Agentic education · Learning science

Actionable companion · Motivation & engagement

Learning with agency: Personalized Learning With Artificial Intelligence in Dental Education: An Integrative Review

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Age / stage not established. Health-professions learning must be distinguished from K–12 instruction.

Original authors’ abstract, reproduced under the linked source license:

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.

What kind of evidence is this?

Intelligent tutoring & adaptive learning · Evidence synthesis

Stage: Age / stage not established. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

The search strategy involved the use of following keywords along with Boolean operators: (“personalized learning” OR “adaptive learning”) AND (“artificial intelligence”) AND (“dental education”). The databases PubMed, Google Scholar, Scopus and Web of Science were searched. Studies were included from 2015 onwards. The studies included were reviews, original research, systematic reviews, and qualitative research. Only full‐text articles available in English language were included. Studies published in other languages, lacking full text and irrelevant to the scope of this review were excluded. The screening process was conducted by pairs of authors (Kavery Chengappa and Mithun Pai) who independently reviewed titles, abstracts, and full texts. Discrepancies were resolved by consensus or with a third author’s input (Shweta Yellapurkar). The start of search date of 2015 was selected to focus on the rising number of publications on this topic over the last 10 years. The search strategy was formed in conjunction with an experienced researcher (Kalyana C Pentapati) to ensure all relevant records in these databases were identified. Manual searching of the references for related studies was also carried out by searching in relevant high impact journals and citation trailing of relevant articles. Only studies that directly discussed the application of AI in personalizing dental education were included.

Limitations and uncertainty · source passage

The studies included had smaller sample size and lacked generalizability. This integrative review presents with limitations. The studies could not be compared systematically due to lack of uniform assessment criteria for personalization in dental education driven by AI. The evolution of AI based tools being studied and developed constantly implies that the advancements discussed in this review could become obsolete in a short time, hampering the long‐term relevance of this review.

Design the system before the pilot

Document the learner model, mastery rule, hint sequence and adaptation signal. Compare adaptive routing with the same content in a non-adaptive sequence. Check whether early errors lead to persistent low-expectation pathways.

  • Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.
  • Co-design accommodations with learners and specialists. Check access and error burden across groups without inferring subgroup effectiveness from small or unreported samples.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Mithun Pai BH; Shweta Yellapurkar; Kavery Chengappa; Kalyana C Pentapati. Personalized Learning With Artificial Intelligence in Dental Education: An Integrative Review. International Journal of Dentistry (2026-01-16). https://doi.org/10.1155/ijod/8928166

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.