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Effect of AI Ethics Education on Ethical Decision-Making Skills among Accounting Education Students in Nigerian Universities

Abstract

The increasing integration of artificial intelligence (AI) into accounting, auditing, financial reporting, data analysis, and educational processes has created new ethical challenges for accounting professionals and future accounting educators. AI technologies can support decision-making, automate accounting tasks, analyse financial information, and improve efficiency, but their use may also raise concerns relating to bias, privacy, data protection, transparency, accountability, misinformation, algorithmic errors, intellectual property, confidentiality, and inappropriate reliance on automated outputs. Accounting education students therefore require adequate understanding of AI ethics to enable them to recognize ethical risks and make responsible decisions when using AI in academic and professional accounting environments. However, limited exposure to structured AI ethics education may leave students inadequately prepared to identify and address ethical issues associated with AI-enabled accounting practices. Against this background, this study investigates the effect of AI ethics education on ethical decision-making skills among accounting education students in Nigerian universities. The study will be anchored on Ethical Decision-Making Theory, Technology Acceptance Model (TAM), and Social Cognitive Theory. Ethical Decision-Making Theory explains how individuals identify ethical issues, evaluate alternative courses of action, make ethical judgements, and select appropriate responses when faced with ethical dilemmas. The Technology Acceptance Model explains how students' perceptions of the usefulness and ease of use of AI technologies may influence their willingness to adopt and apply AI while considering appropriate ethical safeguards. Social Cognitive Theory emphasizes observational learning, self-efficacy, behavioural modelling, reinforcement, and environmental influences in shaping students' ethical attitudes and behaviours. Collectively, these theoretical perspectives provide a suitable framework for explaining how AI ethics education may influence ethical decision-making skills among accounting education students in Nigerian universities. 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 education students. AI ethics education will be assessed using indicators such as exposure to AI ethics instruction, frequency and duration of training, AI bias, algorithmic fairness, data privacy, confidentiality, cybersecurity, transparency, accountability, explainability, misinformation, AI-generated errors, intellectual property, plagiarism, academic integrity, responsible AI use, professional ethics, human oversight, and ethical risk management. Ethical decision-making skills will be assessed using indicators such as recognition of ethical issues, identification of stakeholders, evaluation of ethical alternatives, application of professional accounting principles, consideration of consequences, confidentiality decisions, assessment of AI-related risks, detection of biased or misleading AI outputs, professional judgement, ethical reasoning, justification of decisions, and selection of appropriate courses of action in AI-related accounting scenarios. Data will be collected using structured questionnaires, standardized ethical decision-making assessment tools, AI ethics knowledge tests, scenario-based ethical dilemmas, case studies, 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 technologies, previous AI ethics education, ethical knowledge, and ethical decision-making 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 ethics education on ethical decision-making skills. Where a quasi-experimental design is adopted, ethical decision-making scores before and after the 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 AI ethics education has a significant positive effect on ethical decision-making skills among accounting education students in Nigerian universities. Students exposed to structured, practical, and accounting-specific AI ethics education are expected to demonstrate stronger abilities to identify AI-related ethical issues, evaluate competing alternatives, recognize bias, protect confidential information, assess the reliability of AI-generated outputs, and apply professional ethical principles than students with limited exposure. Practical case studies involving AI-assisted financial reporting, auditing, accounting analysis, data processing, and academic activities may help students understand how ethical principles can be applied to realistic situations. AI ethics education may also strengthen students' awareness of human oversight and encourage them to verify AI-generated information before relying on it for professional or academic decisions. However, inadequate lecturer expertise, limited access to current AI ethics resources, rapidly changing AI technologies, weak institutional policies, limited practical exposure, and students' excessive reliance on AI-generated outputs may reduce the effectiveness of AI ethics education. The study therefore expects practical, current, ethically grounded, and professionally relevant AI ethics education to contribute significantly to improved ethical decision-making skills among accounting education students in Nigerian universities. The study is expected to contribute to the literature on AI ethics education, ethical decision-making, accounting education, artificial intelligence, professional ethics, accounting information systems, digital ethics, responsible AI, 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, technology specialists, employers, policymakers, and other stakeholders regarding strategies for developing ethically competent accounting graduates. The study will also provide evidence-based recommendations for integrating AI ethics into accounting education curricula, strengthening students' understanding of responsible AI use, incorporating AI-related ethical case studies into accounting courses, training accounting educators in AI ethics, developing institutional guidelines for ethical AI use, strengthening awareness of data privacy and confidentiality, and ensuring that future accounting educators and professionals can apply sound ethical judgement when using AI-enabled technologies in Nigeria.

Keywords: AI ethics education, ethical decision-making skills, accounting education students, artificial intelligence, accounting education, professional ethics, responsible AI, digital ethics, AI literacy, accounting information systems, Nigerian universities, Nigeria.