An analysis of AI-enabled authentic assessment strategy using text mining techniques

The development and use of artificial intelligence (AI) tools have raised several concerns about the effectiveness of assessment practice. Authentic assessment has emerged as one of the key strategies to cope with the rapidly evolving technology. Nevertheless, prior studies argued that authentic assessment as a standalone instrument is insufficient to control the impact of AI. Thus, several studies called for the integration of AI in assessment design. However, it is unclear how academics can deploy the use of AI in authentic assessment. This paper aims to present AI-enabled authentic assessment strategies through evidence synthesis. Following the PRISMA guidelines, we collected 103 documents from multiple databases and utilised text mining techniques, including co-occurrence analysis and topic modelling, to synthesise the documents. The results showed that academic integrity, critical thinking, framework, integration of AI, and teaching and learning methods are the dominant themes across the analysis. Most importantly, our findings identified four pathways in which AI can be deployed. Therefore, we propose the DAD (Deployment options, AI role, and Deliverables) conceptual framework to organise knowledge and guide the future design of AI-enabled authentic assessment.