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Effect of AI-Assisted Learning on the Academic Performance of Accounting Education Students in Nigerian Universities

Abstract

The rapid development of artificial intelligence (AI) has created new opportunities for teaching and learning in higher education, including accounting education. AI-assisted learning tools such as generative AI applications, intelligent tutoring systems, automated feedback platforms, AI-powered research tools, personalized learning systems, and adaptive educational technologies can provide students with explanations, learning resources, practice questions, feedback, and individualized academic support. In accounting education, these technologies may help students understand complex accounting concepts, solve accounting problems, practise financial reporting, and receive immediate feedback on their learning activities. However, concerns regarding inaccurate AI-generated information, excessive dependence on AI, academic dishonesty, reduced independent learning, and inadequate guidance may limit the educational benefits of AI-assisted learning. In Nigerian universities, the increasing availability of AI tools creates both opportunities and challenges for accounting education. Against this background, this study investigates the effect of AI-assisted learning on the academic performance of accounting education students 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 AI-assisted learning technologies may influence their willingness to adopt and effectively use these tools for academic purposes. Constructivist Learning Theory emphasizes active participation, knowledge construction, problem-solving, and the integration of new information with existing knowledge, providing a basis for examining how AI-supported learning may enhance students' understanding of accounting concepts. Social Cognitive Theory emphasizes observational learning, self-efficacy, behavioural modelling, reinforcement, and interaction with the learning environment in shaping students' learning behaviour and academic outcomes. Collectively, these theoretical perspectives provide a suitable framework for explaining how AI-assisted learning may influence the academic performance of 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-assisted learning will be assessed using indicators such as frequency of AI-tool use, duration of use, types of AI learning tools utilized, generative AI applications, intelligent tutoring systems, AI-powered accounting learning platforms, automated feedback, personalized learning support, AI-generated explanations, practice-question generation, accounting problem-solving assistance, research support, revision activities, and students' ability to critically evaluate AI-generated information. Academic performance will be assessed using indicators such as examination scores, continuous assessment results, accounting achievement-test scores, assignment performance, practical accounting-task performance, conceptual understanding, problem-solving ability, knowledge retention, and performance in accounting education courses. Data will be collected using structured questionnaires, standardized accounting achievement tests, academic performance records where ethically accessible, AI-assisted learning assessment instruments, accounting problem-solving exercises, 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, patterns of AI use, preferred AI tools, learning activities, and academic performance. 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-assisted learning on academic performance. Where a quasi-experimental design is adopted, academic performance scores before and after exposure to AI-assisted learning 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 appropriately structured AI-assisted learning has a significant positive effect on the academic performance of accounting education students in Nigerian universities. Students exposed to guided and academically relevant AI-assisted learning are expected to demonstrate improved understanding of accounting concepts, problem-solving ability, knowledge retention, examination performance, and practical accounting-task performance compared with students without comparable exposure. AI tools may provide immediate explanations, personalized learning support, additional practice questions, automated feedback, and opportunities for students to revisit difficult accounting concepts at their own pace. AI-assisted learning may also help students develop digital learning competencies and improve their confidence in engaging with complex accounting topics. However, excessive dependence on AI-generated answers, inaccurate or fabricated information, academic misconduct, reduced critical thinking, poor verification of AI outputs, limited access to reliable technology, and inadequate lecturer guidance may reduce the effectiveness of AI-assisted learning. The study therefore expects responsible, guided, interactive, and academically integrated use of AI technologies to contribute significantly to improved academic performance among accounting education students in Nigerian universities. The study is expected to contribute to the literature on AI-assisted learning, artificial intelligence in education, accounting education, educational technology, academic performance, digital learning, AI literacy, 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, educational technology specialists, curriculum developers, policymakers, and other stakeholders regarding strategies for integrating AI into accounting education. The study will also provide evidence-based recommendations for developing institutional guidelines for responsible AI use, strengthening students' AI and digital literacy, training accounting educators in AI-supported teaching methods, integrating appropriate AI tools into accounting education curricula, promoting critical evaluation of AI-generated information, improving access to reliable digital learning technologies, and ensuring that AI-assisted learning enhances rather than replaces independent learning and academic development among accounting education students in Nigerian universities.

Keywords: AI-assisted learning, academic performance, accounting education students, artificial intelligence, accounting education, educational technology, digital learning, AI literacy, academic achievement, Nigerian universities, higher education, Nigeria.