Nursing Students' Attitudes Towards Artificial Intelligence and Exam Anxiety Levels
- Conditions
- Educational ProblemsNurse's RoleAnxiety
- Interventions
- Behavioral: Experimental Group (Educational content; ChatGPT and Google Bard training)
- Registration Number
- NCT06217926
- Lead Sponsor
- Sinop University
- Brief Summary
This study was planned to determine nursing students' attitudes towards artificial intelligence and test anxiety levels after using ChatGPT and Google Bard in their education. The study will be carried out in accordance with the research feature of quasi-experimental, pretest-posttest, with 3rd and 4th year nursing students studying at Sinop University Faculty of Health Sciences in the 2023-2024 fall academic year. The research groups are named as follows; Experimental Group: The group that will receive ChatGPT and Google Bard training, Control Group: The group that will not receive ChatGPT and Google Bard training, in other words, the 3rd year students of the nursing department will be referred to as the control group, and the 4th year students will be the experimental group. Educational content; ChatGPT and Google Bard training for senior students of the Faculty of Health Sciences who volunteered to participate in the study was provided by Dr. Lecturer It will be given by member Yasemin Özyer Güvener. After the training is completed, students will be asked to use generative artificial intelligence for educational purposes. During the posttest implementation of the study (one week before the final exams), data collection tools will be applied again to students who continue to volunteer to participate in the research.
Key words: nursing students, test anxiety, generative artificial intelligence, ChatGPT, Google Bard
- Detailed Description
In health sciences education, generative artificial intelligence programs, namely ChatGPT and Google Bard, can be used, for example, to create different and realistic clinical stories, to ensure that health sciences students receive instant help in unique clinical cases, and to improve students' communication skills (Sallam, 2023). Additionally, generative AI can assist health science students in self-learning and group work. The usefulness of productive artificial intelligence and the individualized interaction it provides have made it accepted as a source of motivation for students (Connor 2023; Kosak et al., 2023; Gilson et al., 2023; Khan et al., 2023).
Additionally, educators can promote deep learning by teaching students to critically evaluate information generated by generative AI, make informed decisions about the accuracy of information, and think about their responsibilities in the use of generative AI (Sun et al., 2023). For example, as homework, health sciences students may be asked to create an evidence-based protocol for stress management. Students can use generative AI to prepare the protocol. Thus, students can spend their time learning subject-related concepts or gaining knowledge about various topics.
In recent years, rapid developments in artificial intelligence technologies have affected all social systems, including economy, politics, science and education (Luan et al., 2020, Stephanidis et al., 2019). However, people are often unaware of the existence of artificial intelligence applications (Tai, 2020). Gansser and Reich (2021) define artificial intelligence as a technology developed simply to facilitate human life and assist people in certain scenarios. In fact, artificial intelligence is used in many useful contexts, such as diagnosing diseases, protecting environmental resources, predicting natural disasters, improving education, preventing violent acts, and reducing risks in the workplace (Brooks, 2019). Test anxiety is defined as the unpleasant feelings and emotional states experienced by the student during an exam or any evaluation. It is stated that test anxiety negatively affects the academic success of the student.
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 211
- Being a 3rd and 4th year student at Sinop University Faculty of Health Sciences in the 2023-2024 fall academic year
- Volunteering to participate in the research and
- Signing the informed consent form
- Not to be a 3rd or 4th year student at Sinop University Faculty of Health Sciences in the 2023-2024 fall academic year
- Not volunteering to participate in the research
- Not signing the informed consent form
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- PARALLEL
- Arm && Interventions
Group Intervention Description Experimental Group Experimental Group (Educational content; ChatGPT and Google Bard training) During the pretest of the study, firstly, the 4th year students of the Faculty of Health Then, data collection tools will be applied to students who volunteer to participate in the research. Educational content; ChatGPT and Google Bard training for senior students of the Faculty of Health Sciences who volunteered to participate in the study was provided by Dr. Lecturer It will be given by member Yasemin Özyer Güvener. After the training is completed, students will be asked to use generative artificial intelligence for educational purposes. During the posttest implementation of the study (one week before the final exams), the Personal Information Form, "Attitude Scale Towards Artificial Intelligence" and "Westside Exam Anxiety Scale" will be re-administered to the 3rd and 4th year students of the Faculty of Health Sciences.
- Primary Outcome Measures
Name Time Method Attitude Scale Towards Artificial Intelligence One Day This scale was developed by Schepman and Rodway (2020) to measure individuals' general attitudes towards artificial intelligence. The scale contains 20 items, 12 positive and 8 negative. Items are scored with a five-point Likert-type rating scale (from 1=strongly disagree to 5=strongly agree). The validity and reliability study of the attitude scale towards artificial intelligence in Turkish was conducted by Feridun et al. (2022).
- Secondary Outcome Measures
Name Time Method Westside Test Anxiety Scale (WSKS) One day The scale was developed by Driscoll (2007) and adapted into Turkish for university students by Totan and Yavuz (2009). The scale was developed in its original form to be used to examine the effect of a program aimed at reducing test anxiety. While Driscoll (2007) shaped the scale as ten items in a single dimension, Totan and Yavuz (2009) translated the scale into Turkish as eleven items, thinking that defining one item as two different sign variables would be more suitable for Turkish grammar.
Trial Locations
- Locations (1)
Yasemin Özyer Güvener
🇹🇷Sinop, Turkey