The AI Paradox in Accounting and Business Studies: Efficiency Versus Ethics

This study examines the ethical paradox of generative artificial intelligence (AI) in accounting education, focusing on the tension between efficiency gains and ethical challenges. It investigates patterns of AI adoption among students and faculty, identifies key moral concerns—including data privacy, academic integrity, diminished human interaction, bias, and educational equity—and explores attitudes toward responsible integration while proposing strategies to balance technological benefits with ethical safeguards. A mixed-methods survey approach was employed, with separate structured questionnaires distributed to students (n = 98, 49% response rate) and professors (n = 5) in accounting and business programs at a Sino-foreign university in South China. Instruments included Likert-scale items, multiple-choice questions, and open-ended prompts to capture usage patterns, perceived benefits, ethical concerns, and future expectations. Quantitative data were analyzed using descriptive statistics (frequencies, percentages), cross-tabulations, and visualization (pie charts, bar charts); qualitative responses were thematically coded to identify recurring perspectives and ethical dilemmas. Students exhibit high AI adoption (63.16% familiarity, 76.32% ChatGPT usage), primarily for essay writing (55.26%) and assignment support, driven by efficiency perceptions. Faculty show greater caution (60% non-users in teaching), prioritizing ethical oversight. Dominant concerns include data privacy (80% professors, 60.53% students), academic integrity (80% professors, 55.26% student usage), reduced human interaction (60% professors, 42.11% students), and fairness issues from unequal AI proficiency (42.11% students). Attitudes cluster into proactive adoption (efficiency-focused), critical engagement (acknowledging risks), and ethical opposition (favoring restrictions), with strong support for institutional guidelines. This research offers a dual-stakeholder (student-faculty) empirical analysis of generative AI’s ethical landscape in accounting education within a Chinese Sino-foreign university context. Grounded in the Technology Acceptance Model and Ethical Decision-Making Theory, it illuminates the efficiency-ethics tension, highlighting adoption disparities and moral awareness gaps. The findings provide actionable insights for institutions to develop balanced policies, training programs, and ethical frameworks that harness AI’s pedagogical advantages while preserving academic integrity, equity, and human-centered learning in a rapidly digitizing discipline. © 2027 selection and editorial matter, Md Jahidur Rahman, Tarek Rana and Hongtao Zhu; individual chapters, the contributors.