The aim of this study is to contribute to the extant literature concerning the utilization of artificial intelligence in education, with a particular focus on its application in the context of assessments. This study combines a descriptive analysis and a bibliometric review to investigate trends in artificial intelligence (AI) related to education and assessment, with a view to delineating its evolving scope. The Web of Science Databases provided the articles for analysis (n = 761), spanning from 2020 to 2024. The objective of the present study is to address research questions across key domains, with a particular focus on identifying influential authors, leading contributing countries, prominent journals, major citation topics, highly cited articles, keyword co-occurrence patterns, as well as co-authorship, citation and institutions networks. This study emphasizes the importance of global collaboration, anticipates emerging technologies, and highlights pedagogical implications. Key research themes include the Technology Acceptance Model (TAM), pedagogical applications, academic integrity, and the use of AI in medical education. Topics such as “generative AI,” “large language models,” and “academic ethics” highlight both the opportunities and the challenges AI presents in educational contexts. The dominance of certain institutions and countries suggests a globally active, yet uneven, research landscape. Citation and co-authorship network analyses reveal a collaborative environment in which small but highly connected groups of researchers and institutions play central roles in advancing AI applications in assessment. Strengthening interdisciplinary and international collaborations could further enhance the field’s innovation and impact.