ReviewMedicine2026
From medical to pedagogical care: A bibliometric review of AI agent research in education.
Review in Medicine, 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe integration of artificial intelligence (AI) agents, including chatbots and virtual tutors, into education has expanded rapidly; however, systematic quantitative analysis of research trends in this field remains limited. Notably, the intellectual foundations of AI agent research lie not in education but in medicine, where AI agents were first extensively deployed and studied in clinical, experimental, and pedagogical contexts.
methodsIn this study, a bibliometric analysis of 377 records retrieved from the Web of Science was conducted using VOSviewer to map the evolution, thematic focus, and collaborative networks within this field.
resultsThe study showed AI agent research originated strongly in healthcare, with educational applications emerging more recently. Key research themes encompassed AI technologies, human-computer interaction, pedagogical applications, and ethical considerations. Collaborative efforts were led by the USA and China, supported by active European and Asian research hubs. The findings revealed a dual narrative of significant benefits for personalized learning and learner well-being alongside persistent challenges such as algorithmic bias and accessibility gaps.
conclusionTo realize the transformative potential of AI in education, future research should prioritize the development of adaptive algorithms, robust ethical frameworks, inclusive design, and interdisciplinary collaboration. By focusing on these areas, the field can work toward overcoming technical, ethical, and logistical barriers, thereby ensuring that AI acts as a sustainable force for equity and excellence in education.
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.