlation (PPMC). Results showed that more than half (64.1%) of the respondents were female with a mean 23 years. Most (99.3%) of the students were single. The result showed that most (90.3%) of the final year students were aware of ChatGPT, with more than half (61.4%) of the students showing interest in using it (ChatGPT). Most (99.3%) of the final-year students used ChatGPT and also indicated daily usage. The result showed that most 14 (89.7%) of the final-year students used artificial intelligence (AI) tools for writing assignments. The results also showed that the most significant perceived abuse of artificial intelligence tools by final year students was use of AI tools to answer test or examination questions (x̄=3.46) and the most significant constraints encountered was the requirement for paid subscriptions for most AI tools (x̄= 3.75). It was concluded that most widely used AI tool by the final year students was ChatGPT, while the use of AI tools to answer test or examination questions was identified as the most significant perceived abuse of AI tools by the respondents. The study therefore recommends that the faculty strengthen academic integrity measures and sensitize students on the ethical use of AI tools, especially discouraging their use for tests and examinations.

This study investigated the awareness, perceptions, and attitudes of Computer Education lecturers toward Artificial Intelligence (AI) at the University of Benin, Benin City, Edo State. To achieve this, four research questions were formulated to guide the research process. A descriptive survey research design was adopted for the study. A structured questionnaire titled Awareness, Perceptions, and Attitudes of Computer Education Lecturers Toward Artificial Intelligence was used to collect data. Descriptive statistics such as mean, frequency, and percentage were used to answer the research questions, while inferential statistics, including the Independent Samples t-test and One-Way ANOVA, were used to test the hypotheses at the 0.05 level of significance.

This study examines the impact of artificial intelligence (AI) technologies on audit efficiency, with a specific focus on selected professional organizations in Benin City, Nigeria. Employing a survey research design, the study gathered data from 50 respondents across various industries using structured questionnaires. The analysis utilized both correlation and linear regression techniques to assess the relationship between AI components—Machine Learning, Natural Language Processing (NLP), Robotic Process Automation (RPA), and Predictive Analytics—and audit efficiency. The findings reveal that Machine Learning and Predictive Analytics significantly enhance audit efficiency, as evidenced by their strong positive correlations and statistically significant regression coefficients. These technologies contribute to improved financial reporting accuracy, enhanced fraud detection, and reduced audit risks. Conversely, NLP and RPA did not show statistically significant effects, suggesting that their integration into audit workflows may be limited or at a developmental stage