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Effect of AI-Generated Accounting Case Studies on Students’ Financial Reporting Skills in Nigerian Universities

Abstract

The increasing use of artificial intelligence (AI) in higher education has created new opportunities for developing innovative and interactive learning resources, including AI-generated accounting case studies. Generative AI systems can produce customized accounting scenarios involving financial transactions, reporting challenges, accounting adjustments, disclosure requirements, ethical dilemmas, and financial statement analysis. Such case studies may provide accounting students with diverse and realistic practice situations, immediate learning opportunities, and exposure to complex financial reporting problems. However, the effectiveness of AI-generated educational content depends on its accuracy, relevance, quality, and appropriate integration into teaching and learning. In Nigerian universities, where accounting education continues to adapt to digital transformation, AI-generated case studies may provide an additional approach to strengthening students' practical financial reporting competencies. Against this background, this study investigates the effect of AI-generated accounting case studies on students' financial reporting skills in Nigerian universities. The study will be anchored on Constructivist Learning Theory, Experiential Learning Theory, and the Technology Acceptance Model (TAM). Constructivist Learning Theory emphasizes active participation, problem-solving, and the construction of knowledge through meaningful learning experiences, providing a basis for examining how AI-generated case studies may support students' understanding of financial reporting concepts. Experiential Learning Theory emphasizes learning through practical experience, reflection, application, and evaluation, making it suitable for examining how repeated engagement with AI-generated accounting cases may develop financial reporting skills. The Technology Acceptance Model explains how students' perceived usefulness and perceived ease of use of AI-generated learning resources may influence their willingness to engage with such materials. Collectively, these theoretical perspectives provide a suitable framework for explaining how AI-generated accounting case studies may influence students' financial reporting skills. The study will adopt a quantitative quasi-experimental or analytical cross-sectional research design. The study population will comprise undergraduate and postgraduate accounting education students enrolled in selected public and private universities across Nigeria. A multistage sampling technique will be used to select geopolitical zones, states, universities, faculties or departments, levels of study, and eligible accounting students. AI-generated accounting case studies will be assessed using indicators such as frequency of exposure, relevance of case scenarios, diversity of accounting problems, level of difficulty, customization of cases to students' learning needs, financial reporting scenarios, accounting adjustments, disclosure issues, financial statement preparation, interpretation of accounting information, ethical reporting dilemmas, feedback generated through AI-supported cases, and students' ability to evaluate the accuracy of AI-generated information. Financial reporting skills will be assessed using indicators such as preparation of financial statements, application of accounting standards and principles, accounting adjustments, classification of transactions, recognition and measurement, presentation and disclosure, error identification and correction, interpretation of financial information, analysis of financial statements, treatment of complex reporting transactions, professional judgement, and ability to communicate financial reporting conclusions. Data will be collected using structured questionnaires, standardized financial reporting skills tests, AI-generated accounting case-study exercises, practical financial statement preparation tasks, scenario-based questions, case analysis assessments, and pre-test and post-test assessments where a quasi-experimental intervention is adopted. Descriptive statistics will be used to summarize students' demographic and academic characteristics, exposure to AI-generated case studies, patterns of AI use, and levels of financial reporting skills. Inferential statistical techniques, including chi-square tests, paired and independent t-tests, correlation analysis, and multiple regression analysis where appropriate, will be used to determine the effect of AI-generated accounting case studies on students' financial reporting skills. Where a quasi-experimental design is adopted, financial reporting skill scores before and after exposure to AI-generated case studies may be compared with those of a comparison group to determine changes associated with the intervention. Diagnostic tests will also be conducted to assess the reliability, validity, and robustness of the findings. The study is expected to find that AI-generated accounting case studies have a significant positive effect on students' financial reporting skills in Nigerian universities. Students exposed to well-designed and properly supervised AI-generated case studies are expected to demonstrate stronger abilities in financial statement preparation, accounting adjustments, application of reporting principles, disclosure, error detection, financial statement interpretation, and professional judgement than students without comparable exposure. AI-generated cases may provide students with a wider range of accounting situations and allow instructors to adjust case complexity according to students' learning needs. Repeated engagement with different reporting scenarios may also improve students' ability to analyse accounting information, identify reporting problems, evaluate alternative treatments, and communicate appropriate financial reporting conclusions. However, inaccurate AI-generated information, inappropriate accounting treatments, outdated reporting requirements, excessive dependence on AI-generated solutions, inadequate lecturer supervision, and limited students' ability to critically evaluate AI outputs may reduce the effectiveness of AI-generated case studies. The study therefore expects accurate, professionally reviewed, appropriately designed, and lecturer-guided AI-generated accounting case studies to contribute significantly to improved financial reporting skills among accounting education students in Nigerian universities. The study is expected to contribute to the literature on AI-generated educational content, accounting case studies, financial reporting skills, accounting education, artificial intelligence in education, digital learning, financial reporting education, educational technology, and higher education in Nigeria. The findings will provide useful information to the National Universities Commission, universities, faculties of education and management sciences, accounting education departments, accounting educators, professional accounting bodies, curriculum developers, educational technology providers, employers, and policymakers regarding responsible use of generative AI in accounting education. The study will also provide evidence-based recommendations for integrating AI-generated accounting case studies into financial reporting courses, establishing procedures for reviewing and validating AI-generated educational content, training accounting educators in effective AI-supported case development, encouraging students to critically evaluate AI-generated information, incorporating diverse and locally relevant Nigerian accounting scenarios, and ensuring that AI-supported case learning strengthens rather than replaces students' independent financial reporting skills and professional judgement.

Keywords: AI-generated accounting case studies, financial reporting skills, accounting education students, artificial intelligence, accounting education, financial reporting, generative AI, digital learning, educational technology, accounting case studies, Nigerian universities, Nigeria.