ReviewInternational journal of dentistry2026
Personalized Learning With Artificial Intelligence in Dental Education: An Integrative Review.
Review in International journal of dentistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effectiveness of artificial intelligence-driven simulation in dental education: a systematic review and meta-analysis of learning outcomes.BMC medical education · 2026Pooled it
- Large language models and generative artificial intelligence in endodontics: a scoping review.Odontology · 2026Review
- The landscape of virtual simulation in undergraduate dental education with implications for artificial intelligence incorporation.NPJ digital medicine · 2026Article
- Personalized Learning With Artificial Intelligence in Dental Education: An Integrative Review.International journal of dentistry · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Dental education is at the pinnacle of innovation and evolution. Advancements in artificial intelligence (AI) have reinforced personalized dental education to learners' preferences and needs to emerge as a game changer. Conventional dental education has rigid and standardized methods of learning that are inconvenient to diverse learning preferences. With the aid of AI, the learning process can be personalized. This integrative review discusses the various AI tool driving change in dental education to provide a tailor-made learning experience for developing new knowledge that can inform practice and policy. Methods: The search strategy involved looking for keywords, "personalized learning" OR "adaptive learning" AND "artificial intelligence" AND "dental education" across databases. The results obtained were subject to review and the various AI tools used in dental education for personalizing learning process are discussed. Results: The keyword search across databases yielded 920 studies. After the 355 duplicates were removed 565 studies remained. The title and abstract screening were conducted; 377 studies were excluded resulting in 188 studies, 175 studies not complying with the inclusion criteria, resulting in 13 studies of which six were included as other studies did not specify on dental education, virtual simulation, augmented reality (AR) without inculcating AI technologies. The studies reviewed showed numerous tools such as virtual reality (VR) and AR being utilized for interactive and immersive engagement of learners. Conclusion: This review highlights both the pros and cons of AI-driven personalized dental education and analyses prospects for improvement such as multistakeholder collaboration to create adaptive and student-centered learning experience. Personalized dental education driven by AI can serve as a game changer in dental education by integrating technology for enhanced learning and development of policies for proper adaptation of such systems in personalized dental education.
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.