Perceptions, Usage, and Educational Impact of ChatGPT Among Medical Students in Germany: Cross-Sectional Mixed Methods Survey
Bibliographic record
Abstract
Background: Large language models such as ChatGPT offer significant opportunities for medical education. However, empirical data on actual usage patterns, perceived benefits, and limitations among medical students remain limited. Objective: This study aimed to assess how medical students in Germany use ChatGPT, their perceptions of its educational value, and the challenges and concerns associated with its use. Methods: A cross-sectional 17-item online survey was conducted between May and August 2024 among medical students from Philipps University Marburg, Germany. A mixed methods approach was applied, combining descriptive and inferential statistical analysis with qualitative content analysis of open-ended responses. Results: A total of 84 fully completed surveys were included in the analysis (response rate: 26.7%; 315 surveys started). Overall, 76.2% (64/84) of the participants reported having used ChatGPT for medical education, with significantly higher usage during exam periods (P=.003). Preclinical students reported higher overall usage than clinical students (P=.02). ChatGPT was primarily used for summarizing information by 60.7% (51/84) of students, for literature research by 57.7% (49/84), and for clarifying concepts by 47.1% (40/84). A total of 70.2% (59/84) felt that it helped them save time, and 51.2% (43/84) reported an improved understanding of content. In contrast, only 31% (26/84) saw benefits for applying knowledge and 15.5% (13/84) for long-term knowledge retention. Qualitative responses highlighted clear benefits such as time savings and support in exam preparation, while also pointing to potential applications in clinical documentation and expressing concerns about misinformation and source transparency. However, 73.3% (55/75) expressed concerns about misinformation, and 72.6% (61/84) reported lacking confidence in their artificial intelligence (AI)-related skills. Only 41.7% (35/84) stated that they trust ChatGPT's outputs. Students who used the tool more frequently also reported higher levels of trust in ChatGPT's outputs (r=0.374, P<.001). Over 70% of respondents indicated a strong desire for increased integration of AI-related education and practical applications within the medical curriculum. Conclusions: ChatGPT was already widely used among medical students, especially in exam preparation and the early stages of training. Students valued its efficiency and support for understanding complex material, but its long-term influence on learning is limited. Concerns about reliability, source transparency, and data privacy remain, and AI skills played a key role in shaping usage. These findings underscore the need to integrate structured, practice-oriented AI education into medical training to support critical, informed, and ethical use of large language models.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".