Quality assurance in generative AI-mediated education: a bibliometric and scoping review

The rapid advancement of generative artificial intelligence has introduced transformative opportunities and critical challenges for quality assurance in educational settings. This study aims to systematically map the scientific landscape on quality assurance in education mediated by generative artificial intelligence, identifying predominant methodological approaches, conceptual frameworks, and emerging research gaps. A bibliometric and scoping review was conducted following PRISMA-ScR guidance and integrating descriptive bibliometric analysis with qualitative thematic mapping. The analysis was based on 482 documents retrieved from Scopus and Web of Science, covering the period 2022-2026. The data were analyzed using a combined approach that integrates descriptive bibliometric analysis and qualitative content analysis of the abstracts. The results show a predominance of review studies, indicating a phase of conceptual consolidation in the field. There is also an increase in quantitative and experimental studies, reflecting a transition toward empirical validation of the use of generative artificial intelligence in education. From a conceptual perspective, the literature is primarily oriented toward quality assessment and ethical implications, particularly concerning biases, transparency, and responsible use of AI. In contrast, pedagogical frameworks and academic integrity show limited development. These findings highlight a structural tension between technological innovation and the need to ensure educational quality, ethical governance, and academic integrity. The need to develop integrative frameworks that articulate pedagogical, technological, and normative dimensions is emphasized. This study contributes to the understanding of an emerging field and provides relevant inputs for researchers, educators, and educational policymakers interested in the responsible implementation of generative artificial intelligence.