Actionable companion · Assessment & reasoning

Learning with agency: Comparison of traditional regression modeling vs. AI modeling for the prediction of dental caries: a secondary data analysis

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): Higher education, Children · stage unspecified. Health-professions learning must be distinguished from K–12 instruction.

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

Abstract Introduction There are substantial gaps in our understanding of dental caries in primary and permanent dentition and various predictors using newer modeling methods such as Machine Learning (ML) algorithms and Artificial Intelligence (AI). The objective of this study is to compare the accuracy, precision, and differences between the caries predictive capability of AI vs. traditional multivariable regression techniques. Methods The study was conducted using secondary data stored in the Temple University Kornberg School of Dentistry electronic health records system (axiUm) of pediatric patients aged 6–16 years who were patients on record at the Pediatric Dentistry Clinic. The outcome variables considered in the study were the decayed–missing–filled teeth (DMFT) and the decayed–extracted–filled teeth (deft) scores. The predictors included age, sex, insurance, fluoride exposure, having a dental home, consumption of sugary meals, family caries experience, having special needs, visible plaque, medications reducing salivary flow, and overall assessment questions. Results The average DMFT score was 0.85 ± 2.15, while the average deft scores were 0.81 ± 2.15. For childhood dental caries, XGBoost was the best performing ML algorithm with accuracy, sensitivity. and Kappa as 81%, 84%, and 61%, respectively, followed by Support Vector Machine and Lasso Regression algorithms, both with 84% specificity. The most important variables for prediction found were age and visible plaque. Conclusions The machine learning model outperformed the traditional statistical model in the prediction of childhood dental caries. Data from a more diverse population will help improve the quality of caries prediction for permanent dentition where the traditional statistical method outperformed the machine learning model. Keywords: machine learning, dental caries, prediction, Artificial Intelligence, traditional statistical

What kind of evidence is this?

Supporting AI learning science · Evidence synthesis

Stage: Higher education; Children · stage unspecified. 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.

Participant ages · source passage

Methods The study was conducted using secondary data stored in the Temple University Kornberg School of Dentistry electronic health records system (axiUm) of pediatric patients aged 6–16 years who were patients on record at the Pediatric Dentistry Clinic.

Study methods · source passage

No prior studies comparing AI vs. traditional modeling have been conducted for predicting caries outcomes and using risk factor information from a standardized CRA system. Therefore, the aim of this study was to compare the accuracy, precision, and differences between the caries predictive capability of AI vs. traditional multivariable regression techniques in a sample of pediatric patients attending the Temple University Maurice H Kornberg School of Dentistry (TUKSoD), located in Philadelphia, Pennsylvania, United States.

Limitations and uncertainty · source passage

Traditional multivariable regression techniques are age-old reliable methods used for caries prediction, but these traditional regression models face limitations based on the principles that need to be followed for regression modeling. These include satisfying assumptions such as independence, following normal distribution among other assumptions. Multicollinearity effect among variables cannot be as strongly studied when using regression models (6). Hence, there is a need to study the prediction of dental caries using stronger modeling techniques. Conversely, some of the newer adopted techniques include Artificial Intelligence (AI) based on Machine Learning (ML) modeling, which in comparison with the traditional approaches have shown to have higher prediction accuracy and are likely to contribute significantly to the diagnostic process. ML is a subset of AI that utilizes algorithms trained on datasets to produce models that can perform complex tasks by reducing over-/under-fitting of models (1). Furthermore, AI can analyze data with a variety of features that cannot be handled by traditional regression techniques such as the ability to test the performance of a model itself (4).

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

A 90-day district pilot

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.
Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
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.
Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. 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

Priya Dey; Chukwuebuka Ogwo; Marisol Tellez. Comparison of traditional regression modeling vs. AI modeling for the prediction of dental caries: a secondary data analysis. Frontiers in Oral Health (2024-05-24). https://doi.org/10.3389/froh.2024.1322733

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.