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Effect of AI-Based Accounting Feedback on Students’ Ability to Correct Financial Reporting Errors in Nigerian Universities

Abstract

The increasing integration of artificial intelligence (AI) into higher education has created new opportunities for improving accounting instruction through intelligent and personalized feedback systems. AI-based accounting feedback can provide students with immediate responses to accounting exercises, identify potential errors, explain incorrect treatments, suggest areas for review, and support repeated practice. In financial reporting education, timely and targeted feedback may help students identify and correct errors relating to recognition, measurement, classification, presentation, adjustment entries, and disclosure. In Nigerian universities, where large class sizes and limited lecturer-student interaction may restrict the provision of individualized feedback, AI-based feedback systems may provide an additional mechanism for supporting students' learning. However, concerns regarding inaccurate AI-generated feedback, students' overreliance on AI, limited lecturer supervision, inadequate digital infrastructure, and students' varying levels of AI literacy may affect the effectiveness of AI-supported accounting learning. Against this background, this study investigates the effect of AI-based accounting feedback on students' ability to correct financial reporting errors in Nigerian universities. The study will be anchored on Constructivist Learning Theory, Feedback Intervention Theory, and the Technology Acceptance Model (TAM). Constructivist Learning Theory emphasizes active learning, problem-solving, reflection, and knowledge construction through meaningful learning experiences, providing a basis for examining how AI-supported feedback may help students develop deeper understanding of financial reporting concepts. Feedback Intervention Theory explains how information about students' performance can direct attention toward errors, task requirements, and appropriate corrective actions, thereby supporting improved performance. The Technology Acceptance Model explains how students' perceived usefulness and perceived ease of use of AI-based accounting feedback systems may influence their engagement with and acceptance of AI-supported learning. Collectively, these theoretical perspectives provide a suitable framework for explaining how AI-based accounting feedback may influence students' ability to correct financial reporting errors. 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-based accounting feedback will be assessed using indicators such as frequency of AI feedback, immediacy of feedback, identification of accounting errors, explanation of errors, suggestions for corrective action, feedback on accounting standards, recognition and measurement guidance, classification feedback, financial statement presentation feedback, disclosure-related feedback, personalized feedback, repeated practice opportunities, interactive explanations, and students' ability to evaluate the accuracy of AI-generated feedback. Students' ability to correct financial reporting errors will be assessed using indicators such as identification of errors, correction of journal entries, adjustment of accounting records, correction of recognition and measurement errors, reclassification of accounting items, correction of financial statement presentation, correction of disclosure errors, application of relevant accounting principles and standards, reconciliation of corrected balances, explanation of corrective procedures, and ability to distinguish between appropriate and inappropriate financial reporting treatments. Data will be collected using structured questionnaires, standardized financial reporting error-correction tests, AI-supported accounting exercises, scenario-based questions, financial reporting case studies, practical correction tasks, 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-based feedback, patterns of AI use, and levels of financial reporting error-correction ability. 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-based accounting feedback on students' ability to correct financial reporting errors. Where a quasi-experimental design is adopted, error-correction scores before and after exposure to AI-based accounting feedback 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-based accounting feedback has a significant positive effect on students' ability to correct financial reporting errors in Nigerian universities. Students exposed to accurate, structured, and lecturer-supervised AI-based accounting feedback are expected to demonstrate stronger abilities to identify, explain, and correct financial reporting errors than students without comparable AI-supported feedback. Immediate feedback may enable students to recognize mistakes in journal entries, accounting adjustments, recognition and measurement, classification, presentation, and disclosure before incorrect approaches become established. Repeated exposure to error-correction exercises may also improve students' analytical skills, professional judgement, accuracy, and confidence in applying financial reporting principles. However, inaccurate AI-generated responses, outdated accounting information, excessive dependence on AI-generated solutions, inadequate lecturer monitoring, limited digital infrastructure, and students' inability to critically evaluate AI feedback may reduce the effectiveness of the intervention. The study therefore expects accurate, current, interactive, and professionally supervised AI-based accounting feedback to contribute significantly to improved financial reporting error-correction skills among accounting education students in Nigerian universities. The study is expected to contribute to the literature on AI-based accounting feedback, financial reporting error correction, accounting education, artificial intelligence in education, intelligent tutoring systems, 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 and effective use of AI in accounting education. The study will also provide evidence-based recommendations for integrating AI-based feedback into financial reporting courses, establishing procedures for validating AI-generated accounting feedback, training accounting educators to supervise AI-supported learning, encouraging students to critically evaluate AI-generated responses, incorporating locally relevant Nigerian financial reporting cases, and ensuring that AI-based feedback strengthens rather than replaces students' independent analytical skills, professional judgement, and ability to correct financial reporting errors.

Keywords: AI-based accounting feedback, financial reporting errors, error-correction skills, accounting education students, artificial intelligence, financial reporting education, digital learning, educational technology, accounting education, intelligent feedback, Nigerian universities, Nigeria.