Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
The aim of this study is to determine the effect of AI-supported internal medicine nursing case analysis on students' case management performance, learning outcomes, learning experience, clinical self-efficacy, and cognitive load levels. This study will be conducted using a single-blind randomized controlled trial design for the quantitative research and an individual interview design for the qualitative research. Students will be randomly assigned to either the intervention (artificial intelligence) or control (case analysis) group.
The increasing complexity of healthcare services necessitates the adoption of innovative and technology-based approaches in nursing education.This study is planned to be conducted using a single-blind randomized controlled trial design for the quantitative research and an individual interview design for the qualitative research, with the aim of determining the effect of AI-supported internal medicine nursing case analysis on students' case management performance, learning outcomes, learning experience, clinical self-efficacy, and cognitive load levels. This study will include fourth-year nursing students (100 students) enrolled in the Integrated Health Practices III course in the Department of Nursing. Students will be divided into two groups: an intervention group (AI) and a control group. In the study, data will be collected by the researchers using the Student Profile Form, Achievement Test, Learning Experience, Perceived Learning Outcomes and Clinical Self-Efficacy, Scale of Different Types of Cognitive Load, and Semi-Structured Interview Form.
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Artificial Intelligence Group | Experimental | In the artificial intelligence supported case analysis course, students will listen to the audio video prepared by artificial intelligence. |
|
| Case Analysis Group | Active Comparator | The case analysis will be made through the presentation prepared by the students in the group. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Case Analysis | Other | The case analysis will be made through the presentation prepared by the students in the group. |
|
| Measure | Description | Time Frame |
|---|---|---|
| Achievement Test | Achievement Test: The achievement test, created by the researchers, will consist of questions related to the case study and will be scored out of a total of 100 points to determine the impact of the case analysis on students' knowledge level. | 3 hours |
| Learning Experience, Perceived Learning Outcomes, and Clinical Self-Efficacy | Learning Experience, Perceived Learning Outcomes, and Clinical Self-Efficacy: The Mentimeter application will be used to determine students' interest, motivation, learning participation, perceived learning outcome, and clinical self-efficacy levels. Students' satisfaction and motivation levels regarding the case analysis method will be determined on a 10-point scale ranging from 0-Strongly Disagree to 10-Strongly Agree. At the end of the study, each category will be evaluated based on an average value. | 3 hours |
| Measure | Description | Time Frame |
|---|---|---|
| Scale of Different Types of Cognitive Load | Scale of Different Types of Cognitive Load: To assess the mental effort and burden experienced by students during the case analysis process, the scale developed by Leppink et al. (2013) will be used. The scale consists of three sub-dimensions. It comprises a total of 10 items and a 10-point rating scale from 1 (very low) to 10 (very high). Higher scores represent higher levels of cognitive load. The Turkish validity and reliability study of the scale was conducted by Türel and Alpsülün (2025). |
Not provided
Inclusion Criteria:
Exclusion Criteria:
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| AYSER DÖNER, Assistant Professor | Contact | +905052349338 | ayserdoner@erciyes.edu.tr | |
| AYSER DÖNER | Contact | 05052349338 | ayserdoner@erciyes.edu.tr |
| Name | Affiliation | Role |
|---|---|---|
| AYSER DÖNER, Assistant Professor | TC Erciyes University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Erciyes University | Kayseri | Türkiye | 38039 | Turkey (Türkiye) |
Only a short protocol can be shared with other researchers.
Not provided
Not provided
Not provided
Not provided
Not provided
Students will be divided into two groups: an intervention group (artificial intelligence) and a control group (case analysis).
Not provided
Not provided
Not provided
| AI-supported case | Other | In the artificial intelligence supported case analysis course, students will listen to the audio video prepared by artificial intelligence |
|
| 3 hours |