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Effect of AI-Powered Personalized Learning on Financial Accounting Knowledge among Accounting Education Students in Nigerian Universities

Abstract

The increasing application of artificial intelligence (AI) in higher education has created new opportunities for personalized teaching and learning, particularly in disciplines that require continuous practice and individualized feedback such as accounting. AI-powered personalized learning systems can analyse students' learning patterns, identify areas of difficulty, provide customized learning materials, generate practice questions, offer immediate feedback, and adjust learning activities according to individual needs. In financial accounting education, these capabilities may help students understand complex concepts, practise accounting procedures, identify errors, and receive targeted academic support. However, limited access to AI-enabled learning technologies, inadequate digital infrastructure, insufficient lecturer training, and concerns about inappropriate reliance on AI may restrict the effective use of personalized learning systems in Nigerian universities. Against this background, this study investigates the effect of AI-powered personalized learning on financial accounting knowledge among accounting education students in Nigerian universities. The study will be anchored on the Technology Acceptance Model (TAM), Constructivist Learning Theory, and Adaptive Learning Theory. The Technology Acceptance Model explains how students' perceived usefulness and perceived ease of use of AI-powered learning technologies may influence their willingness to adopt and effectively utilize personalized learning systems. Constructivist Learning Theory emphasizes active participation, problem-solving, feedback, and knowledge construction through meaningful learning experiences. Adaptive Learning Theory provides a basis for explaining how instructional content, learning activities, practice questions, and feedback can be dynamically adjusted to students' individual learning needs, abilities, and progress. Collectively, these theoretical perspectives provide a suitable framework for explaining how AI-powered personalized learning may influence financial accounting knowledge 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-powered personalized learning will be assessed using indicators such as exposure to AI-enabled personalized learning platforms, frequency and duration of use, adaptive learning activities, personalized accounting content, individualized practice questions, automated feedback, AI-generated explanations, personalized revision exercises, progress tracking, identification of learning gaps, adaptive difficulty levels, individualized learning recommendations, interactive problem-solving, and students' ability to evaluate AI-generated learning information. Financial accounting knowledge will be assessed using indicators such as understanding of accounting principles, double-entry bookkeeping, journal entries, ledger accounts, trial balance preparation, bank reconciliation, adjustment entries, depreciation, inventory valuation, preparation and interpretation of financial statements, accounting concepts, error detection and correction, and application of accounting principles to practical financial accounting problems. Data will be collected using structured questionnaires, standardized financial accounting knowledge tests, practical accounting exercises, scenario-based questions, AI-assisted learning records where available, 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-powered personalized learning, learning patterns, and financial accounting knowledge 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 AI-powered personalized learning on financial accounting knowledge. Where a quasi-experimental design is adopted, financial accounting knowledge scores before and after the intervention may be compared with those of a comparison group to determine changes associated with the personalized learning approach. Diagnostic tests will also be conducted to assess the reliability, validity, and robustness of the findings. The study is expected to find that AI-powered personalized learning has a significant positive effect on financial accounting knowledge among accounting education students in Nigerian universities. Students exposed to personalized AI-supported learning are expected to demonstrate stronger understanding of financial accounting concepts, improved problem-solving ability, greater accuracy in accounting procedures, and better performance in accounting knowledge assessments than students receiving conventional learning without comparable personalization. AI-powered systems may help students address individual learning gaps by providing additional explanations and practice on difficult topics while allowing students to progress according to their learning needs. Immediate feedback may also help students identify and correct errors in journal entries, ledger accounts, trial balances, adjustments, and financial statement preparation. However, inadequate access to reliable internet services and digital devices, unreliable electricity, limited availability of appropriate AI learning platforms, insufficient lecturer training, inaccurate AI-generated explanations, data privacy concerns, and excessive student dependence on AI may reduce the effectiveness of personalized learning. The study therefore expects properly guided, accessible, reliable, and academically supervised AI-powered personalized learning to contribute significantly to improved financial accounting knowledge among accounting education students in Nigerian universities. The study is expected to contribute to the literature on AI-powered personalized learning, financial accounting knowledge, accounting education, artificial intelligence in education, adaptive learning, educational technology, digital learning, personalized education, 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 providers, curriculum developers, policymakers, and other stakeholders regarding strategies for integrating AI-supported personalized learning into accounting education. The study will also provide evidence-based recommendations for developing AI-enabled accounting learning platforms, training accounting educators in personalized digital teaching methods, improving students' access to reliable digital learning technologies, incorporating adaptive accounting exercises into financial accounting courses, establishing guidelines for responsible AI use, and ensuring that AI-powered personalized learning complements lecturer instruction while strengthening students' independent financial accounting knowledge and competencies.

Keywords: AI-powered personalized learning, financial accounting knowledge, accounting education students, artificial intelligence, adaptive learning, accounting education, educational technology, personalized learning, digital learning, financial accounting, Nigerian universities, Nigeria.