Artificial intelligence, ChatGPT, and dental education: Implications for reflective assignments and qualitative research
Bibliographic record
Abstract
INTRODUCTION: Reflections enable students to gain additional value from a given experience. The use of Chat Generative Pre-training Transformer (ChatGPT, OpenAI Incorporated) has gained momentum, but its impact on dental education is understudied. OBJECTIVES: To assess whether or not university instructors can differentiate reflections generated by ChatGPT from those generated by students, and to assess whether or not the content of a thematic analysis generated by ChatGPT differs from that generated by qualitative researchers on the same reflections. METHODS: Hardcopies of 20 reflections (10 generated by undergraduate dental students and 10 generated by ChatGPT) were distributed to three instructors who had at least 5 years of teaching experience. Instructors were asked to assign either 'ChatGPT' or 'student' to each reflection. Ten of these reflections (five generated by undergraduate dental students and five generated by ChatGPT) were randomly selected and distributed to two qualitative researchers who were asked to perform a brief thematic analysis with codes and themes. The same ten reflections were also thematically analyzed by ChatGPT. RESULTS: The three instructors correctly determined whether the reflections were student or ChatGPT generated 85% of the time. Most disagreements (40%) happened with the reflections generated by ChatGPT, as the instructors thought to be generated by students. The thematic analyses did not differ substantially when comparing the codes and themes produced by the two researchers with those generated by ChatGPT. CONCLUSIONS: Instructors could differentiate between reflections generated by ChatGPT or by students most of the time. The overall content of a thematic analysis generated by the artificial intelligence program ChatGPT did not differ from that generated by qualitative researchers. Overall, the promising applications of ChatGPT will likely generate a paradigm shift in (dental) health education, research, and practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.309 | 0.454 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.047 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".