Trial reportBMC medical education2025
Application of DeepSeek-based AI teaching assistant in teaching anesthesiology theories.
Trial report in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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
5 citing papers in PubMed.
- Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation.Molecular biomedicine · 2026Review
- Elucidating the transformative role of large language models in advancing anesthesiology education.The Journal of international medical research · 2026Review
- Learning ophthalmic anatomy with AI-generated visual resource: the moderating role of educational background.Frontiers in medicine · 2026Article
- Generative artificial intelligence in medical education: from knowledge assessment to clinical reasoning and professional competence.Frontiers in medicine · 2026Review
- Application of the artificial intelligence-assisted World Café teaching model in clinical pharmacology graduate course: a pilot study.Frontiers in public health · 2026Article
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
Abstract
backgroundMedical education, particularly in specialized fields like anesthesiology, faces challenges of extensive knowledge systems and low teaching efficiency. Traditional teaching methods often fail to meet individualized learning needs and effectively assess students’ mastery of key learning points. Anesthesiology education is further complicated by its broad learning points and densely constructed knowledge systems, yet it occupies a small portion of undergraduate medical curricula. Recent advancements in generative artificial intelligence (AI) offer promising solutions to enhance medical education. This study explores the application of a DeepSeek-based AI teaching assistant in teaching anesthesiology theories.
methodsA single-center, randomized controlled trial was conducted with 48 participants, including 24 fourth-year medical students and 24 non-anesthesiology resident physicians. Participants were stratified by trainee type, then randomized 1:1 via a random-number table. Whereas the control group received traditional teaching (lectures, Q&A, clinical rotations); the experimental group received traditional teaching (same as the control group) plus 24/7 online support, personalized learning plans, and real-time performance analysis from the AI assistant.
resultsThe experimental group achieved significantly higher theoretical test scores immediately after the course compared to the control group (P = 0.007). However, no significant difference was observed in knowledge retention one month later (P = 0.277). Student satisfaction surveys showed significantly higher scores in learning engagement and responsiveness to questions for the experimental group (P < 0.05), but no significant differences in learning interest or satisfaction.
conclusionsThe DeepSeek-based AI teaching assistant enhanced short-term knowledge acquisition and learning engagement in anesthesiology education. However, long-term knowledge retention and overall satisfaction were not significantly improved. Future studies with larger sample sizes and longer observation periods are needed to further validate the effectiveness of AI teaching assistants in medical education and explore their potential in promoting clinical skill training.
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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.