Navigating Ethics in the AI Age: Perceptions of Academic Integrity in Accounting Education

This study investigates the relationship between generative artificial intelligence (AI) tool usage and perceptions of academic integrity in accounting education. It examines how frequency of AI adoption, perceived helpfulness, and efficiency gains influence students’ awareness of ethical standards, originality, proper attribution, and institutional responses to AI integration, while exploring implications for professional judgment and ethical reasoning in the discipline. A structured online questionnaire was distributed to accounting students and related respondents, yielding 102 valid responses after screening (92.73% effective rate). The instrument captured demographic variables (gender, age, education level), AI usage frequency, perceived assistance and efficiency, and multi-item measures of academic integrity perceptions. Data were analyzed using SPSS through reliability (Cronbach’s Alpha), validity (KMO and Bartlett’s tests), exploratory factor analysis (principal components with varimax rotation), descriptive statistics, Pearson correlations, and multiple OLS regression models with robustness checks via variable disaggregation. AI tool usage exhibits a significant negative association with academic integrity perceptions (= 0.450, p < 0.01), with higher frequency, helpfulness, and efficiency linked to diminished awareness of ethical boundaries, originality requirements, and attribution obligations. Gender (females higher) and age (older respondents higher) positively predict integrity cognition, while education level shows weaker or inconsistent effects. Factor analysis identifies two principal components explaining 66.094% of variance, separating AI usage dimensions from integrity attitudes. Robustness tests confirm the inverse relationship across alternative specifications. This research provides empirical evidence from a Chinese higher education sample on the inverse link between generative AI reliance and academic integrity perceptions in accounting, integrating Neutralization Theory and the Technology Acceptance Model to explain rationalization mechanisms and adoption drivers. By quantifying ethical erosion amid efficiency gains and highlighting demographic moderators, it offers actionable insights for accounting educators and institutions to design targeted ethics training, attribution policies, and balanced AI integration strategies that safeguard professional integrity while leveraging technological benefits in a rapidly evolving field. © 2027 selection and editorial matter, Md Jahidur Rahman, Tarek Rana and Hongtao Zhu; individual chapters, the contributors.