SynthesisFrontiers in artificial intelligence2026
Quality assurance in generative AI-mediated education: a bibliometric and scoping review.
Synthesis in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.