1. Problem
Comprehensive chart documentation is a key competency in dental hygiene education. Providers spend a significant portion of their day on record‐keeping, over 12% in primary care settings [1]. Accurate charting supports decisions, billing, and legal protection, but excessive documentation reduces time for patient education. It also limits communication, leadership, and collaboration by restricting feedback and team interaction.
At the same time, dental education must adapt to emerging artificial intelligence (AI) tools designed to improve efficiency and support learning. AI‐assisted charting and automated note generation show promise in reducing documentation time [2, 3].
Despite the growing interest in AI, little is known about how such systems can be effectively integrated into dental hygiene training to enhance both perceived efficiency and patient communication [4]. Students continue to learn traditional, manual charting methods. AI‐based documentation tools can help create record periodontal charts, clinical notes, analyze data, and improve record accuracy [5]. The lack of such training limits students’ and teachers’ awareness and readiness to use AI systems.
2. Solution
To address these challenges, a dental hygiene program piloted DentalBee, an AI‐powered tool that converts clinical speech into structured dental notes. The project aimed to determine whether AI‐assisted documentation could improve workflow efficiency and patient engagement while integrating digital tools into dental hygiene education. The pilot involved 2nd‐year students during clinical rotations and included faculty training, AI‐assisted modules, and integration with the patient management system (PMS) (Table 1). Launched in Spring 2025, the initiative received administrative and IT support.
TABLE 1.
AI‐assisted teaching modules curriculum.
| Module | Curriculum |
|---|---|
| AI charting training | Hands‐on sessions where students learn to use the AI software, interpret AI‐generated notes, and review or edit documentation as needed. |
| AI literacy lectures | Guided discussions of AI risks such as hallucinations, biases, and data privacy; anchored in teaching frameworks for ethical AI use in dental education [2, 3, 5]. |
| Leadership and communication workshops | Small‐group and peer feedback exercises focused on structuring patient interviews, delivering empathetic care, and providing performance feedback on both communication and AI‐augmented documentation efforts [4]. |
Outcome measures focused on students’ perceived efficiency and patient interaction, assessed through a structured survey. Faculty also observed reduced documentation time, though exact times were not recorded because implementation occurred alongside student training, conditions that would have artificially inflated time data and misrepresented tool performance. This pilot established baseline perceptions of efficiency and engagement, providing a foundation for future studies to measure objective time savings once system use becomes routine.
3. Results
Institutional review board exemption was granted for this educational quality‐improvement project. A quantitative survey assessed student perceptions of documentation efficiency and patient communication at three timepoints: pre‐implementation (n = 23), mid‐rotation (1 month; n = 23), and post‐rotation (3 months; n = 20).
At baseline, 74% of participants reported that documentation significantly impacted their perceived appointment efficiency (Figure 1). Following AI integration, 12.5% of students reported noticeable improvement at mid‐rotation, increasing to 50% by post‐rotation—suggesting a gradual adaptation and perceived benefit over time.
FIGURE 1.

Perceived efficiency: Comparing the impact of notetaking on appointment efficiency pre‐surveyed (left) and the improvement levels after using AI‐powered clinical documentations (mid at 1 month and post at 3 months).
In terms of patient communication, 50% of students initially indicated that note‐taking limited their ability to connect with patients, while 27% reported a moderate burden and 23.5% a high burden (Figure 2). After using the AI system, 15.6% reported high improvement in patient connection at mid‐rotation, increasing to 41.9% by post‐rotation.
FIGURE 2.

Patient connection: Comparing the impact of notetaking on patient connection in pre‐surveyed (left) and the improvement levels after using AI‐powered clinical documentations (mid at 1 month and post at 3 months).
Although self‐reported, these findings suggest that with continued use, AI‐assisted documentation may improve perceived workflow efficiency and facilitate more meaningful patient engagement. Future studies should include objective measurement of documentation time and direct assessment of patient outcomes to strengthen these preliminary results.
Acknowledgments
The authors would like to acknowledge the support of Catherine Ford, Dean of Owens Community College School of Nursing and Health Professions. Acknowledgment to Kanza Javed for the logistic support.
References
- 1. Belotti L., Maito S., Vesga‐Varela A. L., et al., “Activities of the Oral Health Teams in Primary Health Care: A Time‐Motion Study,” BMC Health Services Research 24, no. 1 2024, 617. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Thurzo A., Strunga M., Urban R., Surovková J., and Afrashtehfar K. I., “Impact of Artificial Intelligence on Dental Education: A Review and Guide for Curriculum Update,” Education Sciences 13, no. 2 (2023): 150, 10.3390/educsci13020150. [DOI] [Google Scholar]
- 3. Claman D. and Sezgin E., “Artificial Intelligence in Dental Education: Opportunities and Challenges of Large Language Models and Multimodal Foundation Models,” JMIR Medical Education 10, no. 1 (2024): e52346, 10.2196/52346. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. El‐Hakim M., Anthonappa R., and Fawzy A., “Artificial Intelligence in Dental Education: A Scoping Review of Applications, Challenges, and Gaps,” Dentistry Journal 13, no. 9 (2025): 384, 10.3390/dj13090384. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Yazdi N. A., Zamaniahari U., Khadivimaleki H., and Hasanabadi P., “Readiness to Use Artificial Intelligence: A Comparative Study Among Dental Faculty Members and Students,” BMC Medical Education 25 (2025): 1006, 10.1186/s12909-025-07621-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
