AI-Powered Instant Textual Feedback on Physiotherapist Student Practical Performance

Oluwasegun  Odesola; Hiba Alsmadi; Ala Al Kafri; Gok Kandasamy

doi:10.1109/IMSA61967.2024.10652614

AI-Powered Instant Textual Feedback on Physiotherapist Student Practical Performance

Oluwasegun Odesola, Hiba Alsmadi, Ala Al Kafri, Gok Kandasamy

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This study addresses the need for comprehensive feedback in academic physiotherapy programs. Existing methods often fall short of providing coherent feedback using keywords, leaving a gap in evaluating crucial clinical skills. Introducing iAtexF, a Keyword-Based AI system, it offers instant feedback during practical exams, categorized into ‘Good’, ‘Improvement’, and ‘Read more’, aiding students in understanding their performance. Utilizing a Seq2seq framework with diverse LSTM and attention mechanisms, iAtexF excels in relevance, achieving a high similarity score of 73% and a ROUGE score of 34%. It surpasses both ChatGPT and experts in providing relevant suggestions (80.7%), maintaining an appropriate tone (86.7%), and ensuring a logical structure order (100%). User experience evaluation of the iAtexF web application yielded a favourable 92% rating, indicating its usability and effectiveness. This research signifies a significant advancement in educational technology and natural language processing, enhancing physiotherapy training through personalized AI-generated feedback, thereby improving the overall learning experience

Original language	English
Title of host publication	2024 Intelligent Methods, Systems, and Applications (IMSA)
Publisher	IEEE
Pages	1-7
Number of pages	7
ISBN (Print)	9798350362633
DOIs	https://doi.org/10.1109/IMSA61967.2024.10652614 https://doi.org/10.1016/j.ssaho.2024.101180
Publication status	Published - 27 Sept 2024
Event	IMSA The 2nd International Conference Intelligent Methods, Systems, And Applications - MSA University, Cairo, Egypt Duration: 13 Jul 2024 → 14 Jul 2024 https://imsa.msa.edu.eg/archive/imsa24/

Conference

Conference	IMSA The 2nd International Conference Intelligent Methods, Systems, And Applications
Abbreviated title	IMSA
Country/Territory	Egypt
City	Cairo
Period	13/07/24 → 14/07/24
Internet address	https://imsa.msa.edu.eg/archive/imsa24/

Access to Document

Cite this

@inproceedings{967ae17341af4ee0b50820b24ddd8a7f,

title = "AI-Powered Instant Textual Feedback on Physiotherapist Student Practical Performance",

abstract = "This study addresses the need for comprehensive feedback in academic physiotherapy programs. Existing methods often fall short of providing coherent feedback using keywords, leaving a gap in evaluating crucial clinical skills. Introducing iAtexF, a Keyword-Based AI system, it offers instant feedback during practical exams, categorized into {\textquoteleft}Good{\textquoteright}, {\textquoteleft}Improvement{\textquoteright}, and {\textquoteleft}Read more{\textquoteright}, aiding students in understanding their performance. Utilizing a Seq2seq framework with diverse LSTM and attention mechanisms, iAtexF excels in relevance, achieving a high similarity score of 73% and a ROUGE score of 34%. It surpasses both ChatGPT and experts in providing relevant suggestions (80.7%), maintaining an appropriate tone (86.7%), and ensuring a logical structure order (100%). User experience evaluation of the iAtexF web application yielded a favourable 92% rating, indicating its usability and effectiveness. This research signifies a significant advancement in educational technology and natural language processing, enhancing physiotherapy training through personalized AI-generated feedback, thereby improving the overall learning experience",

author = "Oluwasegun Odesola and Hiba Alsmadi and {Al Kafri}, Ala and Gok Kandasamy",

year = "2024",

month = sep,

day = "27",

doi = "10.1109/IMSA61967.2024.10652614",

language = "English",

isbn = "9798350362633",

pages = "1--7",

booktitle = "2024 Intelligent Methods, Systems, and Applications (IMSA)",

publisher = "IEEE",

note = "IMSA The 2nd International Conference Intelligent Methods, Systems, And Applications, IMSA ; Conference date: 13-07-2024 Through 14-07-2024",

url = "https://imsa.msa.edu.eg/archive/imsa24/",

}

Odesola, O, Alsmadi, H , Al Kafri, A & Kandasamy, G 2024, AI-Powered Instant Textual Feedback on Physiotherapist Student Practical Performance. in 2024 Intelligent Methods, Systems, and Applications (IMSA). IEEE, pp. 1-7, IMSA The 2nd International Conference Intelligent Methods, Systems, And Applications, Cairo, Egypt, 13/07/24. https://doi.org/10.1109/IMSA61967.2024.10652614, https://doi.org/10.1016/j.ssaho.2024.101180

TY - GEN

T1 - AI-Powered Instant Textual Feedback on Physiotherapist Student Practical Performance

AU - Odesola, Oluwasegun

AU - Alsmadi, Hiba

AU - Al Kafri, Ala

AU - Kandasamy, Gok

PY - 2024/9/27

Y1 - 2024/9/27

N2 - This study addresses the need for comprehensive feedback in academic physiotherapy programs. Existing methods often fall short of providing coherent feedback using keywords, leaving a gap in evaluating crucial clinical skills. Introducing iAtexF, a Keyword-Based AI system, it offers instant feedback during practical exams, categorized into ‘Good’, ‘Improvement’, and ‘Read more’, aiding students in understanding their performance. Utilizing a Seq2seq framework with diverse LSTM and attention mechanisms, iAtexF excels in relevance, achieving a high similarity score of 73% and a ROUGE score of 34%. It surpasses both ChatGPT and experts in providing relevant suggestions (80.7%), maintaining an appropriate tone (86.7%), and ensuring a logical structure order (100%). User experience evaluation of the iAtexF web application yielded a favourable 92% rating, indicating its usability and effectiveness. This research signifies a significant advancement in educational technology and natural language processing, enhancing physiotherapy training through personalized AI-generated feedback, thereby improving the overall learning experience

AB - This study addresses the need for comprehensive feedback in academic physiotherapy programs. Existing methods often fall short of providing coherent feedback using keywords, leaving a gap in evaluating crucial clinical skills. Introducing iAtexF, a Keyword-Based AI system, it offers instant feedback during practical exams, categorized into ‘Good’, ‘Improvement’, and ‘Read more’, aiding students in understanding their performance. Utilizing a Seq2seq framework with diverse LSTM and attention mechanisms, iAtexF excels in relevance, achieving a high similarity score of 73% and a ROUGE score of 34%. It surpasses both ChatGPT and experts in providing relevant suggestions (80.7%), maintaining an appropriate tone (86.7%), and ensuring a logical structure order (100%). User experience evaluation of the iAtexF web application yielded a favourable 92% rating, indicating its usability and effectiveness. This research signifies a significant advancement in educational technology and natural language processing, enhancing physiotherapy training through personalized AI-generated feedback, thereby improving the overall learning experience

U2 - 10.1109/IMSA61967.2024.10652614

DO - 10.1109/IMSA61967.2024.10652614

M3 - Conference contribution

SN - 9798350362633

SP - 1

EP - 7

BT - 2024 Intelligent Methods, Systems, and Applications (IMSA)

PB - IEEE

T2 - IMSA The 2nd International Conference Intelligent Methods, Systems, And Applications

Y2 - 13 July 2024 through 14 July 2024

ER -

AI-Powered Instant Textual Feedback on Physiotherapist Student Practical Performance

Abstract

Conference

Access to Document

Fingerprint

Cite this