DOWNLOAD THE COMPLETE PROJECT

10 Users found this project useful

Effect of Artificial Intelligence Tools on Accounting Students’ Learning Outcomes in Nigerian Universities

Abstract

Artificial intelligence (AI) has increasingly influenced higher education by providing students with tools that can support information retrieval, problem-solving, personalized learning, content generation, data analysis, and academic feedback. In accounting education, AI tools such as generative AI applications, intelligent tutoring systems, automated problem-solving platforms, and AI-powered accounting software may provide students with opportunities to improve their understanding of accounting concepts and develop practical analytical skills. However, excessive dependence on AI-generated answers, inaccurate or misleading outputs, limited digital literacy, and inappropriate use of AI tools may negatively affect students' independent learning and critical-thinking abilities. In Nigerian universities, the increasing availability of AI tools presents both opportunities and challenges for accounting education. Against this background, this study investigates the effect of artificial intelligence tools on accounting students' learning outcomes 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 tools may influence their willingness to adopt and utilize AI technologies for learning. Constructivist Learning Theory emphasizes active participation, knowledge construction, problem-solving, and the integration of new knowledge with existing understanding, providing a basis for examining how AI-supported learning may influence accounting students' academic development. Social Cognitive Theory emphasizes observational learning, self-efficacy, interaction with learning environments, and behavioural influences in shaping students' use of AI tools and learning outcomes. Collectively, these theoretical perspectives provide a suitable framework for explaining the relationship between AI-tool use and learning outcomes in accounting education. The study will adopt a quantitative quasi-experimental or analytical cross-sectional research design. The study population will comprise undergraduate 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. Artificial intelligence tools will be assessed using indicators such as frequency of AI-tool use, duration of use, types of AI tools utilized, generative AI use, AI-powered accounting applications, intelligent tutoring systems, automated problem-solving tools, AI-supported research, personalized learning, AI-generated explanations, academic feedback, and students' ability to critically evaluate AI-generated information. Learning outcomes will be assessed using indicators such as accounting knowledge, examination performance, problem-solving ability, conceptual understanding, practical accounting skills, critical-thinking ability, retention of accounting concepts, and performance in accounting-related tasks. Data will be collected using structured questionnaires, AI-use assessment instruments, standardized accounting achievement tests, practical accounting exercises, students' academic performance records where ethically accessible, 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-tool use, preferred AI applications, and learning outcomes. 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 tools on accounting students' learning outcomes. Where a quasi-experimental design is adopted, students' learning outcomes before and after exposure to AI-supported 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 appropriate use of artificial intelligence tools has a significant positive effect on accounting students' learning outcomes in Nigerian universities. Students who use AI tools appropriately and under structured academic guidance are expected to demonstrate improved understanding of accounting concepts, problem-solving ability, academic performance, practical accounting skills, and retention of knowledge compared with students without comparable access to AI-supported learning. AI tools may provide immediate explanations, personalized learning support, opportunities for repeated practice, automated feedback, and assistance with complex accounting problems. They may also expose students to practical applications of accounting technologies and strengthen their digital competencies. However, excessive dependence on AI-generated responses, academic dishonesty, inaccurate AI outputs, reduced independent thinking, poor verification of information, and inadequate lecturer guidance may weaken the educational benefits of AI. The study therefore expects responsible, guided, and academically integrated use of AI tools to contribute significantly to improved learning outcomes among accounting students in Nigerian universities. The study is expected to contribute to the literature on artificial intelligence in education, accounting education, digital learning, educational technology, accounting students' academic performance, AI literacy, technology adoption, 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, lecturers, educational technology specialists, professional accounting bodies, policymakers, and curriculum developers regarding strategies for integrating AI into accounting education. The study will also provide evidence-based recommendations for developing institutional guidelines on responsible AI use, strengthening students' AI and digital literacy, training accounting lecturers in AI-supported teaching, integrating appropriate AI tools into accounting curricula, improving access to reliable educational technologies, promoting critical evaluation of AI-generated information, and ensuring that AI adoption enhances rather than replaces students' independent learning and professional accounting competencies in Nigerian universities.

Keywords: Artificial intelligence tools, accounting students, learning outcomes, accounting education, generative AI, digital learning, educational technology, academic performance, AI literacy, Nigerian universities, higher education, Nigeria.