Action Research Plan: The impact of the use of Artificial Intelligence in Education on the Cognitive Abilities of University Students

doi:10.21203/rs.3.rs-4981281/v1

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Action Research Plan: The impact of the use of Artificial Intelligence in Education on the Cognitive Abilities of University Students

https://doi.org/10.21203/rs.3.rs-4981281/v1

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This research plan about exploring the impact of Artificial Intelligence (AI) on the cognitive abilities of college students so that future researchers can use this experimental program to focus on how AI-powered Intelligent Tutoring Systems (ITS) can impact 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.

In today's society, the rapid development of Artificial Intelligence (AI) has a significant impact on global activities. AI enabled humans to advance significantly in technology. Helm et al. (2020) 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(2024) 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.

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 automatically 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 specified temperature.

The use of autonomous vehicles (AVs) in everyday mobility has growing rapidly in recent years, and the Center for Sustainable Systems (2023) 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 traffic 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 analyzing 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 defined 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 effectively 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 knowledge 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 affect 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 efficiently 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 effect of AI on the cognitive ability of college students. 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 affects 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.

2.1 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 difficult learning problems. ASSISTments proposed by Roschelle et al. (2016) are able to provide detailed answers and learning suggestions based on students' math problems [6]. This real-time Q&A mechanism can effectively improve students' learning efficiency. 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 experiments, and ultimately analyze data and draw conclusions. This inquiry-based learning approach stimulates independent thinking and problem-solving skills.

ITS can also provide teachers and students with innovative and personalized instruction through data-driven analytics. AI technology can provide teachers with learning plans and teaching styles that are appropriate for specific students and meet the need for differentiated learning for different students. Chassignol et al. (2018) argue that using adaptive learning technologies, the personalization features of AI can adjust the content and difficulty 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 recommend 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 learning 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 significantly 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. (2018) mentioned that Intelligent Pupil Analysis (IPA) Systems are able to quickly provide appropriate learning strategies through college students' failures in learning and timely self-discovery of knowledge and information needed in learning [12]. These systems improve learning outcomes by adjusting the difficulty 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 [13]. 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 [14]. 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 significant improvements in math scores [15].

2.3 Negative effects of AI on college students' learning outcomes

Despite the many positive effects of AI in the field of education, there are equally negative effects. Firstly, the use of AI in education may exacerbate existing inequalities. Bulathwela et al. (2024) 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 benefit everyone equally [16]. 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 benefit from AI-powered personalized learning.

Second, the use of AI in education also raises significant 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(2013) suggest that institutions should let students and educators know that personal data will be used by whom and for what purpose [17].

Third, AI can create anxiety for educators and students. Not only do educators feel anxious about whether they can effectively incorporate AI into their teaching, but they also worry about being replaced by this AI. Adams et al. (2023) suggest that this technological anxiety stems from the fact that educators need continuous professional development to match the pace of AI adoption in education [18]. Similarly, Almaiah et al. (2022) argues that various factors can influence anxiety about AI, and that students may even experience higher levels of anxiety due to the fact that a lack of good computer skills may negatively affect their grades [19].

Fourth, the use of AI can increase students' dependence on technology, which can lead to some undesirable consequences. Zhang et al. (2024) argued that students turn to AI for quick solutions, and that this dependence weakens students' ability to think critically and learn independently [20]. Zhai et al. (2024) also suggested that an over-reliance on AI can affect students' cognitive abilities, including critical thinking and reasoning [21].

With the gradual expansion of AI in education, the use of ITS has effectively 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 teachers. Current research reveals the benefits and some challenges of AI in education, but future research needs to address several limitations. Effective 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.

3.1 Introduction

When I begin researching the use of AI in education, I realize that many college students faced significant challenges in areas such as memory, attention, and critical thinking, which severely impacted their academic performance. This phenomenon motivate me to explore and investigate how AI-powered ITS can enhance these cognitive skills. The goal of the study is determine the effectiveness 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 reaching their full potential.

3.2 Research Methodology

The quantitative research for this study uses a combination of questionnaires and experimental research in a cycle totaling six months. Researchers will 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 will discuss and familiarize themselves with the goals, expected outcomes, and logistics of ITS integration. Researchers and instructors customize the ITS platform to align with the course syllabus, ensuring that the learning modules, quizzes, and assessments in the ITS reflect what is taught in the classroom.

Among other things, researchers will first 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 will use descriptive statistics, t-tests, and ANOVA to evaluate the data, focusing on cognitive improvement and comparing the differences in testing of the students using the ITS to the differences 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 will involve in-depth interviews with selected participants. At the end of the experiment, researchers will collect detailed qualitative data about students' experiences and perceptions 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 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 will 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 will utilize a variety of methods. First, course instructors will be asked 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 will 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 participation, and any associated timelines and expectations. Additionally, researchers will 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 will 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 will be obtained from the Institutional Review Boards (IRBs) of the participating universities to ensure that the study adheres to ethical guidelines and protects 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 will 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 will need 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 will be distributed online to all selected participants. The survey will collect quantitative data on students' perceptions, attitudes, and experiences with AI-powered educational tools. The questionnaire will include demographic information, AI usage patterns, perceived benefits 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 patterns. 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 specific instance where an AI tool helped you learn." and "What challenges have you encountered when using AI-powered educational tools?". Researchers will encourage students to maximize their participation in the pre-lab survey.

3.6 Pre-tests and Post-tests

All participants will take a pre-test during the first 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 first 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 cognitive efficiency. 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 different college grade groups to ensure consistency and accuracy in scoring.

After the test was 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 significant improvement in cognitive ability. Next, Cohen's d will utilized to calculate effect sizes to quantify the degree of difference observed between the pretest and post-test scores. Additionally, researchers conducted an ANOVA to compare the difference 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 findings.

3.7 Timeline Schedule

Time	Works
Month 1: Preparation and Planning
Weeks 1–2:	1. Define research objectives. 2. Course selection: Select a specific 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 fit 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 effectiveness.
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 final feedback on ITS experience and perceived benefits.
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 conclusions.

4.1 Analysis of Expected Quantitative Results

Pre- and post-test scores will show a significant increase in cognitive ability for students using ITS compared to students using traditional learning methods. In the experimental group, researchers will focus on the comparison of pre and post-test scores for language proficiency, thinking skills, and cognitive efficiency. The current research program anticipates a positive effect of the three sub-scores of language proficiency, thinking skills, and cognitive efficiency compared to the pre-test mean, indicating effect size (d=0.2: Small effect size, d=0.5: Medium effect size, d=0.8: Large effect size).

Analysis of variance (ANOVA) will show that there is a difference between the control group and the experimental group (p < 0.01), which is able to confirm the effectiveness of the ITS in improving cognitive performance. When the F value is small (close to 0), it indicates that the variance between the two groups is similar, implying little or no effect. When the F value is greater than 0 and larger, it indicates that there may be a significant positive effect between the two groups. In addition, correlation analyses can and will show a positive correlation between time spent on the ITS and cognitive improvement. For example, when r = 0.1 - 0.3, this indicates a small correlation. When r = 0.3 - 0.5, this indicates a moderate correlation. When r = 0.5 - 1.0, it indicates a high correlation.

4.3 Analysis of Expected Qualitative Results

Questionnaires and open-ended question in-depth interviews enhance participant engagement and motivation. In the questionnaires and interviews, the majority of students believed that using ITS would increase interest and engagement in learning. And, many participants will emphasize the benefits that ITS brings to learning. For example, ITS can provide repetitive practice and tailored teaching strategies that allow them to gain a deeper understanding of complex topics in their learning. For example, the systematic approach of ITS helps to consolidate their knowledge, making it easier for them to recall and apply information in different situations. For example, ITS can be effective in improving academic performance and cognitive skills.

The purpose of this study was to evaluate the impact of an ITS on the cognitive abilities of college students, with a particular focus on memory, critical thinking, and problem-solving skills. It was expected that the results of the study would show that ITS would significantly improve these cognitive abilities. Quantitative data showed that students using the ITS had significantly higher test scores, as well as significantly higher memory, critical thinking, and problem solving skills than students using traditional learning methods. These improvements were supported by statistically significant differences, as evidenced by high F-values and low p-values in the ANOVA results, as well as larger effect sizes in the Cohen's d measure.

Qualitative analyses further confirmed these findings, with students reporting improved engagement, motivation, and understanding of the material due to the personalized and interactive nature of the ITS. Participants appreciated the immediate feedback and tailored learning paths provided by the system, which helped them to effectively meet their individual learning needs.

The study has some limitations and restrictions that need to be recognized. The relatively small sample size of 200 students in this study may limit the generalizability of the findings. A larger sample size, more diverse demographics, and a greater span of geographic locations would provide more reliable and generalizable results. Additionally, the sample was predominantly from a single grade level, limiting the applicability of the study to different educational settings. This experiment also requires a longer study to assess the long-term effects of ITS on learning outcomes and cognitive development.

Although ITS showed significant benefits, educators indicated that students can become overly reliant on the technology, and a balanced approach that combines traditional teaching methods with AI tools is needed. Future research should incorporate more objective measures of engagement and learning outcomes, such as usage analytics and performance metrics from the ITS platform. And, further research is needed to explore the integration of ITS with different traditional teaching strategies to maximize learning outcomes.

Artificial Intelligence, as the most optimal technology in today's society, needs to be maximized for its benefits in education. Educators and policymakers need to support its integration into learning environments. For educators, they should be systematically trained on the use of AI in education and how to effectively 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.

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-emotional 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.

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 specific areas of need. This personalized approach can help students master complex concepts more effectively 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 fit 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.

Funding: Not applicable

Conflicts of interest/ Competing interests: Not applicable

Data availability statement: No new data were generated during the course of this study and all data used were publicly available.

Code availability: Not applicable

Authors' 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.

Adams, C., Pente, P., Lemermeyer, G., & Rockwell, G. (2023). Ethical principles for artificial intelligence in K-12 education. Computers and Education. Artificial Intelligence, 4, 100131-. https://doi.org/10.1016/j.caeai.2023.100131
Almaiah, M. A., Alfaisal, R., Salloum, S. A., Hajjej, F., Thabit, S., El-Qirem, F. A., Lutfi, A., Alrawad, M., Al Mulhem, A., Alkhdour, T., Awad, A. B., & Al-Maroof, R. S. (2022). Examining the Impact of Artificial Intelligence and Social and Computer Anxiety in E-Learning Settings: Students’ Perceptions at the University Level. Electronics (Basel), 11(22), 3662-. https://doi.org/10.3390/electronics11223662
Bulathwela, S., Pérez-Ortiz, M., Holloway, C., Cukurova, M., & Shawe-Taylor, J. (2024). Artificial Intelligence Alone Will Not Democratise Education: On Educational Inequality, Techno-Solutionism and Inclusive Tools. Sustainability, 16(2), NA. http://dx.doi.org.proxy1.library.jhu.edu/10.3390/su16020781
Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018). Artificial Intelligence trends in education: a narrative overview. Procedia Computer Science, 136, 16–24. https://doi.org/10.1016/j.procs.2018.08.233
Chen, L., Chen, P., & Lin, Z. (2020). Artificial Intelligence in Education: A Review. IEEE Access, vol. 8, pp. 75264-75278. https://doi.org/10.1109/ACCESS.2020.2988510
Helm, J. M., Swiergosz, A. M., Haeberle, H. S., Karnuta, J. M., Schaffer, J. L., Krebs, V. E., Spitzer, A. I., & Ramkumar, P. N. (2020). Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions. Current reviews in musculoskeletal medicine, 13(1), 69–76. https://doi-org.proxy1.library.jhu.edu/10.1007/s12178-020-09600-8
Kaklauskas, A., Vlasenko, A., Raudonis, V., Zavadskas, E.K. (2011). Intelligent Pupil Analysis of Student Progress System. In: Yang, D. (eds) Informatics in Control, Automation and Robotics. Lecture Notes in Electrical Engineering, vol 133. Springer, Berlin, Heidelberg. https://doi-org.proxy1.library.jhu.edu/10.1007/978-3-642-25992-0_24
Kiely, K.M. (2014). Cognitive Function. In: Michalos, A.C. (eds) Encyclopedia of Quality of Life and Well-Being Research. Springer, Dordrecht. p 975. https://doi.org/10.1007/978-94-007-0753-5_426
Kulik, J. A., & Fletcher, J. D. (2016). Effectiveness of Intelligent Tutoring Systems: A Meta-Analytic Review. Review of Educational Research, 86(1), 42-78. https://doi-org.proxy1.library.jhu.edu/10.3102/0034654315581420
Lassiter, K. S., Matthews, T. D., & Rogers, A. (2015). Validity Comparison of the General Abilities Measure for Adults with the Woodcock-Johnson Tests of Cognitive Abilities - Third Edition. North American Journal of Psychology, 17(3), 417-432. https://proxy1.library.jhu.edu/login?url=https://www.proquest.com/scholarly-journals/validity-comparison-general-abilities-measure/docview/1754565251/se-2
Linn, M. C., Slotta, J. D., Terashima, H., Stone, E., & Madhok, J. (2010). Designing Science Instruction using the Web-based Inquiry Science Environment (WISE). Asia - Pacific Forum on Science Learning and Teaching, 11(2), 1-23. https://proxy1.library.jhu.edu/login?url=https://www.proquest.com/scholarly-journals/designing-science-instruction-using-web-based/docview/1955058500/se-2
Milberg, Tanya. (Apr 28, 2024). EDUCATION AND SKILLS: The future of learning: How AI is revolutionizing education 4.0. WORLD ECONOMIC FORUM. https://www.weforum.org/agenda/2024/04/future-learning-ai-revolutionizing-education-4-0/#:~:text=AI%20presents%20an%20avenue%20through,key%20to%20shaping%20tomorrow's%20workforce.
Pane, J. F., Griffin, B. A., McCaffrey, D. F., & Karam, R. (2016). Effectiveness of Cognitive Tutor Algebra I at Scale. Educational Evaluation and Policy Analysis, 36(2), 127-144. https://doi-org.proxy1.library.jhu.edu/10.3102/0162373713507480
Ritter, S., Anderson, J. R., Koedinger, K. R., & Corbett, A. T. (2007). Cognitive Tutor: Applied Research in Mathematics Education. Psychonomic Bulletin & Review, 14(2), 249-255.
Roschelle, J., Feng, M., Murphy, R. F., & Mason, C. A. (2016). Online Mathematics Homework Increases Student Achievement. AERA Open, 2(4). https://doi-org.proxy1.library.jhu.edu/10.1177/2332858416673968
Slade, S., & Prinsloo, P. (2013). Learning Analytics: Ethical Issues and Dilemmas. American Behavioral Scientist, 57(10), 1510-1529. https://doi-org.proxy1.library.jhu.edu/10.1177/0002764213479366
Steenbergen-Hu, S., & Cooper, H. (2014). A meta-analysis of the effectiveness of intelligent tutoring systems on college students’ academic learning. Journal of Educational Psychology, 106(2), 331–347. https://doi-org.proxy1.library.jhu.edu/10.1037/a0034752
University of Illinois Chicago, Online Master of Engineering. (May 07, 2024). What is (AI) Artificial Intelligence? University of Illinois Chicago.
https://meng.uic.edu/news-stories/ai-artificial-intelligence-what-is-the-definition-of-ai-and-how-does-ai-work/
University of Michigan, Center for Sustainable Systems. (2023). Autonomous Vehicles Factsheet. University of Michigan. Pub. No. CSS16-18. https://css.umich.edu/publications/factsheets/mobility/autonomous-vehicles-factsheet
Volume 4, 100131. https://doi.org/10.1016/j.caeai.2023.100131.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/s41239-019-0171-0
Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review. Smart Learning Environments, 11(1), 28. https://doi.org/10.1186/s40561-024-00316-7
Zhang, S., Zhao, X., Zhou, T., & Kim, J. H. (2024). Do you have AI dependency? The roles of academic self-efficacy, academic stress, and performance expectations on problematic AI usage behavior: Revista de Universidad y Sociedad del Conocimiento. International Journal of Educational Technology in Higher Education, 21(1), 34. https://doi.org/10.1186/s41239-024-00467-0

No competing interests reported.

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Action Research Plan: The impact of the use of Artificial Intelligence in Education on the Cognitive Abilities of University Students

Status:

Version 1

Abstract

Introduction

Literature review

2.1 AI in Education

2.2 How AI affects the cognitive effects of college students in learning

2.3 Negative effects of AI on college students' learning outcomes

Methodology

3.1 Introduction

3.2 Research Methodology

3.3 Participants/ Subjects and Recruitment Procedures

3.4 ITS customization settings and teacher training

3.5 Online questionnaire

3.6 Pre-tests and Post-tests

3.7 Timeline Schedule

Data analysis and expected results

Conclusion

Shortcomings and limitations in the research process

Discussion

Future work

Declarations

References

Additional Declarations

Status:

Version 1