Work

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

Year: 2026

Type: article

Abstract: 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. Context and implications Rationale for this study: The emergence and increasing usage of AI technologies have challenged assessment practices in higher education. While a growing number of studies have proposed various solutions, including frameworks, they address a certain discipline or isolated element, such as authenticity or reflective practice. Why the new findings matter: Our results provide a more holistic and theoretically grounded assessment strategy addressing the alignment between AI integration, pedagogical design, and cognitive engagement to evidence learning in an assessment of learning context. Implications for practitioners, policymakers, and researchers: For authenticity, our result emphasises the need for academics, education practitioners, and policymakers to ensure assessment of learning policies is not against the use of AI. As AI continues to evolve, our result suggests that education practitioners need to ensure that assessment design allows the use of AI as both an agent and a tool to mirror ‘real‐world’ professional context without undermining cognitive engagement.

Source: Review of Education

DOI: 10.1002/rev3.70206

Authors: Bayode Ogunleye, Olakunle Olayinka, Kudirat Ibilola Zakariyyah, Oluwaseun Ajao, Hemlata Sharma

Institutions: University of Brighton, University of Sheffield, Manchester Metropolitan University

Language: English

Topic: Student Assessment and Feedback

Subfield: Education

Field: Social Sciences

Domain: Social Sciences

SDG: Quality education

Open Access status: hybrid

Estimated APC paid: $3,470