Evidence map›Paper›PMID 41126845›Full record

ArticleJournal of Taibah University Medical Sciences2025

Utilization patterns of AI chatbots among physical therapy students: A multicountry cross-sectional study.

Salwa B El-Sobkey, Dina Al-Amir Mohamed, Mahmoud ElKholy, Tayseer Abdeldayem, Aya Fawzy, Yomna F Ahmed, Ayman El Khatib, Hind Khalid, Mutasim D Alharbi, Karim Fathy and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of Taibah University Medical Sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Salwa B El-SobkeyFaculty of Physical Therapy, Beni-Suef University, Beni-Suef, Egypt.
Dina Al-Amir MohamedFaculty of Physical Therapy, Beni-Suef University, Beni-Suef, Egypt.
Mahmoud ElKholyFaculty of Physical Therapy, Beni-Suef University, Beni-Suef, Egypt.
Tayseer AbdeldayemFaculty of Physical Therapy, Beni-Suef University, Beni-Suef, Egypt.
Aya FawzyFaculty of Physical Therapy, Modern University for Technology and Information, Cairo, Egypt.
Yomna F AhmedFaculty of Physical Therapy, Modern University for Technology and Information, Cairo, Egypt.
Ayman El KhatibPhysical Therapy Department, Faculty of Health Sciences, Beirut Arab University, Beirut, Lebanon.
Hind KhalidFaculty of Physical Therapy, October 6 University, October 6, Egypt.
Mutasim D AlharbiDepartment of Physical Therapy, Faculty of Medical Rehabilitation Sciences, King Abdulaziz University, Jeddah, Saudi Arabia.
Karim FathyFaculty of Physical Therapy, October 6 University, October 6, Egypt.
Khaled TakeyPhysical Therapy Department, Faculty of Health Sciences, Beirut Arab University, Beirut, Lebanon.
Mariam AbdallahFaculty of Physical Therapy, Beni-Suef University, Beni-Suef, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To describe usage patterns of artificial intelligence (AI)-powered chatbots (AICs) among physical therapy (PT) students and explore the associations between students' demographic and academic characteristics and their patterns of AIC utilization. Methods: A multicountry cross-sectional survey was conducted from September 2024 to May 2025 across five PT programs in Egypt, KSA, and Lebanon. A convenience sample of 500 PT students (100 per university) completed a questionnaire assessing demographic data, academic characteristics, types of AICs used, duration and frequency of use, course types, and educational tasks. Academic levels were categorized into three groups (junior, intermediate, senior). Data were analyzed using descriptive statistics, chi-square tests, and analysis of variance, with significance set at p < 0.05. Results: The mean age of participants was 20.3 years and 53.2 % of participants were female. Participants had a mean cumulative grade point average percentage (CGPA%) of 83.3 %. Most participants were junior-level students (43 %). ChatGPT was the most frequently used AIC (98.4 %). Students reported using AICs for up to 4 years, with an average of 11.35 months. The most frequent task was topic explanation (89.8 %), followed by knowledge acquisition (69.6 %). Theoretical courses were the predominant context for AIC use (55.6 %), whereas practical (5.8 %) and clinical (3.6 %) courses were less frequently associated. Course type significantly influenced the type of academic task performed with AICs (p < 0.05), with theoretical courses associated with all tasks. Conclusions: PT students demonstrated high engagement with AICs, primarily for theoretical course tasks such as topic explanation and resource acquisition. Practical and clinical course engagement were limited, possibly due to the nature of required competencies and reliance on human interaction. ChatGPT dominated usage. Educators should be aware of these patterns to support balanced integration of AICs in PT education, enhancing cognitive skill development while preserving essential human interactions in practical and clinical learning environments.

Indexed as

Artificial intelligenceEducational technologyPhysical therapy educationStudents

Identifiers

PMID41126845
PMCPMC12538060

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.