A mixed methods crossover randomized controlled trial exploring the experiences, perceptions, and usability of artificial intelligence (ChatGPT) in health sciences education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Generative artificial intelligence (AI) integrated programs such as Chat Generative Pre-trained Transformers (ChatGPT) are becoming more widespread in educational settings, with mounting ethical and reliability concerns regarding its usage. This paper explores the experiences, perceptions, and usability of ChatGPT in undergraduate health sciences students. Methods: Twenty-seven students at Carleton University (Canada) were enrolled in a crossover randomized controlled trial study from a Health Sciences course during the Fall 2023 academic term. The intervention condition involved the use of ChatGPT-3.5, whereas the control condition involved using conventional web-based tools. Technology usability was compared between ChatGPT-3.5 and the traditional tools using questionnaires. Focus group discussions were conducted with seven students to further elaborate on student perceptions and experiences. Reflexive thematic analysis was employed to identify themes from the focus group data. Results: Easiness of learnability for personal use and a perception of quick learnability towards ChatGPT-3.5 were significantly higher, compared to conventional online tools from the Systems Usability Scale. Qualitative results highlighted strong benefits of ChatGPT-3.5, such as being a tool for increased overall productivity and brainstorming. However, students identified challenges associated with reliability and accuracy, and concerns about academic integrity. Conclusions: Despite the benefits and positive usability of ChatGPT-3.5 identified by students, an explicit need for the development of policies, procedures and regulations remains. An established framework of best practices for the usage of AI within health science education is necessary. This will ensure accountability of users and lead to a more effective integration of AI technologies into academic settings.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it