Artificial Intelligence Systems to Instil Digital Acumen and Enhance Employability in Accounting Students: A Selection Model

Artificial Intelligence (AI) is rapidly transforming multiple sectors, with accounting education facing both opportunities and challenges in its integration. This paper presents a practical, transferable model for selecting AI systems that enhance digital acumen and employability among accounting students. The framework supports educators in identifying AI tools that prepare graduates for diverse professional contexts across private, public, and global job markets. Grounded in qualitative methodology and informed by the Technology, Pedagogy, and Content Knowledge (TPACK) framework, the study expands the discourse to include stakeholders such as regulatory bodies, accreditation councils, and employers. The proposed model comprises 13 interrelated criteria: industry alignment; cost and licencing; ease of use and training; curriculum fit (via TPACK); employability linkage; scalability and cloud compatibility; infrastructure adaptability; assessment structure changes; availability of external certification; lifelong learning support; diversity, equity, and inclusion; data privacy compliance; and ethical considerations. Responding to the diversity of AI systems in the labour market, the model highlights the importance of scalable AI skills and the simulation of AI-driven recruitment environments. Notably, it supports both paid and open-access tools, promoting equity in resource-constrained contexts. Using grounded theory, the study synthesised data from 30 secondary sources accessed via academic databases and search engines, achieving data saturation. Document and thematic analyses revealed dominant themes in AI tool selection, including cost, usability, alignment, and adaptability. The model encourages ethically sound, inclusive, and industry-aligned AI integration in accounting curricula. It serves as a structured, globally applicable guide for institutions, educators, and policymakers navigating the complexities of AI adoption in higher education.