Integrating data analytics in teaching audit with machine learning and artificial intelligence

External audit is undergoing rapid changes where more and more routine tasks are automated with analytics and artificial intelligence (AI) instruments. The paper addresses a research problem of mapping data analytics to audit tasks and develops a framework aligning audit phases and AI and using data analytics in teaching audit with AI. The paper contributes to the literature on using data analytics with AI in knowledge specific areas and particularly critical for emerging audit analytics, which is data analytics in external financial audit application. The paper employs the process model methodology (Wynn and Clarkson, Research in Engineering Design 29:161–202, 2018) and the hybrid approach of curriculum development (Dzuranin et al., Journal of Accounting Education 43:24–39, 2018). The framework is extended further by inclusion of knowledge areas and skills recommendations for each identified stage. This inclusion is linked to the peak accounting body guidelines to ensure compliance with course certification and future job prospects. The developed framework is implemented using audit management platform MindBridge AI. The developed teaching and learning materials show implementation of the framework on the practical level. The developed framework was evaluated in a focus group with accounting academics and industry professionals. Its implementation was evaluated in a series of workshops and a survey with participants.