F1000Research F1000Research 2046-1402 F1000 Research Limited London, UK 10.12688/f1000research.145824.1 Study Protocol Articles A Quasi-experimental study to assess the effectiveness of plan teaching on Knowledge regarding Artificial Intelligence-based learning among nursing students in selected College of Wardha City: A Protocol

[version 1; peer review: 1 approved]

 Warghane Utkarsh Formal Analysis Methodology Supervision Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0002-8847-749X a 1 Singh Seema Validation Visualization Writing – Review & Editing 1 Smt. Radhikabai Meghe Memorial College of Nursing, Datta Meghe Institute of Higher Education & Research, Wardha, Maharashtra, 442001, India warghaneu@gmail.com

No competing interests were disclosed.

 16 2 2024 2024 13 103 12 2 2024 Copyright: © 2024 Warghane U and Singh S 2024 This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background

Artificial Intelligence (A.I.) is revolutionizing various sectors like healthcare, specifically in nursing education, by improving the quality of care, streamlining workflows, and reducing the cost of healthcare. Integrating A.I. into nursing education can enhance students’ personalized and efficient learning experiences. This study will aim to develop and implement research on A.I. and identification among (BSc) nursing students in selected colleges in Wardha.

 Protocol

100 students will be selected for the study by using a purposive sampling technique. This study will use one group per test and post-test design, and the structured questionnaires will be delivered to the students. Pre-test and post-test data will be taken to assess the development of student knowledge.

 Conclusions

A.I. can be used to create more realistic stimulation experiences, which can help the students to develop their clinical skills. Furthermore, integrating A.I. into nursing education can revolutionize the sector by benefiting students, tutors, and the healthcare system.

 Artificial Intelligence Basic BSc Nursing Healthcare Implications Knowledge Nursing Education The author(s) declared that no grants were involved in supporting this work. Introduction

As healthcare grows, nursing education must evolve to keep nurses updated on nursing care. Higher education has been integrated with Artificial Intelligence (A.I.) technology, which has the potential to develop education by implementing more personalized and efficient student learning. 1 The integration of A.I. into the education sector has had a long history since the 1970s, which resulted in the development of multimedia learning resources, interactive stimulation, online learning, and game development. 2 A.I. holds significant potential to create more complex stimulation that can help to develop critical thinking capacity in student nurses by preparing them for real-life situations, as A.I. technology simulation will become even more advanced by offering more realistic situations to students, allowing them to practice and help in decision making and critical thinking skills. 3 The benefits of A.I. in nursing education, such as interactive learning experiences using chatbot systems, will raise concerns about academics and proper guidance and ethics. However, A.I. can significantly improve nursing students’ learning. 4

A.I. is an impactable field for nursing and other healthcare industries. A.I.-based learning in nursing is a new field in nursing in which students will gain hands-on experience. A.I.-based learning has the potential to revolutionize nursing education by engaging learning experiences. There are several challenges in nursing education, including preparing students for rapidly changing healthcare; A.I.-based learning can address challenges in healthcare and improve student skills and knowledge. After an intervention, we will assess students’ post-tests to see significant improvement in knowledge of A.I.-based learning among B.Sc. nursing students. 5 For example, by using Artificial intelligence, the patient outcome of the hospital will increase, and by using simulation teaching, the student knowledge and practice will increase.

This study will assess the knowledge regarding A.I.-based learning and its implication for nursing education, such as the need to provide more resources for A.I.-based learning in nursing to improve the quality of instruction, address student concerns about technology and update the knowledge on A.I. among students of primary BSc nursing students.

Aim

This study will assess the effectiveness of plan teaching on knowledge regarding A.I.-based learning among BSc nursing students in selected colleges in Wardha.

 Objectives

To assess the knowledge of A.I.-based learning among BSc nursing students.

To assess the effectiveness of planned teaching among students of BSc Nursing.

Operational definition

Assess: According to the Oxford Dictionary, assessment is “evaluating or estimating the nature, ability, or quality”. This study evaluates the effectiveness of planned teaching on the knowledge regarding A.I.-based learning.

Knowledge: According to Oxford Dictionary, “facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject”. This study focuses on knowledge regarding A.I.-based learning.

Artificial intelligence: According to the Oxford Dictionary, A.I. is “the theory and development of computer systems able to perform tasks that would normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages”.

BSc nursing students: According to the Oxford Dictionary, A four-year undergraduate degree program that prepares individuals to become registered nurses.

Plan teaching: According to the Oxford Dictionary, it is a systematic approach to organizing and delivering instruction designed to meet all learners’ needs.

Protocol Ethical considerations

Ethical approval for the study will obtained from the Institutional Ethics Committee of Datta Meghe Institute of Higher Education and Research. (Deemed to University) Ref No. D.M.I.H.E.R. (D.U.)/I.E.C./2023/1270. Written informed consent will be obtained from students and the head of the Institution.

 Study design

This research study will be conducted at Smt. Radhikabai Meghe Memorial College of Nursing, Datta Meghe Institute of higher Education and Research Sawangi (M) Wardha.

 Data collection process

We will get permission for data collection from the principal of the Nursing college. On the first day, we will take consent from students regarding inclusion in the study. On the same day, a pre-test on knowledge regarding A.I.-based learning will be taken from students. After the pre-test, planned teaching will be given to the students. On the seventh day, the Same group of students’ post-tests will be taken to evaluate the effectiveness of planned teaching.

 Participants

Inclusion criteria

Basic BSc nursing students who will enroll at the Datta Meghe Institution of Higher Education and Research.

Students who are available at the time of data collection.

Students who provide informed consent to participate in this study.

Exclusion criteria

Students who are not available at the time of research.

Students who do not provide informed consent to participate in the study.

Sample size calculation

The sample size calculation with reference studies. 6 The following formula was used for the sample size calculation: N = Z α / 2 + Z β 2 P 1 1 − P 1 + P 2 1 − P 2 P 2 − P 1 2 Z α / 2 = at 95 % CI = 1.96

Represents the desired level of statistical significance at 5% Type I Error

Z β = 0.84 : Represents the desired power for 80% N = Minimum samples required for each group

Primary variable = knowledge of A.I.

The percentage for knowledge before teaching structured programs regarding A.I. = 34.07% (As per Reference study). 6

Percentage for knowledge after teaching structured program = 54.07% (Expected).

Considering % a clinically significant margin of improvement, 20%

Minimum sample size required N = 1.96 + 0.84 2 0.3407 ∗ 1 − 0.3407 + 0.5407 ∗ 1 − 0.5407 / 0.20 = 93 Students .

A total of 93 students will be included in the study. The participants will be selected conveniently, and the method used will be a non-probability convenient sampling technique. Figure 1 shows a schematic presentation of the pre-experimental one group and post-experimental research design used for the present study.

Schematic presentation of pre-experimental one group and post-experimental research design used for the present study. Variables

Change in knowledge.

 Data sources/measurement

The pre-test and post-test will be used to assess knowledge levels. The change in knowledge after planned teaching intervention will be assessed by source.

 Bias

The anticipated bias is possible at the statistical analysis level, which will be mitigated using proper analytical software and double verification of the outputs.

 Quantitative variables

Level of knowledge.

 Data analysis and statistical plan

The data will be analysed according to sociodemographic data and knowledge scores of students on A.I.-based learning. Frequency, percentage, mean, and S.D. by considering the p-value <0.05 will be calculated, and the chi-square will be used to understand the relationship between various demographics and knowledge. All the statistical analysis will be performed by using S.P.S.S. software version 27.

 Dissemination

Once completed, the study will be published in Indexed Journals (Scopus, Web of Science, PubMed).

 Study status

Currently, the study still needs to be started.

 Discussion

Many studies have been performed on A.I., but more research is needed for nursing education. 7 Harsh et al. provided a comprehensive overview of A.I., Machine Learning (ML) and Deep Learning (DL) in healthcare industries that describes the cost of care, the shortage of staff and the need for better data management. The author then discusses how A.I., ML and DL can address these challenges. The article also discusses the challenges that must be addressed to realize the potential of A.I., ML, and DL entirely in healthcare settings. 6 Allam JP et al. proposed a deep learning algorithm for drowsiness detection using single-channel electroencephalogram (E.E.G.) signals. The algorithm is based on the conventional neural networks (CNN) that are trained on datasets of E.E.G. signals collected from awake or drowsy participants. The author evaluated the performance of the algorithms on a test set of E.E.G. signals and found that they achieved an accuracy of 90% 9 —a significant improvement over previous methods for drowsiness detection, which had 70% detection. The author also discusses the algorithms’ limitations and suggests possible future research directions. One limitation of the algorithm is that it is only designed to work with single channels in people wearing E.E.G. headsets with multiple channels. 7 A cross-sectional study conducted in Syria by Sarya Swed et al. on knowledge, attitude and practice of A.I. among doctors. 1494 samples from doctors and medical students. The inclusion was voluntary. Of the total participants, 255 participants (16%) were doctors, and others, 1,252 (83%) were undergraduate medical students, about 1,055 (70%) knew A.I. and 357 (23%) participants knew about an application in the medical field. 10

In Middle Eastern countries, the implication of A.I. for the diagnosis and treatment of patients is more significant for better outcomes. 11 There are many articles published on planned teaching related to different medical categories. Nursing education has several challenges, including preparing students for rapidly changing healthcare. 6 , 12 , 13 A.I.-based learning has the potential to address challenges and provide students with the skills and knowledge they need to succeed. 10

Data availability

No data are associated with this article.

 References Allen DMW Allen JR : How artificial intelligence is transforming the world. Brookings;2018. Chen X Xie H Zou D : Application and theory gaps during the rise of Artificial Intelligence in Education. Comput. Educ. Artif. Intell. 2020 Jan 1;1:100002. 10.1016/j.caeai.2020.100002 Trends M : Artificial Intelligence: Nurses Integral Role in Deployment. Analytics Insight. 2020. Robert N : How artificial intelligence is changing nursing. Nurs. Manag. (Harrow). 2019 Sep;50(9):30–39. 10.1097/01.NUMA.0000578988.56622.21 Kathleen G John S Gerardo D : Artificial Intelligence Education for the Health Workforce: Expert Survey of Approaches and Needs. Khushbu M Archana M Mayuri H : the effectiveness of planned teaching and practice regarding growth monitoring of under-five children among Anganwadi workers in a rural area of Wardha district. Zhang K Aslan AB : A.I. technologies for education: Recent research & future directions. Comput. Educ. Artif. Intell. 2021 Jan 1;2:100025. 10.1016/j.caeai.2021.100025 Rathi HK Dawande P Kane S : Artificial Intelligence, Machine Learning, and Deep Learning in Health Care. ECS Trans. 2022 Apr 24;107(1):15981–15987. 10.1149/10701.15981ecst Allam J Samantray S Behara C : Customized deep learning algorithm for drowsiness detection using single-channel E.E.G. signal. 2022; pp.189–201. Mehta N Harish V Bilimoria K : Knowledge of and Attitudes on Artificial Intelligence in Healthcare: A Provincial Survey Study of Medical Students medRxiv. 2021 Renn BN Schurr M Zaslavsky O : Artificial Intelligence: An Interprofessional Perspective on Implications for Geriatric Mental Health Research and Care. Front. Psych. 2021;12. 34867524 10.3389/fpsyt.2021.734909 PMC8634654 Taksande V Burbare N Chaure K : To Assess the Effectiveness of Planned Teaching on the Knowledge Regarding Epilepsy in Children Among the Anganwadi Workers. Int. J. Curr. Res. Rev. 2020;12(24):151–154. 10.31782/IJCRR.2020.122439 Mendhe DM Lohakare A Lohakare T : To Assess the Effectiveness of Planned Teaching Regarding Domestic Accidents among the Elderly People. Indian J. Forensic Med. Toxicol. 2020 Oct 29;14(4):6641–6645. Khalid A Hani H Daniyal A : A Survey of Artificial Intelligence Techniques Employed for Adaptive Educational Systems within E-Learning Platforms. 10.1515/jaiscr-2017-0004 10.5256/f1000research.159828.r258765 Reviewer response for version 1 Rony Moustaq Karim Khan 1 2 Referee https://orcid.org/0000-0002-6905-0554 University of Dhaka, Dhaka, Dhaka Division, Bangladesh Bangladesh Open University (Ringgold ID: 421966), Gazipur, Dhaka Division, Bangladesh

Competing interests: No competing interests were disclosed.

 30 4 2024 Copyright: © 2024 Rony MKK 2024 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve

Comments to authors-

This intriguing protocol addressing the integration of AI into nursing education holds immense promise for advancing both learning outcomes and healthcare practices. By designing a quasi-experimental study, you are pioneering a path towards personalised and efficient learning experiences for nursing students. Your dedication to innovation is commendable and promises transformative benefits for the entire healthcare sector. However, please address my following comments:

Data Collection Process:

-            Utilizing standardized assessment tools for the pre-test and post-test could enhance the reliability and validity of the data collected, ensuring consistency and comparability of results.

-            Ensuring consistency in the delivery of planned teaching across all sessions and instructors could minimize variability and ensure the uniformity of the intervention's impact on participants.

Thanks

Is the study design appropriate for the research question?

Yes

Is the rationale for, and objectives of, the study clearly described?

Yes

Are sufficient details of the methods provided to allow replication by others?

Yes

Are the datasets clearly presented in a useable and accessible format?

Yes

Reviewer Expertise:

Artificial Intelligence and Nursing

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.