Artificial Intelligence and Accounting Research: A Framework and Agenda

TL;DR

This paper presents a framework for classifying AI-related accounting research, analyzes current studies, and discusses how AI advances like GenAI and LLMs impact research practices, opportunities, and education in accounting.

Contribution

It introduces a novel framework for categorizing AI-accounting research and explores how AI tools transform research workflows and strategic positioning for scholars.

Findings

AI research in accounting is classified along two dimensions: focus and methodology.

GenAI democratizes research capabilities but increases competition and expectations.

Reforming doctoral education is necessary to develop AI fluency and strategic expertise.

Abstract

Recent advances in artificial intelligence, particularly generative AI (GenAI) and large language models (LLMs), are fundamentally transforming accounting research, creating both opportunities and competitive threats for scholars. This paper proposes a framework that classifies AI-accounting research along two dimensions: research focus (accounting-centric versus AI-centric) and methodological approach (AI-based versus traditional methods). We apply this framework to papers from the IJAIS special issue and recent AI-accounting research published in leading accounting journals to map existing studies and identify research opportunities. Using this same framework, we analyze how accounting researchers can leverage their expertise through strategic positioning and collaboration, revealing where accounting scholars' strengths create the most value. We further examine how GenAI and LLMs transform the research process itself, comparing the capabilities of human researchers and AI agents across the entire research workflow. This analysis reveals that while GenAI democratizes certain research capabilities, it simultaneously intensifies competition by raising expectations for higher-order contributions where human judgment, creativity, and theoretical depth remain valuable. These shifts call for reforming doctoral education to cultivate comparative advantages while building AI fluency.