Enhancing dental clinical education through AI: insights into post-clinical summaries

Artificial Intelligence (AI) is increasingly being introduced into clinical education, including dentistry, as a supplement to traditional instructor-led reflection and feedback. Post-clinical summaries—structured end-of-day reviews of patient cases, decision-making, safety concerns, and areas for improvement—are a critical part of this training process. This study explored how trainees perceive the usefulness of AI tools such as ChatGPT and DEEP SEEK in supporting post-clinical summaries, with respect to knowledge consolidation, diagnostic reasoning, teacher–student communication, and professionalism. We conducted a cross-sectional perception survey of 54 participants (undergraduate interns through resident physicians) who had experience with post-clinical summaries. The structured questionnaire used Likert-type items to assess perceived value of AI in four domains. Descriptive statistics were used to summarize responses. Most respondents reported that AI tools help them review theoretical knowledge after clinical work (85.19% agreed/strongly agreed) and clarify diagnostic reasoning (74.08%). Many perceived AI as improving the depth and personalization of teacher–student communication (74.07%) and enhancing confidence in asking questions (72.22%). By contrast, responses regarding non-technical competencies—such as ethical awareness, responsibility, and professional judgment—were more mixed, with many respondents selecting neutral options. Overall, 77.78% of respondents agreed that AI is a valuable resource for improving post-clinical summary activities, and 68.52% would recommend integrating AI into clinical education. Participants generally perceived AI as a helpful adjunct for reinforcing clinical knowledge, supporting diagnostic reasoning, and facilitating communication. Perceived benefits for professionalism, ethics, and responsibility were less clear. Because this study used self-reported perceptions from a single setting, without qualitative data or inferential statistics, the findings should be interpreted as exploratory. Future work should include qualitative interviews, objective performance measures, and longitudinal follow-up to determine whether AI-supported post-clinical summaries translate into measurable educational outcomes.