Physiotherapists and Artificial Intelligence

Adaptation of Future Physiotherapists to the Artificial Intelligence Era: Artificial Intelligence Attitude, Acceptance and Digital Competence

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.

Study Overview

• Status: Completed
• Conditions: Digital Competences; Artificial Intelligence (AI); Physiotherapist Students; Acceptance of Artificial Intelligence; Artificial Intelligence Attitude

Detailed Description

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.

• Study Type: Observational
• Enrollment (Actual): 552

Contacts and Locations

Study Locations

• Turkey
  • Antalya, Turkey
    • Alanya Alaaddin Keykubat University
  • Denizli, Turkey
    • Pamukkale University
  • Malatya, Turkey
    • Inonu University
  • İstanbul, Turkey
    • Okan University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Probability Sample

Study Population

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.

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

physiotherapy students; The acceptance attitude of physiotherapy students towards artificial intelligence and their digital competencies will be conducted in the form of a survey.The characteristics of the study group are that they consist of students studying in a physiotherapy and rehabilitation undergraduate program in Türkiye, that they agree to participate in the study voluntarily and approve the online informed consent form, that they are 18 years of age or older, that they fill out the survey form completely, and that they actively use at least one digital device (smartphone, computer, tablet, etc.).

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Digital Competencies Scale for University Students	It is a valid and reliable scale that measures the digital competences of university students, developed based on the European Digital Competence Framework (DigComp). The original version of the Basic Digital Competences of University Students 2.0 - COBADI scale, developed by López-Meneses et al. (2013), has 4 factors and 31 items. The 4 factors in the COBADI scale are determined as "Competences related to the use of ICT in social communication and collaborative learning", "Competences related to the use of ICT in research", "Interpersonal competences in the use of ICT in the university context" and "University virtual tools and social communication". There are 12 items in the first factor, 11 items in the second factor, and 4 items each in the third and fourth factors. A 4-point Likert type was used in the rating of the scale. Within the scope of the ratings, 1 indicates the least level of competence, while 4 indicates the highest level of competence. The 4-point Likert-type scale co	1 week

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
University Students' Attitude Scale Towards Artificial Intelligence	University Students' Attitude Scale Towards Artificial Intelligence : It is a 5-point Likert-type scale that aims to measure students' feelings, thoughts and attitudes towards artificial intelligence. The scale consists of cognitive, affective and behavioral dimensions. The sub-dimensions of the scale are; Interest in artificial intelligence, Concerns about artificial intelligence, Ethical aspects and social effects of artificial intelligence, Opportunities and threats related to the use of artificial intelligence in education. Its validity and reliability in Turkish were made by Turgut and Kunuroğlu (2025). It is a 5-point Likert-type scale (1: Strongly Disagree - 5: Strongly Agree) consisting of 26 questions. Cronbach Alpha of the scale: 0.89 (total scale), varies between 0.78-0.87 in the sub-dimensions	1 week
Generative Artificial Intelligence Acceptance Scale	A tool to determine the extent to which individuals accept artificial intelligence technologies and how willing they are to use these technologies. To determine the level to which generative artificial intelligence tools (ChatGPT, DALL E, Bard, etc.) are accepted by users and whether they are adopted or not. It was prepared to measure the attitudes, usage intentions and perceptions of individuals in the field of education, especially teacher candidates, students and academics, towards generative artificial intelligence. The scale was developed based on Davis's Technology Acceptance Model (TAM). According to this model, acceptance of technology is related to how useful and easy to use the individual perceives the technology. It is a 5-point Likert-type scale with 20 questions. It is as follows: 1: Strongly Disagree - 5: Strongly Agree. Confirmatory Factor Analysis (CFA) was performed for construct validity and it was determined that	1 week

Collaborators and Investigators

• Sponsor: Uşak University
• Investigators: Principal Investigator: Kevser G Gursan, Dr., Uşak University

Study record dates

Study Major Dates

• Study Start (Actual): April 11, 2025
• Primary Completion (Actual): June 1, 2025
• Study Completion (Actual): June 1, 2025

Study Registration Dates

• First Submitted: April 15, 2025
• First Submitted That Met QC Criteria: April 15, 2025
• First Posted (Actual): April 23, 2025

Study Record Updates

• Last Update Posted (Actual): June 6, 2025
• Last Update Submitted That Met QC Criteria: June 3, 2025
• Last Verified: June 1, 2025

More Information

Terms related to this study

• Keywords: artificial intelligence; physiotherapist; digital age
• Other Study ID Numbers: 607-607-05

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: 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.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No