Assessing the Impact of GPTs on Critical thinking, Creativity and Independent Problem solving in Academic learning of students

Assessing the Impact of GPTs on Critical thinking, Creativity and Independent Problem solving in Academic learning of students | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessing the Impact of GPTs on Critical thinking, Creativity and Independent Problem solving in Academic learning of students Jayasuriya R, Selvanayaki S, Deepa N, Muruganandhi D, Kalpana M This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6367830/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Generative Pre-trained Transformers (GPTs) are transforming education by enhancing students critical thinking, creativity and decision making processes. However, concerns exist regarding over-reliance and diminished independent analytical thinking. This study investigated the impact of GPT usage among Undergraduate and postgraduate students at Tamil Nadu Agricultural University. A structured questionnaire was employed to collect primary data from 164 students (122 GPT users and 42 non users). The research adopts a descriptive design and utilizes statistical tools such as Percentage analysis, Garrett ranking, Relative Importance index and Exploratory Factor Analysis (EFA) to assess the influence of GPTs on cognitive skills. The findings indicate that the GPTs enhance efficiency, creativity and Problem-solving, they additionaly possess the risk of overreliance, reducing independent analytical thinking. Factor analysis revealed key dimensions including decision making enhancement, overreliance on AI, problem solving impact and analytical trust. This study concludes that balanced AI integration is essential for maximizing GPTs advantages while minimizing dependency. Educational institutions should encourage critical engagement strategies to ensure responsible AI utilization, an optimal AI-human synergy in academic learning. Generative Pre-trained Transformers Critical Thinking Academic Learning AI in Education Cognitive Skill Development Problem Solving Skills Figures Figure 1 Figure 2 Figure 3 Figure 4 1. INTRODUCTION Generative Pre-trained Transformers (GPT) are a class of deep learning models designed for Natural Language Processing (NLP) tasks, such as text generation, translation, summarization, question answering, and more. Generative – it generates text based on pattern from training data instead of retrieving information, it generates the new content with various keywords. Pre-trained – it trained on vast amount of data before the fine-tuned versions. It helps for understanding language structure and content. Transformers – models use the transformer architecture that understand the text efficiently by using the self-attention mechanism. Transformer architecture, a type of neural network designed to process and generate sequence of data. Artificial intelligence (AI) has developed rapidly in recent years, leading to various applications in different disciplines, such as healthcare and education. Developed by OpenAI, these foundation models power ChatGPT and other generative AI applications capable of simulating human-created output. AI applications have also been utilised in education to enhance administrative services and academic support. ChatGPT is used to assess inputs and take action in a variety of academic and non-academic sectors. For example, it can give real-time information by analysing data from sensors or other monitoring equipment. It has been observed that AI is progressively moving from an algorithm-based intelligence model to a language-based one in the modern period, demonstrating its enormous potential to catch up to human intellect standards (Zhou et al., 2023). The modern artificial intelligence chatbot, ChatGPT was created by the private business OpenAI and is based on extensive language models (Schulman et al., 2022; Zou and Huang, 2023). It builds the complicated information required during user interactions and offers associated services using natural language processing (NLP) technology. Neural networks based on deep learning were used to train this self-governing machine learning platform. One of the primary concerns surrounding the use of GPTs in education is their influence on students cognitive abilities. Traditional education provides analytical reasoning, originality and self-directed learning. However AI- generated content, may lead to passive consumption rather than active engagement with knowledge. Research suggests that when students depend on AI- generated outputs they may struggle to evaluate the validity of information , weakening their critical thinking skills (Kasneci at al., 2023). While GPTs can assist in brainstorming and ideation, the risk of AI-generated responses reinforcing existing biases or conventional patterns may limit truly orginal thought (Lund & Wang, 2023). GPTs also have the potential to enhance learning experiences by exposing students to diverse perspectives, encouraging curiosity, and interdisciplinary exploration. GPTs can act as a cognitive catalysts, stimulating creative thinking rather than replacing it. In problem solving contexts, AI models can assist in breaking down complex issues, guiding students through structured reasoning processes and facilitating decision making in data driven scenarios (Dwivedi et al., 2023).This study seeks to evaluate the impact of GPTs on academic learning by examining their influence on students ability to think critically, create innovative ideas and solve problems independently. Comparing students who frequently engage with AI assisted learning enviroinments to those who rely on conventional study methods, the research will assess whether AI tools function as enhancers or inhibitors of intellectual growth. The findings will contribute to a deeper understanding of Ai’s role in shaping the future of education and inform strategies for its responsible integration into academic settings. 2. Objectives To assess the impact of GPTs on students Critical thinking, analytical skills, Decision making Process To study the influence of GPTs on Creativity and idea generation in academic and research tasks. To study the influence on GPTs , their impact on ability to think independently and solve problem effectively. 3. Literature Review ChatGPT enhances student learning by providing instant academic support, facilitating homework completion, and improving exam preparation. However, concerns arise regarding overreliance, reduced critical thinking, and inaccurate information. The balance between leveraging ChatGPT’s benefits and mitigating its drawbacks remains crucial in shaping effective educational strategies (Pawar et. al.,). ChatGPT transforms education by enhancing personalized learning and supporting faculty and students with real-time assistance. However, challenges like plagiarism and misinformation highlight the need for ethical guidelines, AI detection tools, and innovative task designs to ensure responsible and effective use in academia (Yu fu). ChatGPT's role in increasing student engagement by providing instant feedback and personalized support. While effective, the study warns of over-reliance, which can hinder the development of independent critical thinking skills ( Limna et al., 2023). ChatGPT in introductory chemistry courses to enhance students' critical thinking. Through a staged activity, students showed improved confidence in evaluating information, analyzing complex concepts, and asking insightful questions. However, challenges like low-quality comments and difficulties in validating sources were noted. ( Guo, Ying et al., 2023). ChatGPT has significantly influenced student behavior in the UAE, particularly in academic and learning contexts. Studies reveal that its use enhances critical thinking, creativity, and personalized learning, offering immediate assistance and diverse perspectives. However, overreliance on ChatGPT may diminish independent problem-solving skills, posing a challenge to traditional education methods. The balance between leveraging AI for academic growth and ensuring authentic learning experiences remains a key focus in understanding its impact(Ammar). ChatGPT influences student learning, focusing on cognitive skills such as critical thinking, problem-solving, and creativity. Findings indicate a positive correlation between ChatGPT interaction and cognitive skill enhancement, though concerns about overreliance persist. The study highlights the need for responsible AI integration in education, emphasizing guidance over direct solutions​ ( Qawqzeh et.al.,) ChatGPT's impact on critical, creative, and reflective thinking skills among university students. Findings indicate that ChatGPT enhances engagement and cognitive skills but raises concerns about over-reliance and misinformation. The balance between AI-assisted learning and traditional educational methods is essential for effective skill development​ (Essel et al.,). ChatGPT in education, highlighting its role in fostering personalized learning, critical thinking, and interactive engagement. While AI tools enhance cognitive development, concerns about data privacy, algorithmic bias, and reliance on AI remain key challenges​ ( Thrun et al.,) . ChatGPT’s role in academic learning, highlighting its ability to provide personalized feedback, enhance engagement, and support critical thinking. However, ethical concerns such as academic integrity, data privacy, and potential bias in AI-generated content pose challenges to its effective implementation. The study emphasizes the importance of responsible AI integration to maximize benefits while maintaining educational fairness​ (Saxena et al.,) ChatGPT in undergraduate education, focusing on its potential to improve engagement, problem-solving, and writing skills. However, concerns about AI biases, plagiarism, and the need for faculty training emphasize the importance of responsible integration ( El-Seoud, Samir A. et al.) . ChatGPT enhances students' critical thinking skills by encouraging analysis, evaluation, and paraphrasing of AI-generated content. Findings suggest that while ChatGPT aids academic work, excessive reliance may hinder independent thought. The research emphasizes the importance of guided AI usage to maximize learning benefits while maintaining originality (Harahap et al.,)​ 4. Methodology The study was carried out in Tamilnadu Agricultural University. The primary data was collected from the Undergraduate and Postgraduate Agricultural students of the university. The study incorporated questionnaires as a tool to engage with the students in the usage of GPTs. Information regarding the factors influencing the usage of the GPTs . The study follows a quantitative research approach based on structured survey conducted among students from various academic institutions. The research methodology consists of Research design, Sampling technique and sample size, Data Collection, Data Analysis Methods, Statistical tools used. 4.1 Research Design A descriptive research design was adopted to analyse students perspective on GPTs impact on Critical thinking, Creativity and Independent problem solving. The study utilized a structured questionnaire as the primary data collection instrument. 4.2 Sampling Technique and sample size A stratified random sampling technique was employed to ensure diversity in student representation across different academic backgrounds (Undergraduate, Postgraduate). The sample size was determined in a survey of 164 students ( 122 GPT users and 42 Non GPT users) from various institutions in Tamilnadu Agricultural University and their affliated colleges. 4.3 Data Collection Primary data was collected through an online questionnaire in Google forms designed to capture students perceptions of GPTs influence on their learning process. The questionnaire included Likert-scale questions (ranging from Strongly Agree to Strongly Disagree), ranking questions to facilitate analysis. 4.4 Data Analysis Methods The collected data from the students were analysed using the statistical techniques 4.4.1 Percentage Analysis Percentage analysis is a fundamental statistical tool used in this study to examine the proportion of students experiencing varying degrees of GPT impact. It helps in quantifying students responses to different aspects of GPT learning by calculating the percentage of respondents selecting specific choices. The percentage is computed using the formula Percentage = No. of respondents / Total respondents x 100 4.4.2 Garrett ranking Garrett Ranking Technique was applied to study the preference, change of orders of factors influencing and advantages into numerical scores. The primary advantage of this technique over simple frequency distribution in that factors influencing are arranged based on the severity from the point of view of respondents. It is used to find the most significant factor which had influenced the respondent in their practices. Founded on the Garrett Ranking technique, the study had the respondents rank different factors and their outcome based on their impact thereby converting into score value and rank with the help of following formula : Percent position = 100 (Rij – 0.5) / Nj Where Rij = Rank given for the ith variable by the jth respondents Nij = Number of variable ranked by jth respondents With the help of Garrett’s table, the percent position estimated is converted into scores. The score of each individual are added and then total value of scores and mean values of score is calculated. The factors having highest mean value is considered to be the most important factor. 4.4.3 Relative Importance Index (RII) Relative importance index analysis was calculated based on the information provided in the questionnaires. The Relative Importance index (RII) calculation was significant to this study because its result indicated the ranked degree of relevance. To calculate index, the following formula was applied RII = ∑W / A x N W = the weight given to each response A = the highest weight ( 5 in this case) N = the total number of respondents 4.4.4 Factor Analysis Factor analysis is a dimensionality reduction technique used to identify underlying relationships between observed variables. It helps in reducing a large set of variables into a smaller number of factors in retaining most of the original information. This study employs a quantitative approach using Exploratory Factor Analysis (EFA) to access the impact of GPTs on students independent problem solving. A structured questionnaire with likert-scale items was used to stratified random sample of University students. EFA was conducted using Principal Component Analysis (PCA) with Varimax rotation , retaining factors based on Eigenvalue > 1 , scree plot and Bartlett’s Test of Sphericity (p < 0.05) 5. Results and Discussion 5.1 Percentage analysis The data shows that 74.39 % of students ( 122 respondents) use GPT-based tools, while 25.60 % (42 respondents) do not use GPTs. This indicates a strong reliance on AI tools in academic learning. GPTs can enhance creativity thinking by generating new ideas, improving access to diverse perspectives, and brainstorming ideas. They support Problem solving by providing quick solutions. Non GPT users might develop stronger independent thinking skills through traditional learning methods. This suggests that while GPTs boost efficiency and creativity, dependence may reduce self-driving critical thinking. Table 1. Education level of the Usage of the GPT users Sl.No Education Level Respondents (n=164) % GPT Non GPT GPT Non GPT 1 Undergraduate 63 32 51.63 76.19 2 Postgraduate 59 10 48.36 23.8 The data provided a percentage analysis of respondents education levels and their usage of GPT tools. Out of 164 respondents, 51.63% were undergraduates and 48.36 % were postgraduate s. Among undergraduates 63 respondents are in usage of GPTs, while 32 did not, representing 76.19% and 23.81% of the undergraduate group respectively. For postgraduates, 59 respondents used GPT tools and 10 did not accounting for 48.36% and 51.64 % of the postgraduate group respectively. This analysis highlights that Undergraduates are more likely to use GPT tools complares to postgraduates, the most of the respondents are Undergraduate students. The higher percentage of GPT tools usage among undergraduates (76.19%) suggests that these tools are more integrated into academic learning of the students. The lower usage among postgraduates may indicate different needs, preferences or familiarity level with such tools. Table 2 : Age of the students in the usage of GPTs S.No Age No of Respondents % 1 17-20 34 20.73% 2 21-24 121 73.78% 3 25-27 9 5.48% The data indicates that majority of the respondents are in the age group of 21-24 age (121 individuals or 73.78%) , followed by age group of 17-20 age (34 individuals or 20.73%) and only 9 individuals are from the age groups of 21-24 age group. The 21-24 age group suggest that GPT tools are most commonly used by their students in undergraduate or postgraduate. This study suggests that younger students (17-20) may adapting to AI tools while older students (25-27) might depend on more traditional methods or they have developed independent learning statergies (Kasneci et al., 2023). AI tools like GPT are particularly Table 3 : Gender of the students in the usage of the GPTs Sl.No Gender No of Respondents Percentage (%) 1 Male 96 59% 2 Female 68 41% The data indicates that 59% respondents are male, while 41% are female. This suggests a higher engagements of male students in usage of GPTs. Studies have shown that gender differences AI adoption in education. Male students are more likely to explore and integrate AI tools into their learning process, while female students may exhibit a different approach, it emphasize critical thinking and problem solving (Holmes et al.). GPT tools can enhance creativity and problem solving, but impact on usage of GPT may varies across gender due to differences in technology and usage pattern of GPTs (Kasneci et al, 2023). The female participation suggest that increasing AI adoption among Female students, emphasizing the needs of AI programs to bridge the gap in academic learning of the students. It encourage in diverse perspective in GPT enhanced learning. Garrent Ranking Table 4 : Factors influencing creativity and idea generation in usage of the GPTs Factors Weighted Sum (ΣW) RII Rank Reduce use of Critical Thinking 445 0.72951 2 Decision-Making 337 0.55246 5 Multi-perspective Problem Solving 456 0.74754 1 Trust Over Personal Judgment 402 0.65902 4 Enhanced Analytical Skills 423 0.69344 3 The table highlights key factors influencing creativity and idea generation, with Alignment with goals ranked highest (Mean : 43.72), indicating that creativity where ideas align with the goals is most effective. Exposure to new ideas (Mean : 43.34) ranked second , gives importance of innovation in various perspectives. Collaborative creativity (Mean : 42.13) ranks lower, suggest that potential challenges such as coordination issues and group thinking. The research supports that goal oriented creativity leads to more practical ideas, while exposure to enhances originality (Liu et al., 2024). Collaboration may require structured facilitation to maximum innovation. This suggests that balancing goal alignment, new idea exposure and effective collaboration is optimizing for creative outcomes. Relative Importance Index : Table 5 : Relative Importance Index for C ritical Thinking and Analytical Skills Sl.No Factors Mean value Rank 1 Exposure to new ideas 43.34 II 2 Overcoming creative blocks 42.69 III 3 Alignment with goals 43.72 I 4 Efficiency in Brainstroming 41.15 V 5 Collaborative Creativity 42.13 IV The table represents that the Relative Importance Index (RII) for various factors influencing critical thinking and analytical skills. “Multi-perspective problem solving” ranks highest (RII = 0.74), emphasizing its crucial role for enhancing analytical skills (Paul & elder, 2019). The “Reduce of Critical Thinking” factor ranks second (RII = 0.72), suggest concern over GPT reliance potentially diminishing cognitive engagement (Rahwan et al., 2019). “Enhanced analytical skills” (RII = 0.69) and “Trust over judgement” ( RII = 0.65) indicate the role of AI in improving independent reasoning. The lowest ranked factor, “ Decision making (RII = 0.55) suggest that AI role in decision making remains constant, with implication for automation (Shin et al.,2022). These result align with concerns about AI shaping human cognition and importance in balanced AI-human collaboration (Muller & Bostrom., 2016). Factor Analysis Table 6 : Factors influencing the usage of GPTs Factors Variables Factor Loadings Variance Decision Making Suggestion Verification 0.61 32.4% Decision enhancement 0.62 Decision Dependency 0.58 Overreliance on GPT Over-reliance Limitation 0.67 8.3% Brainstroming efficiency 0.62 Orginality Limitation 0.57 Problem Solving Confidence decline 0.76 6.8% Problem Simplification 0.64 Analytical Trust Argument support 0.71 5.4% Traditional Methods 0.70 Trust Dependence 0.67 Factor analysis is statistical technique to identify the underlying relationships between observed variables. It was applied to examine the impact of GPTs on critical thinking, creativity and independent problem solving in academic learning. The objective was to determine whether these cognitive skills are grouped into latent constructs and simplifying the complexity of multiple observed variables into meaningful factors. The analysis involved the use of Exploratory Factor Analysis (EFA) to uncover hidden structures within the data to validate the identified factor structure. The KMO measure and Bartlett test of sphericity is to asses the sampling adequacy and suitability of the data for factor analysis. The Kaiser-Meyer –Olkin (KMO) value is 0.859 which confirms the sampling adequacy for factor analysis and Bartlett test (p < 0.05) it justify that the use of factor analysis. The scree plot shows a sharp decline in eigenvalues for the first four components, and gradual levelling off, indicate that these four explain the majority variance. This aligns with the Kaiser criterion, suggests components with eigenvalues greater than 1 (Field, 2024). The clear point at the fourth component supports scree tests. This findings is consistent with studies on AI-assisted learning, while critical thinking, over-reliance on AI, problem –solving and analytical trust emerge as key factors (Zhang & chen., 2024). The four components ensures a balance between interpretability and explanatory power in factor analysis. The factor accounting highest variance in Decision making (32.4%) suggest that students utilize GPT to enhance their decision making process and develop a dependency on AI- generated suggestions excessive reliance on GPTs may diminish critical thinking and independent judgement (Liu et al.,). Overreliance on GPT (8.3%) of variance, this highlights that excessive dependence on GPT can improve originality and cognitive engagement. Studies that have shown that overuse of AI tools may limit students ability to think independently and critically (Sandhaus et al., 2024). Problem solving (6.8%) of variance, this factor suggests that GPTs simplify complex tasks, it may reduce students confidence in problem solving independently. Research on AI assisted learning suggests that students who frequently use AI tools may struggle in problem solving when AI is unavailable (Zhang & Chen., 2024). Analytical trust (5.4%) of variance it reflects that students tendency to trust GPT for analytical skills and tasks, which may reduce their own critical thinking abilities. Studies indicate that AI reliance can lead students to accept AI-generated insights without sufficient evaluation, affecting their analytical development (Jones & Brown., 2024). 6. Conclusion The study highlights the advantages and impact of using Generative Pre-trained Transformers (GPTs) and offers insightful information about how they affect analytical trust, critical thinking and decision making. Important features were found by the factor analysis such as improvements in decision making, the effectiveness of problem solving, over dependence on GPTs and shift in analytical trust. According to the study, GPTs help students to solve problems more quickly and make better decisions by providing a various viewpoints. However they also increases cognitive reliance by decreasing the ability to think critically and independently. The scree plot analysis also indicates the most changes in GPT reliance can be explained by a small number of significant components, which supports the idea that excessive use may impair one’s capacity for problem-solving. Previous research has similarly highlighted the risk of automation bias, where users become overly dependent on AI-generated suggestions without sufficient critical evaluation. However, GPTs also serve as valuable cognitive tools when used appropriately, aiding in structuring arguments and simplifying complex Problem-solving tasks. The findings indicate the balanced integration of GPTs in academic and professional settings, ensuring that they function as complementary aids rather than substitutes for independent reasoning. Future research should explore interventions that promote mindful GPT usage while mitigating the risk of overreliance. The study concludes that GPTs should be integrated as assistive tools rather than replacements for independent cognitive processes. While AI enhances learning experiences by improving accessibility, creativity and efficiency, the risk of overreliance must be given through educational interventions that encourage critical engagement with AI-generated content. They focus on strategies to mitigate dependency on AI, ensuring that students develop a balanced approach in AI and maintain their analytical and decision-making skills. Declarations All participants provided informed consent to participate in this study. Where applicable, consent was also obtained for the publication of the findings. The study received approval from the appropriate ethics committee, and in cases where direct consent was not feasible, the need for consent was waived in accordance with committee guidelines. Author Contribution Conceptualization & Design: [Jayasuriya R]Data Collection & Analysis: [Jayasuriya R]Methodology Development: [Jayasuriya R]Writing – Original Draft: [Jayasuriya R]Review & Editing: All authorsSupervision & Critical Insights: [S Selvanayaki]Final Approval: All authors Data Availability Data is provided within the manuscript References Zou, M., and Huang, L. (2023). To use or not to use? Understanding doctoral students’ acceptance of ChatGPT in writing through technology acceptance model. Front. Psychol. 14:1259531. doi: 10.3389/fpsyg.2023.1259531 Shanto, S. S., Ahmed, Z., & Jony, A. I. (2024). Enriching learning process with generative AI: A proposed framework to cultivate critical thinking in higher education using Chat GPT. Tuijin Jishu/Journal of Propulsion Technology , 45 (1), 3019-3029. Kasneci, E., Sessler, K., Pfeiffer, A., Seegerer, S., & Kasneci, G. (2023). "ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education." 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Computers and Education: Artificial Intelligence , 7 , 100316 Yu, C., Yan, J., & Cai, N. (2024, May). ChatGPT in higher education: factors influencing ChatGPT user satisfaction and continued use intention. In Frontiers in Education (Vol. 9, p. 1354929). Frontiers Media SA. Zawacki-Richter, O.; Marín, V.I.; Bond, M.; Gouverneur, F. Systematic Review of Research on Artificial Intelligence Applications in Higher Education—Where are the Educators? Int. J. Educ. Technol. High. Educ. 2019, 16, 39. Zhai, X. (2023). "ChatGPT for Supporting Student Learning: A Review of AI’s Role in Education." Educational Technology & Society, 26 (1), 15-29. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6367830","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":445847387,"identity":"f29cabde-30e6-400b-86ca-bdd2b65f4cab","order_by":0,"name":"Jayasuriya R","email":"data:image/png;base64,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","orcid":"","institution":"Tamil Nadu Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Jayasuriya","middleName":"","lastName":"R","suffix":""},{"id":445847388,"identity":"0b8c104f-208c-4493-82a2-70ba4636e954","order_by":1,"name":"Selvanayaki S","email":"","orcid":"","institution":"Tamil Nadu Agricultural 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09:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6367830/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6367830/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81258372,"identity":"309e8363-4887-40f9-87fb-330fd135378e","added_by":"auto","created_at":"2025-04-24 05:37:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":84909,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparision of GPTs and Non GPT Users\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6367830/v1/551d032072c4a44208845017.png"},{"id":81258223,"identity":"f2e28569-debb-4ac3-81a7-db5ff7d75cdf","added_by":"auto","created_at":"2025-04-24 05:29:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":23202,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEducation level of students in usage of GPTs\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6367830/v1/721748a38dc84d27d7c5190b.png"},{"id":81258224,"identity":"5a180ef1-04fc-4418-9201-7aacd2336ad1","added_by":"auto","created_at":"2025-04-24 05:29:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":13998,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6367830/v1/11054e6e08545e9db40cec23.png"},{"id":81258229,"identity":"dbe57229-e0ed-495c-a46a-83b1dc47366d","added_by":"auto","created_at":"2025-04-24 05:29:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":15622,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGender of the students in the usage of the GPTs\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6367830/v1/2b32a571ad1c9f2b13189637.png"},{"id":82545163,"identity":"45bbf265-81e5-4a7f-aa79-b3238e30b080","added_by":"auto","created_at":"2025-05-12 18:01:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1503237,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6367830/v1/c33136cb-2da6-4be1-9c37-12884bcd6eab.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing the Impact of GPTs on Critical thinking, Creativity and Independent Problem solving in Academic learning of students","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eGenerative Pre-trained Transformers (GPT) are a class of deep learning models designed for Natural Language Processing (NLP) tasks, such as text generation, translation, summarization, question answering, and more. Generative \u0026ndash; it generates text based on pattern from training data instead of retrieving information, it generates the new content with various keywords. Pre-trained \u0026ndash; it trained on vast amount of data before the fine-tuned versions. It helps for understanding language structure and content. Transformers \u0026ndash; models use the transformer architecture that understand the text efficiently by using the self-attention mechanism. Transformer architecture, a type of neural network designed to process and generate sequence of data. Artificial intelligence (AI) has developed rapidly in recent years, leading to various applications in different disciplines, such as healthcare and education. Developed by OpenAI, these foundation models power ChatGPT and other generative AI applications capable of simulating human-created output. AI applications have also been utilised in education to enhance administrative services and academic support. ChatGPT is used to assess inputs and take action in a variety of academic and non-academic sectors. For example, it can give real-time information by analysing data from sensors or other monitoring equipment. It has been observed that AI is progressively moving from an algorithm-based intelligence model to a language-based one in the modern period, demonstrating its enormous potential to catch up to human intellect standards (Zhou et al., 2023). The modern artificial intelligence chatbot, ChatGPT was created by the private business OpenAI and is based on extensive language models (Schulman et al., 2022; Zou and Huang, 2023). It builds the complicated information required during user interactions and offers associated services using natural language processing (NLP) technology. Neural networks based on deep learning were used to train this self-governing machine learning platform. One of the primary concerns surrounding the use of GPTs in education is their influence on students cognitive abilities. Traditional education provides analytical reasoning, originality and self-directed learning. However AI- generated content, may lead to passive consumption rather than active engagement with knowledge. Research suggests that when students depend on AI- generated outputs they may struggle to evaluate the validity of information , weakening their critical thinking skills (Kasneci at al., 2023). While GPTs can assist in brainstorming and ideation, the risk of AI-generated responses reinforcing existing biases or conventional patterns may limit truly orginal thought (Lund \u0026amp; Wang, 2023). GPTs also have the potential to enhance learning experiences by exposing students to diverse perspectives, encouraging curiosity, and interdisciplinary exploration. GPTs can act as a cognitive catalysts, stimulating creative thinking rather than replacing it. In problem solving contexts, AI models can assist in breaking down complex issues, guiding students through structured reasoning processes and facilitating decision making in data driven scenarios (Dwivedi et al., 2023).This study seeks to evaluate the impact of GPTs on academic learning by examining their influence on students ability to think critically, create innovative ideas and solve problems independently. Comparing students who frequently engage with AI assisted learning enviroinments to those who rely on conventional study methods, the research will assess whether AI tools function as enhancers or inhibitors of intellectual growth. The findings will contribute to a deeper understanding of Ai\u0026rsquo;s role in shaping the future of education and inform strategies for its responsible integration into academic settings.\u003c/p\u003e"},{"header":"2.\tObjectives","content":"\u003col\u003e\n \u003cli\u003eTo assess the impact of GPTs on students Critical thinking, analytical skills, Decision making Process\u003c/li\u003e\n \u003cli\u003eTo study the influence of GPTs on Creativity and idea generation in academic and research tasks.\u003c/li\u003e\n \u003cli\u003eTo study the influence on GPTs , their impact on ability to think independently and solve problem effectively.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"3. Literature Review ","content":"\u003cp\u003eChatGPT enhances student learning by providing instant academic support, facilitating homework completion, and improving exam preparation. However, concerns arise regarding overreliance, reduced critical thinking, and inaccurate information. The balance between leveraging ChatGPT\u0026rsquo;s benefits and mitigating its drawbacks remains crucial in shaping effective educational strategies (Pawar et. al.,). ChatGPT transforms education by enhancing personalized learning and supporting faculty and students with real-time assistance. However, challenges like plagiarism and misinformation highlight the need for ethical guidelines, AI detection tools, and innovative task designs to ensure responsible and effective use in academia (Yu fu). ChatGPT's role in increasing student engagement by providing instant feedback and personalized support. While effective, the study warns of over-reliance, which can hinder the development of independent critical thinking skills \u003cb\u003e(\u003c/b\u003eLimna et al., 2023). ChatGPT in introductory chemistry courses to enhance students' critical thinking. Through a staged activity, students showed improved confidence in evaluating information, analyzing complex concepts, and asking insightful questions. However, challenges like low-quality comments and difficulties in validating sources were noted.\u003cb\u003e(\u003c/b\u003eGuo, Ying et al., 2023). ChatGPT has significantly influenced student behavior in the UAE, particularly in academic and learning contexts. Studies reveal that its use enhances critical thinking, creativity, and personalized learning, offering immediate assistance and diverse perspectives. However, overreliance on ChatGPT may diminish independent problem-solving skills, posing a challenge to traditional education methods. The balance between leveraging AI for academic growth and ensuring authentic learning experiences remains a key focus in understanding its impact(Ammar).\u003c/p\u003e \u003cp\u003eChatGPT influences student learning, focusing on cognitive skills such as critical thinking, problem-solving, and creativity. Findings indicate a positive correlation between ChatGPT interaction and cognitive skill enhancement, though concerns about overreliance persist. The study highlights the need for responsible AI integration in education, emphasizing guidance over direct solutions​ (\u003cb\u003eQawqzeh et.al.,)\u003c/b\u003e ChatGPT's impact on critical, creative, and reflective thinking skills among university students. Findings indicate that ChatGPT enhances engagement and cognitive skills but raises concerns about over-reliance and misinformation. The balance between AI-assisted learning and traditional educational methods is essential for effective skill development​ (Essel et al.,).\u003c/p\u003e \u003cp\u003eChatGPT in education, highlighting its role in fostering personalized learning, critical thinking, and interactive engagement. While AI tools enhance cognitive development, concerns about data privacy, algorithmic bias, and reliance on AI remain key challenges​ (\u003cb\u003eThrun et al.,)\u003c/b\u003e. ChatGPT\u0026rsquo;s role in academic learning, highlighting its ability to provide personalized feedback, enhance engagement, and support critical thinking. However, ethical concerns such as academic integrity, data privacy, and potential bias in AI-generated content pose challenges to its effective implementation. The study emphasizes the importance of responsible AI integration to maximize benefits while maintaining educational fairness​ (Saxena et al.,) ChatGPT in undergraduate education, focusing on its potential to improve engagement, problem-solving, and writing skills. However, concerns about AI biases, plagiarism, and the need for faculty training emphasize the importance of responsible integration (\u003cb\u003eEl-Seoud, Samir A. et al.)\u003c/b\u003e. ChatGPT enhances students' critical thinking skills by encouraging analysis, evaluation, and paraphrasing of AI-generated content. Findings suggest that while ChatGPT aids academic work, excessive reliance may hinder independent thought. The research emphasizes the importance of guided AI usage to maximize learning benefits while maintaining originality (Harahap et al.,)​\u003c/p\u003e"},{"header":"4. Methodology","content":"\u003cp\u003eThe study was carried out in Tamilnadu Agricultural University. The primary data was collected from the Undergraduate and Postgraduate Agricultural students of the university. The study incorporated questionnaires as a tool to engage with the students in the usage of GPTs. Information regarding the factors influencing the usage of the GPTs .\u003c/p\u003e \u003cp\u003eThe study follows a quantitative research approach based on structured survey conducted among students from various academic institutions. The research methodology consists of Research design, Sampling technique and sample size, Data Collection, Data Analysis Methods, Statistical tools used.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Research Design\u003c/h2\u003e \u003cp\u003eA descriptive research design was adopted to analyse students perspective on GPTs impact on Critical thinking, Creativity and Independent problem solving. The study utilized a structured questionnaire as the primary data collection instrument.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Sampling Technique and sample size\u003c/h2\u003e \u003cp\u003eA stratified random sampling technique was employed to ensure diversity in student representation across different academic backgrounds (Undergraduate, Postgraduate). The sample size was determined in a survey of 164 students ( 122 GPT users and 42 Non GPT users) from various institutions in Tamilnadu Agricultural University and their affliated colleges.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Data Collection\u003c/h2\u003e \u003cp\u003ePrimary data was collected through an online questionnaire in Google forms designed to capture students perceptions of GPTs influence on their learning process. The questionnaire included Likert-scale questions (ranging from Strongly Agree to Strongly Disagree), ranking questions to facilitate analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Data Analysis Methods\u003c/h2\u003e \u003cp\u003eThe collected data from the students were analysed using the statistical techniques\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1 Percentage Analysis\u003c/h2\u003e \u003cp\u003ePercentage analysis is a fundamental statistical tool used in this study to examine the proportion of students experiencing varying degrees of GPT impact. It helps in quantifying students responses to different aspects of GPT learning by calculating the percentage of respondents selecting specific choices. The percentage is computed using the formula\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cb\u003ePercentage\u0026thinsp;=\u0026thinsp;No. of respondents / Total respondents x 100\u003c/b\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e4.4.2 Garrett ranking\u003c/h2\u003e \u003cp\u003eGarrett Ranking Technique was applied to study the preference, change of orders of factors influencing and advantages into numerical scores. The primary advantage of this technique over simple frequency distribution in that factors influencing are arranged based on the severity from the point of view of respondents. It is used to find the most significant factor which had influenced the respondent in their practices. Founded on the Garrett Ranking technique, the study had the respondents rank different factors and their outcome based on their impact thereby converting into score value and rank with the help of following formula :\u003c/p\u003e \u003cp\u003e \u003cb\u003ePercent position\u0026thinsp;=\u0026thinsp;100 (Rij \u0026ndash; 0.5) / Nj\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWhere\u003c/p\u003e \u003cp\u003eRij\u0026thinsp;=\u0026thinsp;Rank given for the ith variable by the jth respondents\u003c/p\u003e \u003cp\u003eNij\u0026thinsp;=\u0026thinsp;Number of variable ranked by jth respondents\u003c/p\u003e \u003cp\u003eWith the help of Garrett\u0026rsquo;s table, the percent position estimated is converted into scores. The score of each individual are added and then total value of scores and mean values of score is calculated. The factors having highest mean value is considered to be the most important factor.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e4.4.3 Relative Importance Index (RII)\u003c/h2\u003e \u003cp\u003eRelative importance index analysis was calculated based on the information provided in the questionnaires. The Relative Importance index (RII) calculation was significant to this study because its result indicated the ranked degree of relevance. To calculate index, the following formula was applied\u003c/p\u003e \u003cp\u003e \u003cb\u003eRII = \u0026sum;W / A x N\u003c/b\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eW\u0026thinsp;=\u0026thinsp;the weight given to each response\u003c/p\u003e \u003cp\u003eA\u0026thinsp;=\u0026thinsp;the highest weight ( 5 in this case)\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;the total number of respondents\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e4.4.4 Factor Analysis\u003c/h2\u003e \u003cp\u003eFactor analysis is a dimensionality reduction technique used to identify underlying relationships between observed variables. It helps in reducing a large set of variables into a smaller number of factors in retaining most of the original information. This study employs a quantitative approach using Exploratory Factor Analysis (EFA) to access the impact of GPTs on students independent problem solving. A structured questionnaire with likert-scale items was used to stratified random sample of University students. EFA was conducted using \u003cb\u003ePrincipal Component Analysis (PCA)\u003c/b\u003e with \u003cb\u003eVarimax rotation\u003c/b\u003e, retaining factors based on \u003cb\u003eEigenvalue\u0026thinsp;\u0026gt;\u0026thinsp;1\u003c/b\u003e, \u003cb\u003escree plot\u003c/b\u003e and \u003cb\u003eBartlett\u0026rsquo;s Test of Sphericity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. Results and Discussion","content":"\u003cp\u003e\u003cstrong\u003e5.1 Percentage analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data shows that \u003cstrong\u003e74.39 %\u0026nbsp;\u003c/strong\u003eof students \u003cstrong\u003e( 122 respondents)\u003c/strong\u003e use GPT-based tools, while \u003cstrong\u003e25.60 % (42 respondents)\u0026nbsp;\u003c/strong\u003edo not use GPTs. This indicates a strong reliance on AI tools in academic learning. GPTs can enhance creativity thinking by generating new ideas, improving access to diverse perspectives, and brainstorming ideas. They support Problem solving by providing quick solutions. Non GPT users might develop stronger independent thinking skills through traditional learning methods. This suggests that while GPTs boost efficiency and creativity, dependence may reduce self-driving critical thinking.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Education level of the Usage of the GPT users\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"367\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSl.No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEducation Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eRespondents\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=164)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003eGPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003eNon GPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eGPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eNon GPT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eUndergraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e51.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e76.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePostgraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e48.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e23.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe data provided a percentage analysis of respondents education levels and their usage of GPT tools. Out of 164 respondents, \u003cstrong\u003e51.63% were undergraduates\u003c/strong\u003e and \u003cstrong\u003e48.36 % were postgraduate\u003c/strong\u003es. Among undergraduates 63 respondents are in usage of GPTs, while 32 did not, representing 76.19% and 23.81% of the undergraduate group respectively. For postgraduates, 59 respondents used GPT tools and 10 did not accounting for 48.36% and 51.64 % of the postgraduate group respectively. This analysis highlights that Undergraduates are more likely to use GPT tools complares to postgraduates, the most of the respondents are Undergraduate students. The higher percentage of GPT tools usage among undergraduates (76.19%) suggests that these tools are more integrated into academic learning of the students. The lower usage among postgraduates may indicate different needs, preferences or familiarity level with such tools.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 : Age of the students in the usage of GPTs\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"334\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS.No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo of Respondents \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e17-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e20.73%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e21-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e73.78%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e25-27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e5.48%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe data indicates that majority of the respondents are in the \u003cstrong\u003eage group of 21-24 age (121 individuals or 73.78%) ,\u0026nbsp;\u003c/strong\u003efollowed by age group of \u003cstrong\u003e17-20 age (34 individuals or 20.73%)\u0026nbsp;\u003c/strong\u003eand only 9 individuals are from the age groups of 21-24 age group. The 21-24 age group suggest that GPT tools are most commonly used by their students in undergraduate or postgraduate. This study suggests that younger students (17-20) may adapting to AI tools while older students (25-27) might depend on more traditional methods or they have developed independent learning statergies (Kasneci et al., 2023). AI tools like GPT are particularly \u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 : Gender of the students in the usage of the GPTs\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"354\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSl.No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNo of Respondents\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e59%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e41%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data indicates that 59% respondents are male, while 41% are female. This suggests a higher engagements of male students in usage of GPTs. Studies have shown that gender differences AI adoption in education. Male students are more likely to explore and integrate AI tools into their learning process, while female students may exhibit a different approach, it emphasize critical thinking and problem solving (Holmes et al.). GPT tools can enhance creativity and problem solving, but impact on usage of GPT may varies across gender due to differences in technology and usage pattern of GPTs (Kasneci et al, 2023). The female participation suggest that increasing AI adoption among Female students, emphasizing the needs of AI programs to bridge the gap in academic learning of the students. It encourage in diverse perspective in GPT enhanced learning.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGarrent Ranking\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 :\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFactors influencing creativity and idea generation in usage of the GPTs\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"534\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeighted Sum \u0026nbsp; (\u0026Sigma;W)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReduce use of Critical Thinking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.72951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDecision-Making\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.55246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMulti-perspective Problem Solving\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e456\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.74754\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrust Over Personal Judgment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.65902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnhanced Analytical Skills\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.69344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe table highlights key factors influencing creativity and idea generation, with Alignment with goals ranked highest (Mean : 43.72), indicating that creativity where ideas align with the goals is most effective. Exposure to new ideas (Mean : 43.34) ranked second , gives importance of innovation in various perspectives. Collaborative creativity (Mean : 42.13) ranks lower, suggest that potential challenges such as coordination issues and group thinking. The research supports that goal oriented creativity leads to more practical ideas, while exposure to \u0026nbsp; \u0026nbsp; \u0026nbsp; enhances originality (Liu et al., 2024). Collaboration may require structured facilitation to maximum innovation. This suggests that balancing goal alignment, new idea exposure and effective collaboration is optimizing for creative outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelative Importance Index :\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5 : Relative Importance Index for C\u003c/strong\u003e\u003cstrong\u003eritical Thinking and Analytical Skills\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"509\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSl.No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExposure to new ideas\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e43.34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOvercoming creative blocks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e42.69\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlignment with goals\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e43.72\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEfficiency in Brainstroming\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e41.15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCollaborative Creativity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e42.13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe table represents that the Relative Importance Index (RII) for various factors influencing critical thinking and analytical skills. \u003cstrong\u003e\u0026ldquo;Multi-perspective problem solving\u0026rdquo;\u0026nbsp;\u003c/strong\u003eranks highest (RII = 0.74), emphasizing its crucial role for enhancing analytical skills (Paul \u0026amp; elder, 2019). The \u003cstrong\u003e\u0026ldquo;Reduce of Critical Thinking\u0026rdquo; factor ranks second\u0026nbsp;\u003c/strong\u003e(RII = 0.72), suggest concern over GPT reliance potentially diminishing cognitive engagement (Rahwan et al., 2019). \u003cstrong\u003e\u0026ldquo;Enhanced analytical skills\u0026rdquo;\u0026nbsp;\u003c/strong\u003e(RII = 0.69) and \u003cstrong\u003e\u0026ldquo;Trust over judgement\u0026rdquo; (\u003c/strong\u003eRII = 0.65) indicate the role of AI in improving independent reasoning. The lowest ranked factor,\u003cstrong\u003e\u0026ldquo; Decision making\u0026nbsp;\u003c/strong\u003e(RII = 0.55) suggest that AI role in decision making remains constant, with implication for automation (Shin et al.,2022). These result align with concerns about AI shaping human cognition and importance in balanced AI-human collaboration (Muller \u0026amp; Bostrom., 2016).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFactor Analysis\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6 :\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFactors influencing the usage of GPTs\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"557\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactor Loadings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDecision Making\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eSuggestion Verification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u003cstrong\u003e32.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eDecision enhancement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eDecision Dependency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverreliance on GPT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eOver-reliance Limitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 8.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eBrainstroming efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eOrginality Limitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProblem Solving\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eConfidence decline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 6.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eProblem Simplification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnalytical Trust\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eArgument support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;5.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eTraditional Methods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003eTrust Dependence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFactor analysis is statistical technique to identify the underlying relationships between observed variables. It was applied to examine the impact of GPTs on critical thinking, creativity and independent problem solving in academic learning. The objective was to determine whether these cognitive skills are grouped into latent constructs and simplifying the complexity of multiple observed variables into meaningful factors. The analysis involved the use of Exploratory Factor Analysis (EFA) to uncover hidden structures within the data to validate the identified factor structure. The KMO measure and Bartlett test of sphericity is to asses the sampling adequacy and suitability of the data for factor analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe \u003cstrong\u003eKaiser-Meyer \u0026ndash;Olkin (KMO) value is 0.859\u003c/strong\u003e which confirms the sampling adequacy for factor analysis and Bartlett test (p \u0026lt; 0.05) it justify that the use of factor analysis. The scree plot shows a sharp decline in eigenvalues for the first four components, and gradual levelling off, indicate that these four explain the majority variance. This aligns with the Kaiser criterion, suggests components with eigenvalues greater than 1 (Field, 2024). The clear point at the fourth component supports scree tests. This findings is consistent with studies on AI-assisted learning, while critical thinking, over-reliance on AI, problem \u0026ndash;solving and analytical trust emerge as key factors (Zhang \u0026amp; chen., 2024). The four components ensures a balance between interpretability and explanatory power in factor analysis. The factor accounting highest variance in \u003cstrong\u003eDecision making (32.4%)\u003c/strong\u003e suggest that students utilize GPT to enhance their decision making process and develop a dependency on AI- generated suggestions excessive reliance on GPTs may diminish critical thinking and independent judgement (Liu et al.,). \u003cstrong\u003eOverreliance on GPT (8.3%)\u0026nbsp;\u003c/strong\u003eof variance, this highlights that excessive dependence on GPT can improve originality and cognitive engagement. Studies that have shown that overuse of AI tools may limit students ability to think independently and critically (Sandhaus et al., 2024). \u003cstrong\u003eProblem solving (6.8%)\u003c/strong\u003e of variance, this factor suggests that GPTs simplify complex tasks, it may reduce students confidence in problem solving independently. Research on AI assisted learning suggests that students who frequently use AI tools may struggle in problem solving when AI is unavailable (Zhang \u0026amp; Chen., 2024). \u003cstrong\u003eAnalytical trust (5.4%)\u0026nbsp;\u003c/strong\u003e of variance it reflects that students tendency to trust GPT for analytical skills and tasks, which may reduce their own critical thinking abilities. Studies indicate that AI reliance can lead students to accept AI-generated insights without sufficient evaluation, affecting their analytical development (Jones \u0026amp; Brown., 2024).\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThe study highlights the advantages and impact of using Generative Pre-trained Transformers (GPTs) and offers insightful information about how they affect analytical trust, critical thinking and decision making. Important features were found by the factor analysis such as improvements in decision making, the effectiveness of problem solving, over dependence on GPTs and shift in analytical trust. According to the study, GPTs help students to solve problems more quickly and make better decisions by providing a various viewpoints. However they also increases cognitive reliance by decreasing the ability to think critically and independently. The scree plot analysis also indicates the most changes in GPT reliance can be explained by a small number of significant components, which supports the idea that excessive use may impair one\u0026rsquo;s capacity for problem-solving. Previous research has similarly highlighted the risk of automation bias, where users become overly dependent on AI-generated suggestions without sufficient critical evaluation. However, GPTs also serve as valuable cognitive tools when used appropriately, aiding in structuring arguments and simplifying complex Problem-solving tasks. The findings indicate the balanced integration of GPTs in academic and professional settings, ensuring that they function as complementary aids rather than substitutes for independent reasoning. Future research should explore interventions that promote mindful GPT usage while mitigating the risk of overreliance.\u003c/p\u003e \u003cp\u003eThe study concludes that GPTs should be integrated as assistive tools rather than replacements for independent cognitive processes. While AI enhances learning experiences by improving accessibility, creativity and efficiency, the risk of overreliance must be given through educational interventions that encourage critical engagement with AI-generated content. They focus on strategies to mitigate dependency on AI, ensuring that students develop a balanced approach in AI and maintain their analytical and decision-making skills.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAll participants provided informed consent to participate in this study. Where applicable, consent was also obtained for the publication of the findings. The study received approval from the appropriate ethics committee, and in cases where direct consent was not feasible, the need for consent was waived in accordance with committee guidelines.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization \u0026amp; Design: [Jayasuriya R]Data Collection \u0026amp; Analysis: [Jayasuriya R]Methodology Development: [Jayasuriya R]Writing \u0026ndash; Original Draft: [Jayasuriya R]Review \u0026amp; Editing: All authorsSupervision \u0026amp; Critical Insights: [S Selvanayaki]Final Approval: All authors\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eZou, M., and Huang, L. (2023). To use or not to use? Understanding doctoral students\u0026rsquo; acceptance of ChatGPT in writing through technology acceptance model. Front. 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Systematic Review of Research on Artificial Intelligence Applications in Higher Education\u0026mdash;Where are the Educators? Int. J. Educ. Technol. High. Educ. 2019, 16, 39.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eZhai, X. (2023).\u003c/strong\u003e \u0026quot;ChatGPT for Supporting Student Learning: A Review of AI\u0026rsquo;s Role in Education.\u0026quot; \u003cem\u003eEducational Technology \u0026amp; Society, 26\u003c/em\u003e(1), 15-29.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Generative Pre-trained Transformers, Critical Thinking, Academic Learning, AI in Education, Cognitive Skill Development, Problem Solving Skills","lastPublishedDoi":"10.21203/rs.3.rs-6367830/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6367830/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGenerative Pre-trained Transformers (GPTs) are transforming education by enhancing students critical thinking, creativity and decision making processes. However, concerns exist regarding over-reliance and diminished independent analytical thinking. This study investigated the impact of GPT usage among Undergraduate and postgraduate students at Tamil Nadu Agricultural University. A structured questionnaire was employed to collect primary data from 164 students (122 GPT users and 42 non users). The research adopts a descriptive design and utilizes statistical tools such as Percentage analysis, Garrett ranking, Relative Importance index and Exploratory Factor Analysis (EFA) to assess the influence of GPTs on cognitive skills. The findings indicate that the GPTs enhance efficiency, creativity and Problem-solving, they additionaly possess the risk of overreliance, reducing independent analytical thinking. Factor analysis revealed key dimensions including decision making enhancement, overreliance on AI, problem solving impact and analytical trust. This study concludes that balanced AI integration is essential for maximizing GPTs advantages while minimizing dependency. Educational institutions should encourage critical engagement strategies to ensure responsible AI utilization, an optimal AI-human synergy in academic learning.\u003c/p\u003e","manuscriptTitle":"Assessing the Impact of GPTs on Critical thinking, Creativity and Independent Problem solving in Academic learning of students","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-24 05:29:12","doi":"10.21203/rs.3.rs-6367830/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bf2502a2-e48a-492e-8fc0-f15873308a81","owner":[],"postedDate":"April 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-12T17:53:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-24 05:29:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6367830","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6367830","identity":"rs-6367830","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}