The integration of conversational AI has the potential to revolutionize assessment practices in higher education by offering innovative approaches to both evaluating student performance and supporting students in specific assessment tasks. This paper presents a systematic literature review on the use of conversational AI in assessment within higher education institutions. The review synthesizes current research findings, highlighting the trends, challenges, and perceived benefits of implementing conversational AI in assessment practices.

As a relatively new area of study, the application of conversational AI in educational assessments requires a comprehensive understanding of its current state and potential. A systematic literature review is essential to consolidate existing knowledge, identify gaps in the research, and provide a foundation for future studies. Key themes identified include the enhancement of formative assessments, the provision of real-time feedback, and the support of personalized evaluation pathways. Additionally, the review explores how conversational AI can assist students in completing specific assessment tasks, such as providing guidance, answering questions, and facilitating learning processes.

The review also addresses challenges such as technological barriers, resistance to change, and the need for professional development. By analyzing and summarizing the findings from various studies, this paper offers practical insights and recommendations for educators, researchers, and policymakers. The review sets the stage for future primary research, which will further explore the practical implementation and impact of conversational AI in higher education assessments.

Keywords: Conversational AI, Higher Education, Assessment, Systematic Literature Review, Educational Technology, Student Support.