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Effect of Generative Artificial Intelligence Education on Accounting Students’ Analytical Skills in Nigerian Universities

Abstract

The rapid emergence of generative artificial intelligence (GenAI) has introduced new opportunities for teaching and learning in higher education and has increasingly influenced the skills required in the accounting profession. Generative AI applications can support accounting students through automated explanations, data interpretation, content generation, problem-solving assistance, research support, scenario analysis, and feedback on academic tasks. When appropriately integrated into accounting education, these technologies may provide students with opportunities to examine accounting information from different perspectives and develop analytical approaches to complex problems. However, uncritical reliance on AI-generated outputs, inaccurate information, limited verification skills, and inappropriate academic use may undermine students' independent analytical abilities. In Nigerian universities, the growing availability of generative AI creates a need for structured education that enables accounting students to use these technologies responsibly while maintaining critical and analytical thinking. Against this background, this study investigates the effect of generative artificial intelligence education on accounting students' analytical skills in Nigerian universities. The study will be anchored on the Technology Acceptance Model (TAM), Constructivist Learning Theory, and Social Cognitive Theory. The Technology Acceptance Model explains how students' perceived usefulness and perceived ease of use of generative AI technologies may influence their willingness to adopt and effectively utilize these tools for academic purposes. Constructivist Learning Theory emphasizes active learning, knowledge construction, problem-solving, and the integration of new information with existing knowledge, providing a basis for examining how guided GenAI education may support students' analytical development. Social Cognitive Theory emphasizes observational learning, self-efficacy, behavioural modelling, reinforcement, and environmental influences in shaping students' use of generative AI and development of analytical competencies. Collectively, these theoretical perspectives provide a suitable framework for explaining how generative AI education may influence accounting students' analytical skills. The study will adopt a quantitative quasi-experimental or analytical cross-sectional research design. The study population will comprise undergraduate and postgraduate accounting 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. Generative artificial intelligence education will be assessed using indicators such as exposure to GenAI training, frequency and duration of instruction, prompt development, AI-assisted accounting problem-solving, interpretation of AI-generated financial information, AI-supported research, scenario analysis, data interpretation, verification of AI-generated outputs, identification of AI errors, responsible AI use, academic integrity, data privacy, and ethical considerations. Analytical skills will be assessed using indicators such as critical thinking, data interpretation, financial analysis, problem identification, problem-solving, pattern recognition, anomaly detection, evaluation of alternatives, logical reasoning, evidence-based judgement, interpretation of accounting information, and ability to draw appropriate conclusions from financial and business data. Data will be collected using structured questionnaires, standardized analytical-skills assessment tools, accounting case studies, financial-data interpretation exercises, scenario-based tasks, GenAI knowledge 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 GenAI education, patterns of AI use, and analytical skill levels. 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 generative AI education on analytical skills. Where a quasi-experimental design is adopted, analytical-skill scores before and after the GenAI educational intervention 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 structured and responsible generative artificial intelligence education has a significant positive effect on accounting students' analytical skills in Nigerian universities. Students exposed to guided GenAI education are expected to demonstrate stronger abilities in interpreting accounting information, identifying problems, analysing financial data, evaluating alternatives, recognizing patterns and anomalies, verifying information, and making evidence-based judgements than students without comparable exposure. GenAI education may help students learn how to formulate effective prompts, compare alternative explanations, interrogate financial information, identify inconsistencies in AI-generated responses, and use AI outputs as supplementary information rather than unquestioned answers. Practical accounting case studies and scenario-based activities may further strengthen students' critical thinking and analytical reasoning. However, excessive dependence on generative AI, acceptance of inaccurate outputs, inadequate digital literacy, weak verification skills, academic misconduct, privacy concerns, and limited lecturer guidance may reduce the potential benefits of GenAI education. The study therefore expects practical, critical, ethical, and academically guided generative AI education to contribute significantly to improved analytical skills among accounting students in Nigerian universities. The study is expected to contribute to the literature on generative artificial intelligence education, analytical skills, accounting education, AI literacy, educational technology, accounting analytics, critical thinking, digital learning, and higher education in Nigeria. The findings will provide useful information to the National Universities Commission, universities, faculties of management sciences, accounting departments, accounting educators, professional accounting bodies, curriculum developers, educational technology specialists, employers, and policymakers regarding strategies for responsible integration of generative AI into accounting education. The study will also provide evidence-based recommendations for incorporating GenAI literacy into accounting curricula, strengthening students' ability to verify AI-generated information, training accounting lecturers in effective GenAI-supported teaching methods, promoting ethical and responsible AI use, developing practical accounting case studies involving generative AI, strengthening data privacy and academic-integrity guidelines, and ensuring that generative AI enhances rather than replaces the analytical and professional competencies of accounting students in Nigerian universities.

Keywords: Generative artificial intelligence education, analytical skills, accounting students, accounting education, generative AI, artificial intelligence, AI literacy, critical thinking, accounting analytics, educational technology, Nigerian universities, Nigeria.