Cross-country insights into students’ perceptions and applications of generative AI in accounting higher education

This mixed methods study integrates the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) with Self-Regulated Learning (SRL) theory to examine accounting and finance students’ perceptions and use of generative AI (GenAI) across three institutional settings in the United Kingdom, Australia, and Hungary. Drawing on survey data from 523 students collected in 2024, it employs nonparametric statistical analyses alongside thematic analysis using Leximancer 5.0 within a convergent parallel mixed methods design. Results show that students primarily use GenAI for research and idea generation, with significant cross-institutional variation in academic tasks. While 73% of students were at least moderately familiar with GenAI tools, only 24% reported engagement with institutional guidelines. Students recognise GenAI's employability benefits yet concerns about academic integrity and over-reliance persist across all contexts. Institutional guidance differed substantially, with the Hungarian institution reporting lower availability (mean = 0.42) than the Australian (mean = 1.38) and UK (mean = 1.57) institutions. Guideline availability is positively associated with students’ verification practices (γ = 0.272, p = .024). Qualitative findings underline educator-led GenAI integration to support critical thinking and ethical judgement. The study offers evidence-based insights to inform curriculum design, assessment practices, and institutional policy in accounting higher education.