This study is a cross-sectional study designed within the scope of the descriptive and relational screening model of quantitative research methods. The research aims to evaluate the digital competence levels, attitudes towards artificial intelligence and artificial intelligence acceptance levels of undergraduate students of the physiotherapy department and to reveal the relationships between these variables.

Research Questions

1. What are the digital competence levels of physiotherapy students?
2. What are the attitude levels of physiotherapy students towards artificial intelligence?
3. What are the acceptance levels of physiotherapy students towards artificial intelligence technologies?
4. Is there a significant relationship between the level of digital competence and the attitude towards artificial intelligence?
5. Is there a significant relationship between the level of digital competence and the acceptance of artificial intelligence technologies?
6. Is there a significant relationship between the attitude towards artificial intelligence and the acceptance level of artificial intelligence technologies?
7. Is there a significant difference between the participants' digital competence, attitudes towards artificial intelligence and acceptance levels according to variables such as gender, grade level and duration of digital tool use? The universe of the research will consist of undergraduate students studying in the Department of Physiotherapy and Rehabilitation at the Faculty of Health Sciences of Alanya, İnönü, Pamukkale, Okan University. The sample of the research is planned to be approximately 600 students who are randomly selected from four different universities to represent different geographical regions and are determined on a voluntary basis.

The research is planned to consist of students studying in the undergraduate program of physiotherapy and rehabilitation in Türkiye. While collecting the data, the Introductory Information Form, Digital Competencies Scale for University Students, Scale of Attitude of University Students Towards Artificial Intelligence, and Productive Artificial Intelligence Acceptance Scale will be used.

The collected data will be analyzed using the SPSS (Statistical Package for Social Sciences) program. The Kolmogorov-Smirnov and Shapiro-Wilk tests will be used to evaluate whether the data are normally distributed. In variables that are normally distributed: Mean, standard deviation, independent sample t-test, ANOVA and Pearson correlation test will be used. In non-normal distribution: Median, minimum-maximum, Mann-Whitney U test, Kruskal Wallis test, Spearman correlation tests will be applied. In addition, regression analysis will be performed to evaluate the relationships between students' sociodemographic information, digital competence, artificial intelligence attitude and artificial intelligence acceptance levels. P < 0.05 will be accepted as the significance level.

Rapid developments in digitalization and artificial intelligence technologies have caused significant changes in the way healthcare services are provided. Today, artificial intelligence-supported applications are actively used in many areas in the healthcare field, from early diagnosis of diseases to treatment, from patient follow-up to personalized care planning. In disciplines where clinical decision-making processes are important, such as physiotherapy and rehabilitation, digital tools and artificial intelligence systems are integrated into the field with motion analysis, exercise tracking, rehabilitation robots, virtual reality-based treatments and artificial intelligence-supported mobile applications. This technological transformation affects not only professional practice but also vocational education. The digital competence levels of university students receiving health education, their capacity to adopt technology and their attitudes towards artificial intelligence are of critical importance in terms of both their individual professional development and post-graduation service quality. Understanding how ready physiotherapy students in particular are for the digital transformation process will guide both the restructuring of educational programs and the harmonization of the profession with technological developments. Studies have shown that health sciences students generally have access to digital tools, but they experience various inadequacies in using these tools effectively and consciously. In addition, it is reported that individuals who develop a positive attitude towards artificial intelligence adapt to these technologies faster and achieve more efficient results in education and clinical practices. However, the number of holistic studies in the literature, especially those specific to physiotherapy students, where artificial intelligence attitudes, technology acceptance and digital competence levels are evaluated together, is quite limited.

Therefore, the rationale of this research is to evaluate the digital competence levels of physiotherapy students, their attitudes towards artificial intelligence and their tendency to accept artificial intelligence technologies, to evaluate their adaptation processes to digitalization in the health field and to produce scientific data that will contribute to educational policies, course content and clinical practice strategies in this context.

The main purpose of this research is to evaluate the digital competence levels of physiotherapy undergraduate students, their attitudes towards artificial intelligence and their tendency to accept artificial intelligence technologies. In addition, by examining the possible relationships between these three variables, it is aimed to reveal to what extent students have developed their professional competencies in the age of digital transformation and artificial intelligence. In this context, the data to be obtained will contribute to the determination of educational needs for digital literacy and artificial intelligence-based applications in the field of health; and will pave the way for understanding the level of adaptation of future physiotherapists to technological developments.

Inclusion Criteria:

• Consisting of students studying in a physiotherapy and rehabilitation undergraduate program in Türkiye,
• Agreeing to participate in the research voluntarily and approving the online informed consent form,
• Being 18 years of age or older,
• Completely filling out the survey form,
• Actively using at least one digital device (smartphone, computer, tablet, etc.)

Exclusion Criteria:

• Studying in any department other than the physiotherapy department,
• Filling out the survey without approving the informed consent form,
• Filling out the survey form incompletely or incorrectly,
• Being under the age of 18

The universe of the study is undergraduate students studying in the Department of Physiotherapy and Rehabilitation at Okan University, Faculty of Health Sciences, Alanya, İnönü, Pamukkale.

In order to protect participant confidentiality and comply with the confidentiality commitment approved by the ethics committee, no sharing of individual-level data is planned. In addition, the ethics committee decision that approved the study covers data use with limited access only. Therefore, it may not be possible to share IPD data publicly.