Action research plan: a methodology to examine the impact of artificial intelligence (AI) on the cognitive abilities of university students

Abstract

This research suggests a methodology to examine the effectiveness Artificial Intelligence (AI) on the cognitive abilities of college students so that future researchers can utilize this experimental project to focus on how AI-powered Intelligent Tutoring Systems (ITSs) affect learning outcomes. As AI continues to revolutionize all walks of life, so has its integration with education. ITS is a key application of AI in education, providing a personalized learning experience by analyzing student data and tailoring instructional materials accordingly. This provides a research program to study the effectiveness of ITS in improving cognitive skills such as memory, critical thinking, and problem solving abilities in college students. The research project combined quantitative and qualitative research methods including surveys, pre-tests, post-tests, and in-depth interviews to assess the cognitive differences between students using ITS and those using traditional learning methods. The research project also recognized potential challenges, such as dependence on technology and the risk of increased educational inequality. The action research program concluded by advocating for a balanced integration of AI into education, highlighting the need for ongoing research to optimize its use across different stages of education and to ensure equitable access to AI-powered tools.

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Discover Education
Research
Action research plan: amethodology toexamine theimpact
ofartificial intelligence (AI) onthecognitive abilities ofuniversity
students
QianXu1
Received: 27 August 2024 / Accepted: 4 November 2024
© The Author(s) 2024 OPEN
Abstract
This research suggests a methodology to examine the eectiveness Articial Intelligence (AI) on the cognitive abilities of
college students so that future researchers can utilize this experimental project to focus on how AI-powered Intelligent
Tutoring Systems (ITSs) aect learning outcomes. As AI continues to revolutionize all walks of life, so has its integration
with education. ITS is a key application of AI in education, providing a personalized learning experience by analyzing
student data and tailoring instructional materials accordingly. This provides a research program to study the eectiveness
of ITS in improving cognitive skills such as memory, critical thinking, and problem solving abilities in college students. The
research project combined quantitative and qualitative research methods including surveys, pre-tests, post-tests, and
in-depth interviews to assess the cognitive dierences between students using ITS and those using traditional learning
methods. The research project also recognized potential challenges, such as dependence on technology and the risk of
increased educational inequality. The action research program concluded by advocating for a balanced integration of AI
into education, highlighting the need for ongoing research to optimize its use across dierent stages of education and
to ensure equitable access to AI-powered tools.
Abbreviations
AI Articial intelligence
AVs Autonomous vehicles
ITS Intelligent tutoring systems
1 Introduction
In today’s society, the rapid development of Articial Intelligence (AI) has a signicant impact on global activities. AI
enabled humans to advance signicantly in technology. Helm etal. suggest that AI was born in the 1950s and initially
referred to machines with human-like intelligence, but has since evolved into a plethora of practical applications driven
by rapid technological advances and big data [1]. University of Michigan similarly proposes that AI enables machines to
learn from experience, adapt to new inputs, and execute tasks resembling human capabilities [2]. Today, AI is enabling
transformative technological changes across industries, with people using AI’s ability to learn, reason, and feedback to
revolutionize the way they approach tasks and make decisions.
* Qian Xu, qianxu2002@gmail.com | 1Johns Hopkins University School ofEducation, Baltimore, Maryland, USA.

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In recent years, people have already experienced the convenience and change that AI has brought to humanity. In
daily home life, people control various home appliances through the voice control function of AI. For example, when
people get up, people only need to say "open curtains" to the smart speaker, and the curtains in the bedroom will auto-
matically open and close. At mealtime, people can start the oven or gas stove by voice command. When adjusting the
air conditioning temperature, as long as people say to the intelligent assistant, "adjust the air conditioning temperature
to 26 degrees", the AI can immediately respond and adjust to the specied temperature.
The use of autonomous vehicles (AVs) in everyday mobility has growing rapidly in recent years, and the Center for
Sustainable Systems believes that AVs use technology to partially or entirely replace the human driver in navigating
a vehicle [2]. AVs replace the human driver in navigating a vehicle from an origin to a destination while avoiding road
hazards and responding to trac conditions. Driverless cars Utilizing smart sensors, they are able to quickly sense the
driving environment and make immediate driving decisions. By continuously and uninterruptedly collecting and analyz-
ing road data, potential hazards while driving are dramatically avoided.
The application of AI in education has fully developing. Milberg (2024) states that Intelligent tutoring systems (ITS) is
a computer-assisted learning platform Integrating AI into education, through traditional or innovative methods, is key
to shaping tomorrow’s workforce [3]. Steenbergen-Hu& Cooper dened ITS, as a key application of AI in education, are
able to intelligently analyze students’ learning data to provide each student with personalized tutoring [4]. For example,
it can help students eectively improve their learning by creating suitable teaching plans. Virtual teaching assistants can
handle a lot of repetitive tasks in a short period of time and help students focus on personalized learning.
In higher education, the academic success of college students depends not only on the quality of education they
receive, but also on their cognitive abilities. Kiely (2014) states that a wide range of mental processes involving knowl-
edge acquisition, information processing, and reasoning are collectively referred to as cognitive functions [5]. These
abilities include memory, learning, perception, attention, decision-making, and language skills. Over time, the important
role of cognitive skills in college students’ learning has been increasingly emphasized. Cognitive skills not only aect
students’ academic performance, but also their approach to and experience of learning. For example, students with
strong memories can store and retrieve classroom information more eciently and perform better on exams and real-
world applications. Similarly, students who pay attention are better able to engage with and understand content in the
classroom. Thinking and problem-solving skills are especially more important when students are faced with complex
and challenging tasks.
The purpose of this study is to design a research scheme to study the eect of AI on the cognitive ability of college stu-
dents. Understanding and improving the use of AI in educational Settings can help improve the cognitive development
and teaching outcomes of college students. Through this action research plan, researchers can explore the relationship
between ITS use and cognitive ability, so as to explore how AI-driven personalized learning aects the cognitive function
of college students. This research project has important implications for educators and educational practitioners who aim
to optimize the learning environment and prepare college students for long-term academic and professional success.
2 Literature review
2.1 AI ineducation
The application of AI in globalized education is expanding, which better highlights the importance of ITS in education.
ITS can answer students’ questions in real time, provide instant feedback and tutoring, and help students to solve dif-
cult learning problems. ASSISTments proposed by Roschelle etal. are able to provide detailed answers and learning
suggestions based on students’ math problems [6]. This real-time Q&A mechanism can eectively improve students’
learning eciency. ASSISTments are also able to determine the students’ knowledge by analyzing their answers. Based
on the algorithm, it suggests follow-up learning suggestions to help students master complex subjects and knowledge
sequentially.
ITS also promotes the development of higher-order thinking skills, as Linn etal. (2009) suggest that the WISE (Web-
based Inquiry Science Environment) system fosters critical thinking and creativity by guiding students to think on their
own [7]. Higher-order thinking helps students to better apply what they have learned to analyze and solve problems
when faced with complex and challenging tasks. Students need to understand concepts and apply them to design experi-
ments, and ultimately analyze data and draw conclusions. This inquiry-based learning approach stimulates independent
thinking and problem-solving skills.

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ITS can also provide teachers and students with innovative and personalized instruction through data-driven analyt-
ics. AI technology can provide teachers with learning plans and teaching styles that are appropriate for specic students
and meet the need for dierentiated learning for dierent students. Chassignol etal. (2018) argue that using adaptive
learning technologies, the personalization features of AI can adjust the content and diculty of instruction for teachers
in real time, based on students’ learning progress and performance, ensuring that each student learns in an appropriate
context [8]. Innovative and appropriate teaching strategies can better meet students’ individualized needs and improve
the quality of teaching. Chen etal. (2020) mentioned that online learning platforms are able to use AI algorithms to rec-
ommend course strategies and learning materials suitable for students based on their learning bases and preferences
[9]. This personalized learning path helps to increase students’ motivation and engagement. Zawacki-Richter etal. (2019)
also argued that the use of personalized AI learning assistance support systems can enable students to have more learn-
ing opportunities, especially in large higher education institutions [10].
2.2 How AI affects thecognitive effects ofcollege students inlearning
AI driven ITS systems in education are impacting the cognitive outcomes of college students. AI-powered personalized
learning platforms and ITS can improve students’ academic performance and knowledge acquisition. Chen etal. (2020)
found that ITS systems enable students to achieve higher quality and deeper learning outcomes [9]. Students were able
to self-discover the knowledge and information they needed to learn and were able to signicantly increase the rate
of knowledge uptake and retention. Chen etal. (2020) also showed that students using ITS systems received superior
instruction to students using traditional instructional methods [9]. Kulik and Fletcher(2016) also concluded that the use
of ITS systems allowed students to perform largely exceeds the level of academic performance achieved by students in
traditional classrooms and those receiving other forms of tutoring [11].
Chassignol etal. mentioned that Intelligent Pupil Analysis (IPA) Systems are able to quickly provide appropriate learn-
ing strategies through college students’ failures in learning and timely self-discovery of knowledge and information
needed in learning [8]. These systems improve learning outcomes by adjusting the diculty of content and knowledge
in real time, and by providing customized instruction and materials based on individual student needs. The Intelligent
Pupil Analysis of Student Progress (IPASP) system by Kaklauskas etal. (2011) is able to improve learning outcomes through
bio-analysis and the use of a variety of methods, including the use of a variety of methods, including the use of a variety
of methods [12]. System is capable of assessing student knowledge through bioanalysis and specialized algorithms that
provide more detailed assessments than traditional methods. Pane etal. (2016) suggest that the use of AI to develop
personalized learning has a high potential to improve student learning outcomes [13]. For example, Cognitive Tutor, an
instructional assistance system, utilizes AI technology to provide personalized instructional tutoring. A study by Ritter
etal. (2007) demonstrated that students using the Cognitive Tutor system showed signicant improvements in math
scores [14].
2.3 Negative effects ofAI oncollege students’ learning outcomes
Despite the many positive eects of AI in the eld of education, there are equally negative eects. Firstly, the use of AI
in education may exacerbate existing inequalities. Bulathwela etal. argued that the use of tools in education is meant
to narrow rather than widen the inequality gap in education, but the use of AI does not benet everyone equally [15].
Students from underprivileged families may not have access to advanced AI technology, leading to a greater disparity in
educational resources. Students and educators without reliable internet access or necessary equipment may not benet
from AI-powered personalized learning.
Second, the use of AI in education also raises signicant data privacy concerns.AI systems often require large amounts
of personal data to operate, which, if not managed properly, can lead to privacy breaches for educators and students.
This raises data security and student privacy issues. Sensitive information can be misused or accessed by unauthorized
parties, resulting in potential harm. Slade & Prinsloo suggest that institutions should let students and educators know
that personal data will be used by whom and for what purpose [16].
Third, AI can create anxiety for educators and students. Not only do educators feel anxious about whether they can
eectively incorporate AI into their teaching, but they also worry about being replaced by this AI. Adams etal. suggest
that this technological anxiety stems from the fact that educators need continuous professional development to match
the pace of AI adoption in education [17]. Similarly, Almaiah etal. argues that various factors can inuence anxiety about

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AI, and that students may even experience higher levels of anxiety due to the fact that a lack of good computer skills
may negatively aect their grades [18].
Fourth, the use of AI can increase students’ dependence on technology, which can lead to some undesirable conse-
quences. Zhang etal. argued that students turn to AI for quick solutions, and that this dependence weakens students’
ability to think critically and learn independently [19]. Zhai etal. also suggested that an over-reliance on AI can aect
students’ cognitive abilities, including critical thinking and reasoning [20].
With the gradual expansion of AI in education, the use of ITS has eectively assisted the learning process of college
students. However, the use of AI in education also poses a number of challenges, including the potential to exacerbate
educational inequality, student dependency, raise data privacy issues, and increase anxiety among students and teach-
ers. Current research reveals the benets and some challenges of AI in education, but future research needs to address
several limitations. Eective AI applications can enhance student learning, but their short- and long-term impacts have
yet to be validated. Educators must conduct more comprehensive and in-depth research to understand the impact of
ITS on students’ cognitive abilities in educational settings. By addressing these issues, future research can better apply
ITS in education and maximize its positive impact on student learning and development.
3 Methodology
3.1 Introduction
When I begin researching the use of AI in education, I realize that many college students faced signicant challenges in
areas such as memory, attention, and critical thinking, which severely impacted their academic performance. This phe-
nomenon motivate me to explore and investigate how AI-powered ITS can enhance these cognitive skills. The goal of
the study is determine the eectiveness of ITS in enhancing the cognitive skills of college students, thereby improving
their learning outcomes and preparing them for future academic and career challenges. By determining the successful
use of ITS in education, we can develop strategies to optimize teaching methods and better support students in reach-
ing their full potential.
3.2 Research methodology
The quantitative research for this study is suggesting to use a combination of questionnaires and experimental research
in a cycle totaling six months. Researchers are suggesting to create and distribute online questionnaires to students in
the early stages. The questionnaire will primarily collect data on students’ perceptions, attitudes, and experiences with
ITS. The experimental study will divide the participants into a control group of 100 and an experimental group of 100.
The experimental group will use the ITS assisted learning system while the control group will use traditional learning
methods. Researchers will select college courses that match the ITS features and target cognitive skills, such as, math,
language, or business management courses. By working with course instructors to supplement ITS related teaching
modules and supplement course materials. All course instructors are suggesting to discuss and familiarize themselves
with the goals, expected outcomes, and logistics of ITS integration. Researchers and instructors customize the ITS plat-
form to align with the course syllabus, ensuring that the learning modules, quizzes, and assessments in the ITS reect
what is taught in the classroom.
Among other things, researchers are suggesting to rst use the Woodcock-Johnson Test of Cognitive Ability (WJ-IV)
as a test of cognitive ability to measure changes in cognitive ability levels through pre- and post-tests. SPSS and Excel
statistical software were used to record the quantitative data collected from the survey and test. Researchers is sug-
gesting to use descriptive statistics, t-tests, and ANOVA to evaluate the data, focusing on cognitive improvement and
comparing the dierences in testing of the students using the ITS to the dierences in testing of the students in the
control group. In addition, researchers will need to ensure that all data collected from pretests, post-tests, and surveys
are complete and error-free.
The qualitative aspect of the study is suggesting to involve in-depth interviews with selected participants. At the
end of the experiment, researchers will collect detailed qualitative data about students’ experiences and percep-
tions of AI in education. Researchers will conduct in-depth interviews with 10% of the participants with open-ended
survey questions that will allow the participants to express their thoughts and feelings about using AI tools in their

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learning process. Through the in-depth interviews, the researchers explore in more detail how their in the use of AI
affects their academic performance and experiences.
3.3 Participants/subjects andrecruitment procedures
This study is suggesting to involve undergraduate students enrolled in the same level and program at a university.
Controlling for multiple variables will allow for a better understanding of the impact and changes in the cognitive
abilities of students from the same educational background. The target sample size is approximately 200 students
to ensure that the results are statistically robust and generalizable.
In order to recruit these participants, researchers are suggesting to utilize a variety of methods. First, course instruc-
tors are suggesting to announce the study in class, explaining the purpose of the study, the procedures involved, and
the potential benefits of participation, aiming to stimulate student interest and willingness to participate in the study.
Researchers are suggesting to send a detailed email invitation to students containing a comprehensive description
of the study, such as the study objectives, the activities participants will be required to perform, the benefits of par-
ticipation, and any associated timelines and expectations. Additionally, researchers are suggesting to strategically
place informational posters around campus, especially in high-traffic areas such as libraries, cafeterias, and student
centers, to attract attention. These posters will be designed to be clear and concise, contain basic information about
the study and how to participate, and provide QR codes for students to scan for more information or to enroll directly.
Finally, researchers are suggesting to post announcements on the university’s online platforms, including student
portals and relevant social media groups, to reach a wider audience. These online announcements will contain links
that students can click on to fill out the enrollment form or for more information.
Prior to the commencement of the study, ethical approval is suggesting to be obtained from the Institutional
Review Boards (IRBs) of the participating universities to ensure that the study adheres to ethical guidelines and pro-
tects the rights and confidentiality of the participants. Each participant will be given a unique identification number
to link all data points while ensuring anonymity.
3.4 ITS customization settings andteacher training
Researchers are suggesting to provide initial training to the 100 students in the experimental group to familiarize all
students with the ITS instructional support platform and distribute unique login accounts. The training will include
how to log in, navigate the system, access learning materials, and use the various features of ITS. Researchers will
provide detailed user manuals and video tutorials to help participants with common problems. Similarly, researchers
will set up a technical support team that participants can contact at any time for technical assistance. Researchers is
suggesting to use icons to record student engagement, time spent on the platform, and module completion rates
when using the ITS.
3.5 Online questionnaire
The structured questionnaire is suggesting to be distributed online to all selected participants. The survey is suggesting
to collect quantitative data on students’ perceptions, attitudes, and experiences with AI-powered educational tools. The
questionnaire will include demographic information, AI usage patterns, perceived benets and challenges. The survey
will be created through a secure online survey platform using multiple choice questions, Likert scale questions, and
some open-ended questions. The multiple-choice questions will collect demographic information and AI usage pat-
terns. For example, "How often do you use AI tools for learning?" and "How much do you know about ITS?". Likert scale
questions will measure students’ perceptions of the impact of AI on their learning. For example, "AI-powered educational
tools have improved my understanding of subject matter." and "I feel that learning is more interesting after using AI
tools.". And open-ended questions will ask students about their personal experiences with AI and any challenges they
have faced. For example, "Describe a specic instance where an AI tool helped you learn." and "What challenges have
you encountered when using AI-powered educational tools?". Researchers can encourage students to maximize their
participation in the pre-lab survey.

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3.6 Pre‑tests andpost‑tests
All participants are suggesting to take a pre-test during the rst week of the experiment and a post-test the week after
the course to determine the cognitive abilities of all participants. A post-test will be administered immediately after
the experiment to compare the changes in students’ cognitive abilities over the course of the experiment. Ensure that
test results are anonymously and securely collected and stored when recording scores from the pre-test and post-test.
Researchers will use the Woodcock-Johnson Test of Cognitive Ability (WJ-IV).
Using the Woodcock-Johnson Test of Cognitive Ability (WJ-IV) scale, the General Intelligence Score (GIA) will calculate
based on scores from the rst seven independent subtests (or all 17 independent subtests) of the standardized test and
the three cognitive subtests. The major cognitive performance factors include language skills, thinking skills, and cogni-
tive eciency. These factors measure language development, thinking processes, and the ability to process information
automatically, respectively. The results of this test will be able to compared between dierent college grade groups to
ensure consistency and accuracy in scoring.
After the test is completed in its entirety, researchers will conduct a paired t-test to compare pre-test and post-test
scores within the experimental group to determine if there was a signicant improvement in cognitive ability. Next,
Cohen’s d can utilized to calculate eect sizes to quantify the degree of dierence observed between the pretest and
post-test scores. Additionally, researchers conducted an ANOVA to compare the dierence values between the pre-test
and post-test between the control and experimental groups to assess the impact of the ITS. Finally, researchers will use
a Pearson correlation to analyze the correlation between ITS use during the experiment and cognitive improvement. Bar
graphs, line graphs, and scatter plots will be created to visualize the data and highlight key ndings.
3.7 Timeline schedule
Time Works
Month 1: Preparation and Planning
Weeks 1–2: 1. Dene research objectives
2. Course selection: Select a specic university course that will integrate ITS
3. Obtain IRB Ethics approval: To the Institutional Review Board (IRB)
4. Work with the University and appropriate classroom teachers to customize ITS modules to
t the course content
Weeks 3–4: 1. Create accounts for participants and train them on the use of ITS
2. Provide participants with detailed user guides and video tutorials through the technical
department
3. Pretest the Woodcock-Johnson cognitive ability test and distribute online surveys
Month 2–5: ITS Implementation 1. Implement regular ITS courses within the course schedule and continuously monitor
progress
2. Regularly check student engagement and collect feedback
3. Continue to provide ITS technical support
4. Conduct surveys to gather feedback on ITS and integrate areas for improvement to
enhance engagement and eectiveness
Month 6: Post-Testing and Final Data Collection
Weeks 1–2: 1. Perform a post-test to compare the changes in cognitive ability compared to the front
2. Conduct in-depth interviews with open questions to collect nal feedback on ITS experi-
ence and perceived benets

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Time Works
Weeks 3–4: 1. Collect quantitative data from pre-test, post-test and survey
2. Quantitative analysis: descriptive statistics, T-test, ANOVA and correlation analysis were
carried out using statistical software
3. Qualitative analysis: Analysis of online questionnaire responses and in-depth interview
data to determine students’ views on ITS
4. Write a comprehensive report documenting methods, results, discussions, and conclu-
sions
4 Conclusion
The purpose of this research plan is for future researchers to use to assess the impact of intelligent learning systems
on the cognitive abilities of college students, with a particular focus on memory, critical thinking, and problem solv-
ing skills, in order to better utilize AI in education. The quantitative and qualitative methodology suggested in the
research proposal can determine the relationship between students who use intelligent learning systems and those
who use traditional methods of learning in terms of test scores, memory, critical thinking, and relationships between
problem-solving skills.
5 Shortcomings andlimitations intheresearch plan
There are some limitations to this research program that need to be recognized. In the future, the sample size of this
study could be expanded. A larger sample size, a more diverse demographic composition, and a greater span of geo-
graphic areas would provide more reliable and generalizable results. In addition, the sample will have to be studied for
applicability in dierent educational settings. This experiment will need to be studied over a longer period of time in the
future to assess the impact of ITS on learning outcomes and cognitive development.
Although there are clear advantages of AI in the application of education, students may become overly dependent
on the technology, so a rational combination of traditional teaching methods and AI tools is needed in the future. Future
research should employ more objective methods to measure student engagement and learning outcomes, such as
usage analytics and performance metrics for ITS. Further research is also needed on how to combine ITS with dierent
traditional teaching strategies to maximize learning outcomes.
6 Discussion
AI, as the most optimal technology in today’s society, needs to be maximized for its benets in education. Educators
and policymakers need to support its integration into learning environments. For educators, they should be systemati-
cally trained on the use of AI in education and how to eectively integrate AI tools into teaching practices. Schools and
educational institutions should promote and support AI infrastructure on a large scale, for example, internet access,
suitable equipment and technical support, especially in remote areas. Policymakers should encourage the adoption of
AI technologies in education, and there is a particular need to provide funding for building AI technologies in poorer
areas to ensure that the technological divide is avoided in all regions.
7 Future work
Future research will explore the impact of new media technologies at various stages of education. In early childhood
education, researchers will examine how AI tools can support language development, cognitive skills, and social-emo-
tional learning in preschoolers. AI apps can provide personalized language practice to help young children improve
their vocabulary and pronunciation. Cognitive development can be supported through AI games and activities. These
games and activities can be adapted to a child’s individualized learning pace and provide challenges appropriate to
their stage of development.

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In middle and high school settings, researchers will focus on how AI can enhance the learning experience and improve
academic performance through personalized instruction. AI education platforms can analyze a student’s strengths and
weaknesses and provide customized learning paths for specic areas of need. This personalized approach can help
students master complex concepts more eectively and at their own pace.
In adult education and lifelong learning, AI can provide learning opportunities for those looking to upgrade their skills
or re-skill. AI powered platforms can provide personalized learning experiences that t schedules, making education more
accessible to those juggling work, family, and other responsibilities. These platforms can recommend courses based on
previous learning experiences and career goals, ensuring that content is relevant and useful.
Author contributions Qian Xu is the sole author. The author read and approved the nal manuscript. Biography: Qian Xu completed her
master degree from the Johns Hopkins University School of Education in 2024 and her undergraduate degree from Arizona State University
in 2023. She is currently planning to apply for a PhD in China in 2025. Research Field Qian Xu: Education, Policy, Equity, Teaching, Sociology.
Funding This work is not supported by any external funding.
Data availability Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.
Code availability Not applicable.
Declarations
Competing interests Not applicable.
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which
permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to
the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modied the licensed material. You
do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party
material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If
material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds
the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco
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