ChatGPT Use and Academic Performance among University Students: Moderating Role of Critical Thinking

ChatGPT Use and Academic Performance among University Students: Moderating Role of Critical Thinking | 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 ChatGPT Use and Academic Performance among University Students: Moderating Role of Critical Thinking Niazmeen Razzaq, Najia Zulfiqar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9463352/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 The rapid adoption of artificial intelligence (AI) tools in higher education has raised important questions about their influence on students’ academic outcomes. However, limited research has examined how students’ cognitive characteristics, particularly critical thinking, shape the academic benefits of ChatGPT use. Addressing this limitation, the present study examined the relationships among ChatGPT use, academic performance, and critical thinking, and whether critical thinking moderates these relationships among university students. Using a cross-sectional survey design, data were collected from 300 undergraduate students at the University of XYZ via convenience sampling. Participants completed the ChatGPT Use Scale, the Academic Performance Scale, and the Critical Thinking Questionnaire. Descriptive statistics, Pearson correlations, hierarchical regression, moderation analysis, and independent-samples t tests were conducted. Results showed that ChatGPT use, academic performance, and critical thinking were positively correlated. Critical thinking significantly moderated the relationship between ChatGPT use and academic performance, such that the positive association between ChatGPT use and academic performance became weaker as students’ critical thinking increased. Gender differences were also observed, with boys reporting higher scores on ChatGPT use and academic performance than girls. Implications for AI integration in higher education and directions for future research are discussed. Educational Psychology Academic performance artificial intelligence ChatGPT use critical thinking university students Figures Figure 1 Figure 2 Introduction The integration of Artificial Intelligence (AI) in higher education has enabled students to synthesize information and complete academic tasks in unprecedented ways. One of the popular tools, ChatGPT, with its seamless integration into a constantly evolving academic framework, offers an opportunity to improve academic efficiency and engagement while addressing concerns raised by the phenomena of ‘knowledge hallucinations’ and ‘cognitive offloading’. Knowledge hallucinations, sometimes referred to as AI hallucinations, are situations in which a Large Language Model (LLM) generates an output that is linguistically fluent and coherent but is factually false, illogical, or completely fabricated. Cognitive offloading refers to the use of physical actions or external tools to reduce the mental effort required to complete a task (Gerlich, 2025 ). As a result of these opposing forces, although AI improves students' productivity in the short term, its long-term effectiveness for academic achievement remains variable and largely subject to students' ability to perform critical analysis and edit the generated material (Sullivan et al., 2024 ). ChatGPT Use is defined as the multidimensional, intentional integration of LLMs into academic workflows for generative support, technical assistance, and information synthesis (Taktak & Bafrali, 2025 ). According to Vygotsky's cognitive development theory, ChatGPT serves as a digital "More Knowledgeable Other" to help students move from their current level of ability to their potential level of achievement within their Zone of Proximal Development (ZPD). By using structural supports to address challenging academic work, the AI enables students to complete tasks at a higher intellectual level, thereby improving their overall performance (Su & Yang, 2023 ). Through the lens of Domestication Theory, ChatGPT has evolved from a novelty into a normalized academic tool embedded in daily study habits (Silverstone & Hadan, 1996). A complex interplay between utility and engagement shapes ChatGPT's impact on academic outcomes. Academic performance, which people commonly believe is identical to academic achievement, refers to the comprehensive evaluation of students' learning across all educational activities, measuring their success in achieving specific academic targets and learning outcomes across all their courses. Academic performance reflects how well students learn, and their reliance on AI systems, such as ChatGPT, requires ethical use that expands their learning ability without interfering with their core study activities (Ashraf et al., 2025 ). A growing body of empirical research highlights a significant shift in the educational landscape for enhancing students’ academic performance. Students who use ChatGPT for personal tutoring and the creation of study materials achieve better academic performance and learning outcomes (Naveed et al., 2023). In another study with Pakistani university students, users of ChatGPT generally performed better than peers who did not use it, and learning improved by about triple the usual gain. What made it stand out was not just test scores and higher grades; instead, students began thinking more deeply and questioning ideas in ways they had not before (Shehri et al., 2023 ). Consistent with this, Janse van Rensburg (2024) examined how ChatGPT use positively affected university students' learning and academic performance. He confirmed a high internal consistency of responses and concluded that a positive relationship exists between utilizing ChatGPT and improved academic outcomes. Recent research has focused on the role of genAI in students’ academic performance. Ashraf et al. ( 2025 ) extrapolated that ChatGPT supports learning when students use it as a customized feedback system, helping them better understand concepts and improve their academic outcomes. Alghazo et al. ( 2025 ) investigated university students' opinions on the use of ChatGPT in their education, including its advantages, its impact on academic integrity, and issues related to AI dependency. They showed that different demographic groups used AI in educational settings in distinct ways. The rapid adoption of ChatGPT at universities is now changing academic performance, and universities need to evaluate student progress in developing career skills that require adaptation to rapid technological and AI advancements. Wang et al. ( 2025 ) conducted a meta-analysis of 51 studies to determine the pedagogical value of ChatGPT on student outcomes. They demonstrated that ChatGPT produces significant advantages for student learning, according to the academic consensus, which currently does not exist. Critical thinking, which moderates academic success, is associated with ChatGPT use and academic performance. According to Emmert-Streib ( 2023 ), the most important trait for assessing and authenticating information from all sources and creating new knowledge, including ChatGPT results, is critical thinking, which requires them to strengthen their personal judgment. Usman et al. ( 2024 ) investigated the connection between ChatGPT use and the development of critical thinking skills among 300 university students. The study's findings showed that frequent ChatGPT users experienced a 15% increase in their critical thinking assessment scores because they valued receiving immediate feedback from the AI system. The findings of a mixed-methods study support the claim that students in the experimental group who used ChatGPT for in-class learning tasks exhibited better critical thinking skills than students in the control group who learned in traditional ways (Essel et al., 2024 ). Thus, the process of evaluating artificial intelligence applications in educational settings requires critical thinking to function as its main component. In recent years, the growing emphasis on critical thinking has become a cornerstone of higher education. As the complexity of both academic subjects and real-world challenges increases, critical thinking skills are seen as essential for students to navigate and solve complex problems. While AI is a highly developed technological framework to support academic research and development, it will only be effective in academic contexts if a student transitions from the "passive accepting" phase of using the tool to the "active engaging" or "Type 2" use, where they must carefully examine its output for low-quality or poorly validated information (Selvaraj, 2025 ). In this context, critical thinking acts as a dual force: it is both a direct predictor of academic performance and a vital moderator of how AI use affects student outcomes (Ashraf et al., 2025 ). This happens because critical thinking provides the self-regulation necessary to treat AI as a cognitive partner that actually boosts their performance. Without critical thinking skills, students use ChatGPT as a shortcut to reduce the mental effort required to complete a task, which leads to dependency and lower academic performance (Rivas et al., 2023 ). Ultimately, the difference lies in whether the technology is leading the student or whether the student is using their own insight to lead it. Ultimately, the shift to advanced educational development depends on this planned integration, where critical thinking ensures that technological adoption translates into sustained academic success (Emmert-Streib, 2023 ). Gender Differences Extant research indicates significant gender-based variations in how ChatGPT is adopted and utilized at universities. Mogelvang et al. ( 2024 ) found that men tend to use genAI chatbots more often than women for a wider range of purposes. Examples of these tasks include writing, programming, solving mathematical problems, and testing. Further, show a greater interest in genAI chatbots as tools and in their applicability to future employment opportunities. At the same time, women use genAI chatbots mostly for text-related activities. They express greater concerns about critical and autonomous thinking and show greater interest in understanding when and how to trust genAI chatbots (Mogelvang et al., 2024 ). Another study examined university students' use of ChatGPT for academic purposes and whether there are statistically significant differences in usage by gender. Findings showed that, overall, men used ChatGPT more frequently for academic purposes than women. Particularly, higher ChatGPT use was reported among men in intermediate courses than among those who have just begun or are finishing their studies, and among those enrolled in the undergraduate degree program than in other degrees (Galindo-Domínguez et al., 2024 ). Research shows that boys and girls exhibit different patterns and reactions to genAI chatbots. Bouzar et al. ( 2024 ) found that women used ChatGPT more frequently and expressed more concern about becoming overly dependent on it, while men reported using it for longer periods of time. Both genders considered ChatGPT useful for educational purposes, suggesting its potential as an inclusive learning tool. There was no significant gender difference in ChatGPT acceptance, but differences were observed in usage patterns and concerns about privacy and technological over-reliance. Men found ChatGPT more valuable and easier to use in their education than women (Bouzar et al., 2024 ). Previous research also supports gender differences in the academic performance of undergraduate female university students compared to their male counterparts (Dayioğlu & Türüt-Aşik, 2007 ). In a Pakistani study, researchers investigated teachers' perceptions of the reasons for male students' underperformance and female students' outperformance. Findings showed that students' educational background, socio-economic factors, educational determinants, and parents' cultural preferences influenced their performance in higher education (Shoaib & Ullah, 2026 ). Empirical research also supports a significant difference in critical thinking abilities between male and female university students, with male students performing better (Guo & Lee, 2023 ). The results of these studies reveal that males and females use ChatGPT differently, and their academic performance and critical thinking abilities are shaped differently. The Current Study The rapid adoption of artificial intelligence tools, particularly ChatGPT, has transformed the learning environment in higher education. Some researchers have examined the academic outcomes of AI-assisted learning, but the extent to which students' cognitive abilities influence these outcomes remains insufficiently understood. In particular, it is unclear how students' critical thinking shapes the relationship between ChatGPT use and academic performance, and whether the academic impact of ChatGPT use varies depending on students' levels of critical thinking. To address these research gaps, the present study examines the interrelationships among ChatGPT use, academic performance, and critical thinking among university students. More specifically, it explores how critical thinking moderates the relationship between ChatGPT use and academic performance, and also examines potential gender differences in the study variables. By investigating these relationships, the study contributes to expanding the literature on the use of artificial intelligence in higher education to increase students' academic performance. Understanding how critical thinking skills interact with AI use is essential for developing responsible educational policies and practices. ChatGPT assists students who struggle with critical analysis by helping them bridge learning gaps when completing complex academic tasks. The findings will guide educators and policymakers in developing strategies for responsible AI integration into higher education while discouraging students' dependence on educational technology. The following hypotheses were formulated, aligned with the research objectives: There will be positive interrelationships among ChatGPT use, academic performance, and critical thinking among university students. Critical thinking will moderate the relationship between ChatGPT use and academic performance. There will be significant gender differences in ChatGPT use, student academic performance, and university students' critical thinking. Method This quantitative research used a cross-sectional survey to collect data from university students via self-report questionnaires. This design allows for collecting a large amount of data in a short period and is appropriate for understanding the interrelationships among variables. Participants A total of 300 undergraduate university students (150 boys, 150 girls) were recruited from the University of XYZ using convenience sampling. Participants' ages ranged from 18 to 25 years, with a mean age of 21.45 years (SD = 2.82). Students represented various years of study: First Year ( n = 75, 25%), Second Year ( n = 82, 27.3%), Third Year ( n = 68, 22.7%), and Fourth Year ( n = 75, 25%). The participants’ inclusion criteria were being currently enrolled in any undergraduate program at the University of XYZ. The exclusion criteria were being a graduate or postgraduate student and a diagnosis of any psychological disorder. Measures ChatGPT Use Scale (CUS) Taktak and Bafrali ( 2025 ) developed the ChatGPT Use Scale, a 12-item self-report tool designed to assess attitudes towards using ChatGPT in an educational context. It is a brief yet reliable psychometric instrument. Responses are recorded on a 5-point Likert scale, " totally disagree (1)” to “ strongly agree (5)", ranging from 12 to 60. This format allows for easy administration and interpretation. The scale has been used in academic settings to measure perceptions related to ChatGPT use. The scale demonstrated acceptable internal consistency, with a Cronbach’s alpha of .71 (Taktak & Bafrali, 2025 ). Academic Performance Scale (APS) Birchmeier et al. ( 2015 ) developed the Academic Performance Scale to measure students' academic behaviors, including preparedness, attentiveness, effort on difficult tasks, study habits, and problem-solving. It is a self-report 8-item measure that uses a 5-point scale of " strongly agree " to " strongly disagree. The total score ranges from 8–40, with five further categories: 0–8 (failing); 9–16 (poor); 17–24 (moderate); 25–32 (good); and 33–40 (excellent). This format helps capture nuanced self-assessments of academic performance. It has been widely applied in studies involving student achievement and learning outcomes. It has demonstrated high internal consistency, with a Cronbach's alpha of .85 (Birchmeier et al., 2015 ). Critical Thinking Questionnaire (CThQ) Kobylarek et al. ( 2022 ) designed CThQ to assess cognitive skills based on Bloom's taxonomy. It consists of 25 items across 6 scales: remembering, understanding, applying, analyzing, evaluating, and creating. It uses a 5-point Likert scale from " totally disagree ” to “ strongly agree ", with a range of 25 to 125. It is widely used in educational and cognitive research contexts. Cronbach's Alpha coefficient of the original questionnaire was .87 (Kobylarek et al., 2022 ). Procedure After seeking approval from the Ethical Review Committee of the University of XYZ under the protocol number UOH/DASR/2026/3323, permission was also obtained from the department heads for data collection. The hard-form questionnaires were administered face-to-face to the participants. Informed consent was obtained from all participants, outlining the study's purpose, their rights, and confidentiality. They were allowed to ask freely if they had any questions about any item on the scale. While answering the question, participants had no time limit to answer all the items on the scale. The researchers asked them to answer each question as honestly as possible and ensured confidentiality and the right to withdraw from the study at any time without consequences. They thanked the participants for their cooperation and time. Data storage complied with relevant ethical guidelines. Statistical Analysis Data were checked for normality, linearity, missingness, independence of errors, multicollinearity among variables, and homoscedasticity using SPSS version 26. The negative items were reverse-scored. Next, descriptive statistics, Pearson correlations, hierarchical regression analysis, moderation analysis, and independent-samples t tests were conducted to examine the study hypotheses. ChatGPT and critical thinking scores were mean-centered to create an interaction term. The effect of ChatGPT use on academic performance was examined, and an interaction term was created to explore whether critical thinking strengthens or weakens this relationship. Results The present study examines ChatGPT use as a predictor and critical thinking as a moderator of university students' academic performance. It also explores gender differences in the study variables. Initially, 350 students were approached to participate in the research, and 342 expressed willingness to share their experiences. Following a rigorous screening, the final sample was narrowed to 300 students with complete data, necessitating the removal of 42 incomplete responses. Removing the 12.3% missing data ensured that the analysis was based on reliable data. Table 1 Alpha Reliability and Descriptive Statistics of Study Scales (n = 300) Scales k α M SD Actual Potential Skew Kurt ChatGPT Use 12 .89 37.81 11.57 12–55 12–60 − .76 − .41 Academic Performance 8 .86 15.63 5.17 8–30 8–40 .67 .31 Critical Thinking 25 .87 84.51 15.71 32–121 25–125 -1.97 4.95 Note . n = sample size; k = Number of items; α = Reliability; M = Mean; SD = Standard Deviation; Skew = Skewness; Kurt = Kurtosis. Table 1 presents alpha coefficients and descriptive statistics for the scales utilized in this study. In the present sample, Cronbach’s α was .89 for ChatGPT use, .86 for academic performance, and .87 for critical thinking, indicating high internal consistency across all measures. On average, participants reported moderate use of ChatGPT, relatively low to moderate academic performance, and moderate to high critical thinking. The actual score ranges also verify the respondents’ scores span, enhancing the validity of the measures. Skewness and kurtosis values for each scale were within the acceptable range (-3 to + 3 for skewness and − 10 to + 10 for kurtosis), suggesting a normal distribution. ChatGPT use and critical thinking showed negative skew, while academic performance showed positive skew. Table 2 Correlation Coefficients of ChatGPT Use, Academic Performance, and Critical Thinking 1 Variables 1 2 ChatGPT Use 2 Academic Performance .27** 3 Critical Thinking .47** .13* Note . * p < .05, ** p < .01. Table 2 shows that all three variables were significantly positively correlated. However, there were weak intercorrelations between ChatGPT use and academic performance (r = .27, p < .01) as well as academic performance and critical thinking (r = .13, p < .05). The correlation between ChatGPT use and critical thinking was in the moderate range ( r = .47, p < .01). Table 3 Moderation of Critical Thinking between ChatGPT Use and Academic Performance Models b SE R² Δ R² F df p Model 1 Constant 11.21* .99 ChatGPT Use .12* .03 .07 .07 21.88 1, 298 .00 Model 2 Constant 11.04* 1.60 ChatGPT Use .12* .03 .07 21.88 1, 298 .00 Critical Thinking .04* .02 .02 .02 10.91 2, 297 .00 Model 3 Constant 18.66* 2.55 ChatGPT Use .09* .03 .07 21.88 1, 298 .00 Critical Thinking .07* .03 .09 10.91 2, 297 .00 ChatGPT Use x Critical Thinking 1.03* .27 .11 .04 12.42 3, 296 .00 Note . *p < .05. A hierarchical multiple regression analysis was performed to examine the effects of ChatGPT use and critical thinking on academic performance, and whether critical thinking moderates the relationship between ChatGPT use and academic performance (Table 3 ). In Model 1, ChatGPT use significantly predicted academic performance, explaining 7% of the variance. In Model 2, critical thinking was added. It also significantly predicted academic performance ( B = .04, SE = .02), explaining 9% of the variance which contributed to additional explained variance (Δ R² = .02, F (2,297) = 10.91, p < .05 ). As illustrated in Fig. 1, the standardized regression coefficients of ChatGPT use ( β = .26*, t = 4.06, p = .00) and critical thinking ( β = .13*, t = 2.25, p = .00) on academic performance were significant positive. Figure 1 Moderating Effect of Critical Thinking on the Relationship between ChatGPT Use and Academic Performance Mod Graph for the Low and High Levels of Critical Thinking In Fig. 2 , the mod graph for students with low and high critical thinking shows simple slopes in opposite directions. Students with low critical thinking (-1 SD) have a strong positive slope, whereas students with high critical thinking (+ 1 SD) have a negative slope. This implies that students with lower critical thinking abilities use ChatGPT as a compensatory academic tool and temporarily benefit more than students with high critical thinking abilities. Conversely, students with high levels of critical thinking rely on their analytical thinking skills and benefit less from ChatGPT. Thus, as critical thinking increases, the relationship between ChatGPT and academic performance weakens. Table 4 Gender Differences in ChatGPT Use, Academic Performance, and Critical Thinking (n = 300) Scales Boys ( n = 150) Girls ( n = 150) t (298) P 95% CI Cohen’s d M SD M SD LL UL ChatGPT Use 42.69 7.82 32.93 12.62 8.05 .00 7.38 12.15 .93 Academic Performance 17.06 5.18 14.20 4.76 4.97 .00 .57 1.73 .58 Critical Thinking 85.86 10.62 83.16 19.46 1.49 .00 − .86 6.26 .17 Note . M = Mean, SD = Standard Deviation, CI = Confidence Interval. An independent-samples t-test was conducted to examine gender differences in the study variables. Table 4 shows statistically significant gender differences in ChatGPT use and academic performance, where boys reported significantly higher scores than girls. There was no significant gender difference in critical thinking. These results partially support the third hypothesis. Discussion In recent years, the significance of ChatGPT's use in higher education has attracted increasing attention. Against this backdrop, the current study was designed to explore the moderating role of critical thinking in the relationship between ChatGPT use and academic performance among university students. Consistent with the hypotheses, ChatGPT use positively predicted academic performance, and critical thinking was a significant negative moderator. Most notably, critical thinking had a significant positive direct effect on academic performance; however, this effect became negative when an interaction effect emerged. These findings suggest that the benefits of ChatGPT use depend on students' critical thinking profiles, thereby reducing reliance on ChatGPT for performance gains. The primary contribution of the study is to investigate the interrelationships among ChatGPT use, academic performance, and critical thinking. The findings demonstrate a positive association between ChatGPT use and academic performance. This supports the notion that when students engage with AI tools to generate or organize ideas, clarify complex tasks, and generate responses, their academic performance accelerates through enhanced self-regulated learning and reduced cognitive overload. ChatGPT use has emerged as an academic support system for university students, particularly for demanding scholastic tasks. However, this study examined only one AI tool, i.e., ChatGPT, and explained only a small amount of variance in academic performance. The reason is that academic performance is a multifactorial phenomenon influenced by prior knowledge, demographic and contextual variables, and students' personal characteristics, which were not explored in this study. This finding of a positive association between ChatGPT use and academic performance aligns with existing literature from the Pakistani context (Shehri et al., 2023 ) and with the meta-analysis by Wang et al. ( 2025 ). Critical thinking also positively predicted university students' academic performance. Like ChatGPT, the amount of variance explained was modest, yet the findings show that students' academic performance increases with greater critical thinking skills. These findings suggest that university students can independently learn, solve problems, and adapt to new learning materials when using ChatGPT. They employ their analytical abilities and reasoning to develop an understanding of the AI feedback to their prompts. Frequent use of AI tools to understand issues, challenge assumptions, and construct well-reasoned arguments directly improves grades. Another meaningful contribution of this study is its significant moderating role of critical thinking in the relationship between ChatGPT use and academic performance. ChatGPT use is associated with academic performance at both low and high levels of critical thinking. The negative interaction term indicates that the association between ChatGPT use and academic performance is stronger for students with low levels of critical thinking, and weaker for students with high levels of critical thinking. Students with weak critical thinking skills more frequently use technology without questioning it, compensating for limitations in analytical reasoning, synthesis, or evaluative processing, and thereby, enhancing performance outcomes. Thus, AI tools serve as a compensatory tool for students with low critical thinking skills. Conversely, students with stronger critical thinking skills may rely less on AI-generated assistance and instead depend more on their own analytical abilities. They do not benefit as much from external cognitive aids, as they already possess strong analytical and evaluative abilities. They use ChatGPT as a support system, as AI tools might not provide much additional advantage to enhance their performance. Consequently, they have a marginal benefit from using ChatGPT in achieving superior academic performance. The theory of cognitive offloading helps to interpret this trend, which refers to the strategic use of external tools to reduce internal cognitive demands. This study also provides empirical evidence for the literature on gender differences. Findings show that boys reported significantly higher levels of ChatGPT use than girls. These differences align with Mogelvang et al.'s ( 2024 ) findings and traditional gender socialization theories, suggesting that boys are more inclined to use technology to complete their educational assignments than girls. While analyzing gender-based differences in academic performance, the present study found that boys outperformed girls. In contrast, most previous studies report higher academic performance among girls than among boys, such as Dayioğlu and Türüt-Aşik ( 2007 ). Boys and girls did not differ significantly in their critical thinking scores. This finding is consistent with Marnı et al. ( 2020 ) This research has important theoretical contributions and aligns with the domestication theory and self-regulation theory. Silverstone and Hadan (1996) explain, in their domestication theory, how technologies like ChatGPT are embedded in students' academic routines. As students domesticate ChatGPT for studying, writing, or problem-solving, it begins to influence their critical thinking and academic performance. When integrated reflectively, ChatGPT can enhance critical thinking by providing diverse perspectives, prompting analysis, and supporting deeper understanding. Domestication theory postulates that the social normalization and personal integration of ChatGPT shape whether it becomes a tool for intellectual growth or a form of cognitive dependency in the academic lives of university students (Alghazo et al., 2025 ). Limitations and Future Recommendations First, the current research design limits causal analysis through its cross-sectional framework and one-time cross-sectional data collection. Future longitudinal studies can better understand the technological trajectories of these study variables over time. Second, relying on self-reported measures can introduce biases, such as social desirability and inaccuracies in self-perception. Future studies should incorporate multi-method approaches, including observational data and reports from multiple informants (e.g., parents, teachers, peers) to control for biases. Third, the study's findings lack generalizability because the research used a small sample from a single institutional context. It provides an area- and culture-specific picture of how the ChatGPT use and academic performance are interlinked. Future research should include more diverse samples and locations to increase the applicability of the results across different populations. Fourth, the research faces a major methodological obstacle: it does not measure different types of ChatGPT use, such as brainstorming and problem-solving, nor does it examine how these uses, practices, and patterns affect the development of critical thinking skills among students. Lastly, the overall explained variance remained modest, even though the moderation was statistically significant, suggesting that other moderators (e.g., academic motivation) and mediators (e.g., self-efficacy) should be investigated to refine the understanding of differential AI benefits. Theoretical and Practical Implications This research offers a theoretical implication for the educational literature on AI by demonstrating that the impact of AI, specifically ChatGPT use, is not uniform across university students. Rather, its effectiveness depends on students' cognitive characteristics and use of internal versus external cognitive scaffolds. In other words, students benefit differently from AI technology, depending on individual differences in cognitive capacity. These findings support a shift away from oversimplified "AI improves performance" narratives towards more intricate models that integrate cognitive processes with technological affordances. A major strength of the study lies in the practical implications of its findings. The findings pinpoint a possible developmental issue among students with low critical thinking skills who blindly rely on AI-generated outputs without reflection, thereby jeopardizing their cognitive development. Institutions should encourage technological advancement by blending AI literacy with critical reasoning. Teaching students how to interrogate, refine, and ethically use AI tools will help reduce the risk of technological dependency. Academicians should foster logical reasoning, metacognition, and critical assessment of AI-generated information among students to have a balanced integration of AI with pedagogy. The research results enable policymakers to develop programs and policies to improve students' academic performance by leveraging ChatGPT effectively and critical thinking skills. Conclusion The present study underscores a positive association between ChatGPT use and academic performance among university students, with critical thinking as a significant moderator. However, the academic outcomes of ChatGPT are not uniform across students with varying levels of critical thinking skills. In particular, students with low levels of critical thinking benefit more from AI, suggesting a compensatory role for ChatGPT in academic settings. Gender differences show different patterns, with boys having higher levels of ChatGPT use and academic performance than girls. With increasing reliance on AI technologies, students, teachers, educational administrators, and practitioners must cultivate critical thinking and safeguard cognitive development, ensuring that AI technology complements rather than replaces analytical reasoning. This study offers theoretical and practical implications and adds to a more sophisticated knowledge of AI integration in higher education. 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Sustainability , 15 (2), 1527–1534. https://doi.org/10.3390/su15021527 Shehri, F. A., Maham, R., Malik, A., & Saif, O. B. (2023). Effects of ChatGPT on students’ academic performance: Mediating role of prompt engineering. The Asian Bulletin of Big Data Management , 3 (2), 137–147. https://doi.org/10.62019/abbdm.v3i2.58 Shoaib, M., & Ullah, H. (2026). Gender differentials in academic performance in Pakistani higher education. Educational Research and Evaluation , 31 (1-2), 1–21. https://doi.org/10.1080/13803611.2025.2542582 Silverstone, R., & Haddon, L. (1996). Design and the domestication of information and communication technologies: Technical change and everyday life. In R. Mansell & R. Silverstone (Eds.), Communication by design: The politics of information and communication technologies (pp. 44–74). Oxford University Press. https://doi.org/10.1093/oso/9780198289418.003.0003 Su, J., & Yang, W. (2023). Unlocking the power of ChatGPT: A framework for promoting transformative learning in higher education. Creative Education, 14 (6), 1129–1140. https://doi.org/10.4236/ce.2023.146072 Sullivan, M., Kelly, A., & McLaughlan, P. (2024). ChatGPT in higher education: Considerations for academic integrity and student learning. Journal of Applied Learning and Teaching, 7 (1), 1–15. https://doi.org/10.37074/jalt.2024.7.1.18 Taktak, M., & Bafrali, G. (2025). ChatGPT use scale in education: Validity and reliability study. International Journal of Technology in Education , 8 (1), 193–207. https://doi.org/10.46328/ijte.1024 Usman, A., Agustina, L., & Bahri, A. (2024). Enhancing critical thinking and academic achievement through different learning methods. International Journal of Evaluation and Research in Education, 13 (6), 4271–4311. https://doi.org/10.11591/ijere.v13i6.27993 Wang, J., Alum, J., & Fan, W. (2025).The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: Insights from a meta-analysis. Humanities and Social Sciences Communication, 12 (2), 621-655. https://doi.org/10.1057/s41599-025-04787-y Additional Declarations The authors declare no competing interests. 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9463352","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625761455,"identity":"b7deded9-9e3c-4b17-ab21-ff27705df81d","order_by":0,"name":"Niazmeen Razzaq","email":"","orcid":"","institution":"Department of Psychology, University of Haripur, KPK, Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Niazmeen","middleName":"","lastName":"Razzaq","suffix":""},{"id":625761456,"identity":"595eb2a1-8982-4be3-890e-f796aebcd362","order_by":1,"name":"Najia Zulfiqar","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-0649-7877","institution":"Department of Psychology, University of Haripur, KPK, Pakistan","correspondingAuthor":true,"prefix":"","firstName":"Najia","middleName":"","lastName":"Zulfiqar","suffix":""}],"badges":[],"createdAt":"2026-04-19 15:49:58","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9463352/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9463352/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107446936,"identity":"48378b79-7f59-419d-bb1f-ea5c4b518aca","added_by":"auto","created_at":"2026-04-21 14:53:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57329,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eModerating Effect of Critical Thinking on the Relationship between ChatGPT Use and Academic Performance\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9463352/v1/8e516ee7560550c4fdc2d4da.png"},{"id":107489272,"identity":"482ee737-9c2d-4e92-b594-851575ede4ab","added_by":"auto","created_at":"2026-04-22 02:47:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":32896,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMod Graph for the Low and High Levels of Critical Thinking\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9463352/v1/9eaeea4060b840aa3dd14e38.png"},{"id":107705512,"identity":"0c26b3dc-9fe4-4e3f-abc5-b68c50908754","added_by":"auto","created_at":"2026-04-24 09:13:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":430283,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9463352/v1/eceeb07c-e9b2-4595-9553-35bc880d1519.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eChatGPT Use and Academic Performance among University Students: Moderating Role of Critical Thinking\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe integration of Artificial Intelligence (AI) in higher education has enabled students to synthesize information and complete academic tasks in unprecedented ways. One of the popular tools, ChatGPT, with its seamless integration into a constantly evolving academic framework, offers an opportunity to improve academic efficiency and engagement while addressing concerns raised by the phenomena of \u0026lsquo;knowledge hallucinations\u0026rsquo; and \u0026lsquo;cognitive offloading\u0026rsquo;. Knowledge hallucinations, sometimes referred to as AI hallucinations, are situations in which a Large Language Model (LLM) generates an output that is linguistically fluent and coherent but is factually false, illogical, or completely fabricated. Cognitive offloading refers to the use of physical actions or external tools to reduce the mental effort required to complete a task (Gerlich, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). As a result of these opposing forces, although AI improves students' productivity in the short term, its long-term effectiveness for academic achievement remains variable and largely subject to students' ability to perform critical analysis and edit the generated material (Sullivan et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChatGPT Use is defined as the multidimensional, intentional integration of LLMs into academic workflows for generative support, technical assistance, and information synthesis (Taktak \u0026amp; Bafrali, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). According to Vygotsky's cognitive development theory, ChatGPT serves as a digital \"More Knowledgeable Other\" to help students move from their current level of ability to their potential level of achievement within their Zone of Proximal Development (ZPD). By using structural supports to address challenging academic work, the AI enables students to complete tasks at a higher intellectual level, thereby improving their overall performance (Su \u0026amp; Yang, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Through the lens of Domestication Theory, ChatGPT has evolved from a novelty into a normalized academic tool embedded in daily study habits (Silverstone \u0026amp; Hadan, 1996). A complex interplay between utility and engagement shapes ChatGPT's impact on academic outcomes.\u003c/p\u003e \u003cp\u003eAcademic performance, which people commonly believe is identical to academic achievement, refers to the comprehensive evaluation of students' learning across all educational activities, measuring their success in achieving specific academic targets and learning outcomes across all their courses. Academic performance reflects how well students learn, and their reliance on AI systems, such as ChatGPT, requires ethical use that expands their learning ability without interfering with their core study activities (Ashraf et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA growing body of empirical research highlights a significant shift in the educational landscape for enhancing students\u0026rsquo; academic performance. Students who use ChatGPT for personal tutoring and the creation of study materials achieve better academic performance and learning outcomes (Naveed et al., 2023). In another study with Pakistani university students, users of ChatGPT generally performed better than peers who did not use it, and learning improved by about triple the usual gain. What made it stand out was not just test scores and higher grades; instead, students began thinking more deeply and questioning ideas in ways they had not before (Shehri et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consistent with this, Janse van Rensburg (2024) examined how ChatGPT use positively affected university students' learning and academic performance. He confirmed a high internal consistency of responses and concluded that a positive relationship exists between utilizing ChatGPT and improved academic outcomes.\u003c/p\u003e \u003cp\u003eRecent research has focused on the role of genAI in students\u0026rsquo; academic performance. Ashraf et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) extrapolated that ChatGPT supports learning when students use it as a customized feedback system, helping them better understand concepts and improve their academic outcomes. Alghazo et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) investigated university students' opinions on the use of ChatGPT in their education, including its advantages, its impact on academic integrity, and issues related to AI dependency. They showed that different demographic groups used AI in educational settings in distinct ways. The rapid adoption of ChatGPT at universities is now changing academic performance, and universities need to evaluate student progress in developing career skills that require adaptation to rapid technological and AI advancements. Wang et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) conducted a meta-analysis of 51 studies to determine the pedagogical value of ChatGPT on student outcomes. They demonstrated that ChatGPT produces significant advantages for student learning, according to the academic consensus, which currently does not exist.\u003c/p\u003e \u003cp\u003eCritical thinking, which moderates academic success, is associated with ChatGPT use and academic performance. According to Emmert-Streib (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the most important trait for assessing and authenticating information from all sources and creating new knowledge, including ChatGPT results, is critical thinking, which requires them to strengthen their personal judgment. Usman et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) investigated the connection between ChatGPT use and the development of critical thinking skills among 300 university students. The study's findings showed that frequent ChatGPT users experienced a 15% increase in their critical thinking assessment scores because they valued receiving immediate feedback from the AI system. The findings of a mixed-methods study support the claim that students in the experimental group who used ChatGPT for in-class learning tasks exhibited better critical thinking skills than students in the control group who learned in traditional ways (Essel et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, the process of evaluating artificial intelligence applications in educational settings requires critical thinking to function as its main component.\u003c/p\u003e \u003cp\u003eIn recent years, the growing emphasis on critical thinking has become a cornerstone of higher education. As the complexity of both academic subjects and real-world challenges increases, critical thinking skills are seen as essential for students to navigate and solve complex problems. While AI is a highly developed technological framework to support academic research and development, it will only be effective in academic contexts if a student transitions from the \"passive accepting\" phase of using the tool to the \"active engaging\" or \"Type 2\" use, where they must carefully examine its output for low-quality or poorly validated information (Selvaraj, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this context, critical thinking acts as a dual force: it is both a direct predictor of academic performance and a vital moderator of how AI use affects student outcomes (Ashraf et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This happens because critical thinking provides the self-regulation necessary to treat AI as a cognitive partner that actually boosts their performance. Without critical thinking skills, students use ChatGPT as a shortcut to reduce the mental effort required to complete a task, which leads to dependency and lower academic performance (Rivas et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Ultimately, the difference lies in whether the technology is leading the student or whether the student is using their own insight to lead it. Ultimately, the shift to advanced educational development depends on this planned integration, where critical thinking ensures that technological adoption translates into sustained academic success (Emmert-Streib, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eGender Differences\u003c/h3\u003e\n\u003cp\u003eExtant research indicates significant gender-based variations in how ChatGPT is adopted and utilized at universities. Mogelvang et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that men tend to use genAI chatbots more often than women for a wider range of purposes. Examples of these tasks include writing, programming, solving mathematical problems, and testing. Further, show a greater interest in genAI chatbots as tools and in their applicability to future employment opportunities. At the same time, women use genAI chatbots mostly for text-related activities. They express greater concerns about critical and autonomous thinking and show greater interest in understanding when and how to trust genAI chatbots (Mogelvang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Another study examined university students' use of ChatGPT for academic purposes and whether there are statistically significant differences in usage by gender. Findings showed that, overall, men used ChatGPT more frequently for academic purposes than women. Particularly, higher ChatGPT use was reported among men in intermediate courses than among those who have just begun or are finishing their studies, and among those enrolled in the undergraduate degree program than in other degrees (Galindo-Dom\u0026iacute;nguez et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearch shows that boys and girls exhibit different patterns and reactions to genAI chatbots. Bouzar et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that women used ChatGPT more frequently and expressed more concern about becoming overly dependent on it, while men reported using it for longer periods of time. Both genders considered ChatGPT useful for educational purposes, suggesting its potential as an inclusive learning tool. There was no significant gender difference in ChatGPT acceptance, but differences were observed in usage patterns and concerns about privacy and technological over-reliance. Men found ChatGPT more valuable and easier to use in their education than women (Bouzar et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious research also supports gender differences in the academic performance of undergraduate female university students compared to their male counterparts (Dayioğlu \u0026amp; T\u0026uuml;r\u0026uuml;t-Aşik, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In a Pakistani study, researchers investigated teachers' perceptions of the reasons for male students' underperformance and female students' outperformance. Findings showed that students' educational background, socio-economic factors, educational determinants, and parents' cultural preferences influenced their performance in higher education (Shoaib \u0026amp; Ullah, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Empirical research also supports a significant difference in critical thinking abilities between male and female university students, with male students performing better (Guo \u0026amp; Lee, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The results of these studies reveal that males and females use ChatGPT differently, and their academic performance and critical thinking abilities are shaped differently.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eThe Current Study\u003c/h2\u003e \u003cp\u003eThe rapid adoption of artificial intelligence tools, particularly ChatGPT, has transformed the learning environment in higher education. Some researchers have examined the academic outcomes of AI-assisted learning, but the extent to which students' cognitive abilities influence these outcomes remains insufficiently understood. In particular, it is unclear how students' critical thinking shapes the relationship between ChatGPT use and academic performance, and whether the academic impact of ChatGPT use varies depending on students' levels of critical thinking. To address these research gaps, the present study examines the interrelationships among ChatGPT use, academic performance, and critical thinking among university students. More specifically, it explores how critical thinking moderates the relationship between ChatGPT use and academic performance, and also examines potential gender differences in the study variables. By investigating these relationships, the study contributes to expanding the literature on the use of artificial intelligence in higher education to increase students' academic performance.\u003c/p\u003e \u003cp\u003eUnderstanding how critical thinking skills interact with AI use is essential for developing responsible educational policies and practices. ChatGPT assists students who struggle with critical analysis by helping them bridge learning gaps when completing complex academic tasks. The findings will guide educators and policymakers in developing strategies for responsible AI integration into higher education while discouraging students' dependence on educational technology. The following hypotheses were formulated, aligned with the research objectives:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThere will be positive interrelationships among ChatGPT use, academic performance, and critical thinking among university students.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCritical thinking will moderate the relationship between ChatGPT use and academic performance.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThere will be significant gender differences in ChatGPT use, student academic performance, and university students' critical thinking.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Method","content":"\u003cp\u003eThis quantitative research used a cross-sectional survey to collect data from university students via self-report questionnaires. This design allows for collecting a large amount of data in a short period and is appropriate for understanding the interrelationships among variables.\u003c/p\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eA total of 300 undergraduate university students (150 boys, 150 girls) were recruited from the University of XYZ using convenience sampling. Participants' ages ranged from 18 to 25 years, with a mean age of 21.45 years (SD\u0026thinsp;=\u0026thinsp;2.82). Students represented various years of study: First Year (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;75, 25%), Second Year (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;82, 27.3%), Third Year (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;68, 22.7%), and Fourth Year (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;75, 25%). The participants\u0026rsquo; inclusion criteria were being currently enrolled in any undergraduate program at the University of XYZ. The exclusion criteria were being a graduate or postgraduate student and a diagnosis of any psychological disorder.\u003c/p\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eChatGPT Use Scale (CUS)\u003c/h2\u003e \u003cp\u003eTaktak and Bafrali (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) developed the ChatGPT Use Scale, a 12-item self-report tool designed to assess attitudes towards using ChatGPT in an educational context. It is a brief yet reliable psychometric instrument. Responses are recorded on a 5-point Likert scale, \"\u003cem\u003etotally disagree\u003c/em\u003e (1)\u0026rdquo; to \u0026ldquo;\u003cem\u003estrongly agree\u003c/em\u003e (5)\", ranging from 12 to 60. This format allows for easy administration and interpretation. The scale has been used in academic settings to measure perceptions related to ChatGPT use. The scale demonstrated acceptable internal consistency, with a Cronbach\u0026rsquo;s alpha of .71 (Taktak \u0026amp; Bafrali, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAcademic Performance Scale (APS)\u003c/h2\u003e \u003cp\u003eBirchmeier et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) developed the Academic Performance Scale to measure students' academic behaviors, including preparedness, attentiveness, effort on difficult tasks, study habits, and problem-solving. It is a self-report 8-item measure that uses a 5-point scale of \"\u003cem\u003estrongly agree\u003c/em\u003e\" to \"\u003cem\u003estrongly disagree.\u003c/em\u003e The total score ranges from 8\u0026ndash;40, with five further categories: 0\u0026ndash;8 (failing); 9\u0026ndash;16 (poor); 17\u0026ndash;24 (moderate); 25\u0026ndash;32 (good); and 33\u0026ndash;40 (excellent). This format helps capture nuanced self-assessments of academic performance. It has been widely applied in studies involving student achievement and learning outcomes. It has demonstrated high internal consistency, with a Cronbach's alpha of .85 (Birchmeier et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCritical Thinking Questionnaire (CThQ)\u003c/h3\u003e\n\u003cp\u003eKobylarek et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) designed CThQ to assess cognitive skills based on Bloom's taxonomy. It consists of 25 items across 6 scales: remembering, understanding, applying, analyzing, evaluating, and creating. It uses a 5-point Likert scale from \"\u003cem\u003etotally disagree\u003c/em\u003e\u0026rdquo; to \u0026ldquo;\u003cem\u003estrongly agree\u003c/em\u003e\", with a range of 25 to 125. It is widely used in educational and cognitive research contexts. Cronbach's Alpha coefficient of the original questionnaire was .87 (Kobylarek et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003eAfter seeking approval from the Ethical Review Committee of the University of XYZ under the protocol number UOH/DASR/2026/3323, permission was also obtained from the department heads for data collection. The hard-form questionnaires were administered face-to-face to the participants. Informed consent was obtained from all participants, outlining the study's purpose, their rights, and confidentiality. They were allowed to ask freely if they had any questions about any item on the scale. While answering the question, participants had no time limit to answer all the items on the scale. The researchers asked them to answer each question as honestly as possible and ensured confidentiality and the right to withdraw from the study at any time without consequences. They thanked the participants for their cooperation and time. Data storage complied with relevant ethical guidelines.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData were checked for normality, linearity, missingness, independence of errors, multicollinearity among variables, and homoscedasticity using SPSS version 26. The negative items were reverse-scored. Next, descriptive statistics, Pearson correlations, hierarchical regression analysis, moderation analysis, and independent-samples \u003cem\u003et\u003c/em\u003e tests were conducted to examine the study hypotheses. ChatGPT and critical thinking scores were mean-centered to create an interaction term. The effect of ChatGPT use on academic performance was examined, and an interaction term was created to explore whether critical thinking strengthens or weakens this relationship.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe present study examines ChatGPT use as a predictor and critical thinking as a moderator of university students' academic performance. It also explores gender differences in the study variables. Initially, 350 students were approached to participate in the research, and 342 expressed willingness to share their experiences. Following a rigorous screening, the final sample was narrowed to 300 students with complete data, necessitating the removal of 42 incomplete responses. Removing the 12.3% missing data ensured that the analysis was based on reliable data.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eAlpha Reliability and Descriptive Statistics of Study Scales (n\u0026thinsp;=\u0026thinsp;300)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScales\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ek\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eα\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eActual\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePotential\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSkew\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eKurt\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChatGPT Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u0026ndash;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic Performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCritical Thinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32\u0026ndash;121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25\u0026ndash;125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eNote\u003c/em\u003e. \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;sample size; \u003cem\u003ek\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Number of items; \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Reliability; \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Mean; \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Standard Deviation; \u003cem\u003eSkew\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Skewness; \u003cem\u003eKurt\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Kurtosis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents alpha coefficients and descriptive statistics for the scales utilized in this study. In the present sample, Cronbach\u0026rsquo;s α was .89 for ChatGPT use, .86 for academic performance, and .87 for critical thinking, indicating high internal consistency across all measures. On average, participants reported moderate use of ChatGPT, relatively low to moderate academic performance, and moderate to high critical thinking. The actual score ranges also verify the respondents\u0026rsquo; scores span, enhancing the validity of the measures. Skewness and kurtosis values for each scale were within the acceptable range (-3 to +\u0026thinsp;3 for skewness and \u0026minus;\u0026thinsp;10 to +\u0026thinsp;10 for kurtosis), suggesting a normal distribution. ChatGPT use and critical thinking showed negative skew, while academic performance showed positive skew.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eCorrelation Coefficients of ChatGPT Use, Academic Performance, and Critical Thinking\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChatGPT Use\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcademic Performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.27**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCritical Thinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.47**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.13*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote\u003c/em\u003e. *\u003cem\u003ep\u003c/em\u003e \u0026lt; .05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; .01.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that all three variables were significantly positively correlated. However, there were weak intercorrelations between ChatGPT use and academic performance \u003cem\u003e(r\u003c/em\u003e = .27, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01) as well as academic performance and critical thinking \u003cem\u003e(r\u003c/em\u003e = .13, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05). The correlation between ChatGPT use and critical thinking was in the moderate range (\u003cem\u003er\u003c/em\u003e = .47, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eModeration of Critical Thinking between ChatGPT Use and Academic Performance\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eb\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eR\u0026sup2;\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eΔ\u003cem\u003eR\u0026sup2;\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.21*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChatGPT Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.12*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1, 298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.04*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChatGPT Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.12*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1, 298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCritical Thinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.04*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2, 297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.66*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChatGPT Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.09*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1, 298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCritical Thinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.07*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2, 297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChatGPT Use x Critical Thinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3, 296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eNote\u003c/em\u003e. \u003cem\u003e*p\u003c/em\u003e \u0026lt; .05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA hierarchical multiple regression analysis was performed to examine the effects of ChatGPT use and critical thinking on academic performance, and whether critical thinking moderates the relationship between ChatGPT use and academic performance (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In Model 1, ChatGPT use significantly predicted academic performance, explaining 7% of the variance. In Model 2, critical thinking was added. It also significantly predicted academic performance (\u003cem\u003eB\u003c/em\u003e = .04, \u003cem\u003eSE\u003c/em\u003e = .02), explaining 9% of the variance which contributed to additional explained variance (Δ\u003cem\u003eR\u0026sup2;\u003c/em\u003e = .02, \u003cem\u003eF\u003c/em\u003e(2,297)\u0026thinsp;=\u0026thinsp;10.91, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05 ). As illustrated in Fig.\u0026nbsp;1, the standardized regression coefficients of ChatGPT use (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.26*, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.06, \u003cem\u003ep\u003c/em\u003e = .00) and critical thinking (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.13*, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.25, \u003cem\u003ep\u003c/em\u003e = .00) on academic performance were significant positive.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1\u003c/b\u003e \u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eModerating Effect of Critical Thinking on the Relationship between ChatGPT Use and Academic Performance\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMod Graph for the Low and High Levels of Critical Thinking\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the mod graph for students with low and high critical thinking shows simple slopes in opposite directions. Students with low critical thinking (-1 SD) have a strong positive slope, whereas students with high critical thinking (+\u0026thinsp;1 SD) have a negative slope. This implies that students with lower critical thinking abilities use ChatGPT as a compensatory academic tool and temporarily benefit more than students with high critical thinking abilities. Conversely, students with high levels of critical thinking rely on their analytical thinking skills and benefit less from ChatGPT. Thus, as critical thinking increases, the relationship between ChatGPT and academic performance weakens.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eGender Differences in ChatGPT Use, Academic Performance, and Critical Thinking (n\u0026thinsp;=\u0026thinsp;300)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eScales\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eBoys\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eGirls\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e(298)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCohen\u0026rsquo;s\u003c/p\u003e \u003cp\u003e\u003cem\u003ed\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eLL\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eUL\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChatGPT Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e12.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic Performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCritical Thinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cem\u003eNote\u003c/em\u003e. \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Mean, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Standard Deviation, \u003cem\u003eCI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Confidence Interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAn independent-samples t-test was conducted to examine gender differences in the study variables. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows statistically significant gender differences in ChatGPT use and academic performance, where boys reported significantly higher scores than girls. There was no significant gender difference in critical thinking. These results partially support the third hypothesis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn recent years, the significance of ChatGPT's use in higher education has attracted increasing attention. Against this backdrop, the current study was designed to explore the moderating role of critical thinking in the relationship between ChatGPT use and academic performance among university students. Consistent with the hypotheses, ChatGPT use positively predicted academic performance, and critical thinking was a significant negative moderator. Most notably, critical thinking had a significant positive direct effect on academic performance; however, this effect became negative when an interaction effect emerged. These findings suggest that the benefits of ChatGPT use depend on students' critical thinking profiles, thereby reducing reliance on ChatGPT for performance gains.\u003c/p\u003e \u003cp\u003eThe primary contribution of the study is to investigate the interrelationships among ChatGPT use, academic performance, and critical thinking. The findings demonstrate a positive association between ChatGPT use and academic performance. This supports the notion that when students engage with AI tools to generate or organize ideas, clarify complex tasks, and generate responses, their academic performance accelerates through enhanced self-regulated learning and reduced cognitive overload. ChatGPT use has emerged as an academic support system for university students, particularly for demanding scholastic tasks. However, this study examined only one AI tool, i.e., ChatGPT, and explained only a small amount of variance in academic performance. The reason is that academic performance is a multifactorial phenomenon influenced by prior knowledge, demographic and contextual variables, and students' personal characteristics, which were not explored in this study. This finding of a positive association between ChatGPT use and academic performance aligns with existing literature from the Pakistani context (Shehri et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and with the meta-analysis by Wang et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCritical thinking also positively predicted university students' academic performance. Like ChatGPT, the amount of variance explained was modest, yet the findings show that students' academic performance increases with greater critical thinking skills. These findings suggest that university students can independently learn, solve problems, and adapt to new learning materials when using ChatGPT. They employ their analytical abilities and reasoning to develop an understanding of the AI feedback to their prompts. Frequent use of AI tools to understand issues, challenge assumptions, and construct well-reasoned arguments directly improves grades.\u003c/p\u003e \u003cp\u003eAnother meaningful contribution of this study is its significant moderating role of critical thinking in the relationship between ChatGPT use and academic performance. ChatGPT use is associated with academic performance at both low and high levels of critical thinking. The negative interaction term indicates that the association between ChatGPT use and academic performance is stronger for students with low levels of critical thinking, and weaker for students with high levels of critical thinking. Students with weak critical thinking skills more frequently use technology without questioning it, compensating for limitations in analytical reasoning, synthesis, or evaluative processing, and thereby, enhancing performance outcomes. Thus, AI tools serve as a compensatory tool for students with low critical thinking skills.\u003c/p\u003e \u003cp\u003eConversely, students with stronger critical thinking skills may rely less on AI-generated assistance and instead depend more on their own analytical abilities. They do not benefit as much from external cognitive aids, as they already possess strong analytical and evaluative abilities. They use ChatGPT as a support system, as AI tools might not provide much additional advantage to enhance their performance. Consequently, they have a marginal benefit from using ChatGPT in achieving superior academic performance. The theory of cognitive offloading helps to interpret this trend, which refers to the strategic use of external tools to reduce internal cognitive demands.\u003c/p\u003e \u003cp\u003eThis study also provides empirical evidence for the literature on gender differences. Findings show that boys reported significantly higher levels of ChatGPT use than girls. These differences align with Mogelvang et al.'s (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) findings and traditional gender socialization theories, suggesting that boys are more inclined to use technology to complete their educational assignments than girls. While analyzing gender-based differences in academic performance, the present study found that boys outperformed girls. In contrast, most previous studies report higher academic performance among girls than among boys, such as Dayioğlu and T\u0026uuml;r\u0026uuml;t-Aşik (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Boys and girls did not differ significantly in their critical thinking scores. This finding is consistent with Marnı et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThis research has important theoretical contributions and aligns with the domestication theory and self-regulation theory. Silverstone and Hadan (1996) explain, in their domestication theory, how technologies like ChatGPT are embedded in students' academic routines. As students domesticate ChatGPT for studying, writing, or problem-solving, it begins to influence their critical thinking and academic performance. When integrated reflectively, ChatGPT can enhance critical thinking by providing diverse perspectives, prompting analysis, and supporting deeper understanding. Domestication theory postulates that the social normalization and personal integration of ChatGPT shape whether it becomes a tool for intellectual growth or a form of cognitive dependency in the academic lives of university students (Alghazo et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Future Recommendations\u003c/h2\u003e \u003cp\u003eFirst, the current research design limits causal analysis through its cross-sectional framework and one-time cross-sectional data collection. Future longitudinal studies can better understand the technological trajectories of these study variables over time.\u003c/p\u003e \u003cp\u003eSecond, relying on self-reported measures can introduce biases, such as social desirability and inaccuracies in self-perception. Future studies should incorporate multi-method approaches, including observational data and reports from multiple informants (e.g., parents, teachers, peers) to control for biases.\u003c/p\u003e \u003cp\u003eThird, the study's findings lack generalizability because the research used a small sample from a single institutional context. It provides an area- and culture-specific picture of how the ChatGPT use and academic performance are interlinked. Future research should include more diverse samples and locations to increase the applicability of the results across different populations.\u003c/p\u003e \u003cp\u003eFourth, the research faces a major methodological obstacle: it does not measure different types of ChatGPT use, such as brainstorming and problem-solving, nor does it examine how these uses, practices, and patterns affect the development of critical thinking skills among students.\u003c/p\u003e \u003cp\u003eLastly, the overall explained variance remained modest, even though the moderation was statistically significant, suggesting that other moderators (e.g., academic motivation) and mediators (e.g., self-efficacy) should be investigated to refine the understanding of differential AI benefits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eTheoretical and Practical Implications\u003c/h2\u003e \u003cp\u003eThis research offers a theoretical implication for the educational literature on AI by demonstrating that the impact of AI, specifically ChatGPT use, is not uniform across university students. Rather, its effectiveness depends on students' cognitive characteristics and use of internal versus external cognitive scaffolds. In other words, students benefit differently from AI technology, depending on individual differences in cognitive capacity. These findings support a shift away from oversimplified \"AI improves performance\" narratives towards more intricate models that integrate cognitive processes with technological affordances.\u003c/p\u003e \u003cp\u003eA major strength of the study lies in the practical implications of its findings. The findings pinpoint a possible developmental issue among students with low critical thinking skills who blindly rely on AI-generated outputs without reflection, thereby jeopardizing their cognitive development. Institutions should encourage technological advancement by blending AI literacy with critical reasoning. Teaching students how to interrogate, refine, and ethically use AI tools will help reduce the risk of technological dependency. Academicians should foster logical reasoning, metacognition, and critical assessment of AI-generated information among students to have a balanced integration of AI with pedagogy. The research results enable policymakers to develop programs and policies to improve students' academic performance by leveraging ChatGPT effectively and critical thinking skills.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study underscores a positive association between ChatGPT use and academic performance among university students, with critical thinking as a significant moderator. However, the academic outcomes of ChatGPT are not uniform across students with varying levels of critical thinking skills. In particular, students with low levels of critical thinking benefit more from AI, suggesting a compensatory role for ChatGPT in academic settings. Gender differences show different patterns, with boys having higher levels of ChatGPT use and academic performance than girls. With increasing reliance on AI technologies, students, teachers, educational administrators, and practitioners must cultivate critical thinking and safeguard cognitive development, ensuring that AI technology complements rather than replaces analytical reasoning. This study offers theoretical and practical implications and adds to a more sophisticated knowledge of AI integration in higher education.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlghazo, R., Fatima, G., Malik, M., Abdelhamid, S. E., Jahanzaib, M., Nayab, D., \u0026amp; Raza, A. (2025). 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(2026). Gender differentials in academic performance in Pakistani higher education. \u003cem\u003eEducational Research and Evaluation\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(1-2), 1\u0026ndash;21. https://doi.org/10.1080/13803611.2025.2542582\u003c/li\u003e\n\u003cli\u003eSilverstone, R., \u0026amp; Haddon, L. (1996). Design and the domestication of information and communication technologies: Technical change and everyday life. In R. Mansell \u0026amp; R. Silverstone (Eds.), \u003cem\u003eCommunication by design: The politics of information and communication technologies\u003c/em\u003e (pp. 44\u0026ndash;74). Oxford University Press. https://doi.org/10.1093/oso/9780198289418.003.0003\u003c/li\u003e\n\u003cli\u003eSu, J., \u0026amp; Yang, W. (2023). 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Enhancing critical thinking and academic achievement through different learning methods. \u003cem\u003eInternational Journal of Evaluation and Research in Education, 13\u003c/em\u003e(6), 4271\u0026ndash;4311. https://doi.org/10.11591/ijere.v13i6.27993\u003c/li\u003e\n\u003cli\u003eWang, J., Alum, J., \u0026amp; Fan, W. (2025).The effect of ChatGPT on students\u0026rsquo; learning performance, learning perception, and higher-order thinking: Insights from a meta-analysis. \u003cem\u003eHumanities and Social Sciences Communication, 12\u003c/em\u003e(2), 621-655. https://doi.org/10.1057/s41599-025-04787-y\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Haripur, KPK Pakistan.","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":"Academic performance, artificial intelligence, ChatGPT use, critical thinking, university students","lastPublishedDoi":"10.21203/rs.3.rs-9463352/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9463352/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid adoption of artificial intelligence (AI) tools in higher education has raised important questions about their influence on students\u0026rsquo; academic outcomes. However, limited research has examined how students\u0026rsquo; cognitive characteristics, particularly critical thinking, shape the academic benefits of ChatGPT use. Addressing this limitation, the present study examined the relationships among ChatGPT use, academic performance, and critical thinking, and whether critical thinking moderates these relationships among university students. Using a cross-sectional survey design, data were collected from 300 undergraduate students at the University of XYZ via convenience sampling. Participants completed the ChatGPT Use Scale, the Academic Performance Scale, and the Critical Thinking Questionnaire. Descriptive statistics, Pearson correlations, hierarchical regression, moderation analysis, and independent-samples \u003cem\u003et\u003c/em\u003e tests were conducted. Results showed that ChatGPT use, academic performance, and critical thinking were positively correlated. Critical thinking significantly moderated the relationship between ChatGPT use and academic performance, such that the positive association between ChatGPT use and academic performance became weaker as students\u0026rsquo; critical thinking increased. Gender differences were also observed, with boys reporting higher scores on ChatGPT use and academic performance than girls. Implications for AI integration in higher education and directions for future research are discussed.\u003c/p\u003e","manuscriptTitle":"ChatGPT Use and Academic Performance among University Students: Moderating Role of Critical Thinking","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 14:53:05","doi":"10.21203/rs.3.rs-9463352/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":"ceeb1922-4b2d-4895-9286-73d9df8accc5","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":66601508,"name":"Educational Psychology"}],"tags":[],"updatedAt":"2026-04-21T14:53:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 14:53:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9463352","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9463352","identity":"rs-9463352","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}