A critical review of AI in accounting education: Threat and opportunity

Joan Ballantine, Gordon Boyce and Greg Stoner

CRITICAL PERSPECTIVES ON ACCOUNTING, 2024, vol. 99, issue C

Abstract: In this essay, we contribute to the limited literature that has critically examined the potential implications of generative AI on the accounting academy and accounting education (AE). We argue that the recent accelerated growth of AI, especially large language models (LLMs) such as ChatGPT, raises significant issues and challenges that the accounting academy needs to urgently address to survive in the long term. Developments in AI have, we suggest, created a ‘change-inducing crisis’, presenting a unique opportunity for accounting academics to address the uncritical and problematic functionalist view of the discipline and the technical reductionism of accounting. Our arguments represent a call for action to embrace AI in learning and teaching practices in a way that brings about a renewed focus on the human dimension of accounting, incorporating broader social and critical perspectives, thereby addressing longstanding calls for change in AE to move beyond the technical, managerial, and financial focus (core of the AE curriculum) that has dominated the discipline for many decades. Accounting academics have a fundamental role to play in recognising the nature of the threats and the associated challenge of AI and to seize the opportunities available in ways that bring both critique and being critical to the fore. However, to bring about the sort of change we argue for in this essay, the accounting academy has to lead to ‘take education back from the market’ and provide the impetus that can make accounting education more relevant to our students and the needs of contemporary society.

Keywords: Artificial Intelligence; Accounting Education; Curricular change; Large language models (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:crpeac:v:99:y:2024:i:c:s1045235424000108

DOI: 10.1016/j.cpa.2024.102711

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