A mixed methods crossover randomized controlled trial exploring the experiences, perceptions, and usability of artificial intelligence (ChatGPT) in health sciences education
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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; 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".