View in EDS HTML Full Text PDF Full Text

Who Is Hooked on AI? The Role of the Big Five Personality Traits in Compulsive ChatGPT Use among Chinese Students

Saved in:
Bibliographic Details
Title: Who Is Hooked on AI? The Role of the Big Five Personality Traits in Compulsive ChatGPT Use among Chinese Students
Language: English
Authors: Yinyao Hu (ORCID 0009-0004-2868-2274), Chee-Seng Tan (ORCID 0000-0003-2474-6942), Shimeng Wang (ORCID 0009-0006-8802-4200), Hanyun Zhang (ORCID 0009-0003-0256-0271), Jiahui Qian (ORCID 0009-0002-9707-3910), Yihan Wang (ORCID 0009-0003-9418-5658)
Source: Asia-Pacific Education Researcher. 2025 34(5):1899-1907.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 9
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Artificial Intelligence, Technology Uses in Education, Personality Traits, Foreign Countries, College Students, Neurosis, Extraversion Introversion
Geographic Terms: China
DOI: 10.1007/s40299-025-01001-0
ISSN: 0119-5646
2243-7908
Abstract: The rapid growth of artificial intelligence (AI) technologies, such as ChatGPT, presents both opportunities and challenges. While some studies suggest that AI use can be beneficial, others have identified detrimental effects on performance. Drawing on literature that explores the relationship between personality traits and compulsive technology use, this study investigated whether the Big Five personality traits contribute to compulsive ChatGPT use. In this cross-sectional study, 207 university students in China completed a survey measuring compulsive ChatGPT use, Big Five personality traits, and demographic information. Hierarchical multiple regression analysis revealed that compulsive ChatGPT use was positively associated with neuroticism and negatively associated with agreeableness, after controlling for age. No significant relationships were found between compulsive ChatGPT use and openness, conscientiousness, or extraversion. This study provides empirical evidence of the role of personality traits in compulsive ChatGPT use. The findings may help educators identify students at risk for compulsive ChatGPT use, particularly those high in neuroticism, and suggest directions for potential interventions.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1484459
Database: ERIC
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
    Url: https://content.ebscohost.com/cds/retrieve?content=AQICAHj0k_4E0hTGH8RJwT4gCJyBsGNe_WN95AvKlDbXJGqwxwFMFvE_DzcMf4OsctcItZ_-AAAA4zCB4AYJKoZIhvcNAQcGoIHSMIHPAgEAMIHJBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDJp1ytM0ZIFVpEeeaQIBEICBm8oXrKQa6hNN7F3-GUqY1uWQgj6Xu1OQB2PfeB6bKscVMtFENw2il4YtL-Dzh_s3AUCckBupdv-OE-NOuvLKCO6jyRYrNMbNqGrQ4Y5ud_EKV0w8v4vbMS2twJsEfQtikMt70hXOmeQqli9kQzRwPBBS6-c3BexCqddNGaH3JwrK3Qq7lpNaLalf4hNjN91ZbP6Fkfggy5X68Ip0
Text:
  Availability: 1
  Value: <anid>AN0188150135;[gchw]01oct.25;2025Sep25.05:40;v2.2.500</anid> <title id="AN0188150135-1">Who is Hooked on AI? The Role of the Big Five Personality Traits in Compulsive ChatGPT Use Among Chinese Students </title> <p>The rapid growth of artificial intelligence (AI) technologies, such as ChatGPT, presents both opportunities and challenges. While some studies suggest that AI use can be beneficial, others have identified detrimental effects on performance. Drawing on literature that explores the relationship between personality traits and compulsive technology use, this study investigated whether the Big Five personality traits contribute to compulsive ChatGPT use. In this cross-sectional study, 207 university students in China completed a survey measuring compulsive ChatGPT use, Big Five personality traits, and demographic information. Hierarchical multiple regression analysis revealed that compulsive ChatGPT use was positively associated with neuroticism and negatively associated with agreeableness, after controlling for age. No significant relationships were found between compulsive ChatGPT use and openness, conscientiousness, or extraversion. This study provides empirical evidence of the role of personality traits in compulsive ChatGPT use. The findings may help educators identify students at risk for compulsive ChatGPT use, particularly those high in neuroticism, and suggest directions for potential interventions.</p> <p>Keywords: Artificial intelligence; Big five personality trait; Compulsive use; Technology addiction; University students</p> <p>Copyright comment Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</p> <hd id="AN0188150135-2">Introduction</hd> <p>The rise of AI technologies such as ChatGPT has both bright and dark sides in educational practices. The use of ChatGPT offers benefits by enabling students to adjust their learning pace and providing continuous support throughout the learning process, thereby enhancing the overall effectiveness of their educational experience (Montenegro-Rueda et al., [<reflink idref="bib32" id="ref1">32</reflink>]). On the other hand, students have increasingly relied on ChatGPT to write assignments and academic essays and respond to exam questions (Barrot, [<reflink idref="bib4" id="ref2">4</reflink>]; Michel-Villarreal et al., [<reflink idref="bib31" id="ref3">31</reflink>]). Duong, Ngo, et al. ([<reflink idref="bib9" id="ref4">9</reflink>]) introduced the term <emph>compulsive ChatGPT use</emph> to explore the potential consequences of this problematic behavior. It involves excessive use of ChatGPT and is associated with symptoms that significantly interfere with users' daily routines. These include constant preoccupation with ChatGPT, a growing tolerance requiring more frequent use to achieve satisfaction, withdrawal symptoms when access is restricted and an inability to control the compulsion to stop using ChatGPT. In their study involving 2,709 higher education students, Duong, Vu, et al. ([<reflink idref="bib9" id="ref5">9</reflink>]) found that compulsive use of ChatGPT exacerbated social avoidance, loneliness, and psychological distress. Moreover, psychological distress was shown to impair students' life satisfaction and academic performance. Therefore, it is essential to explore personal factors contributing to compulsive ChatGPT use to identify students at risk and prevent them from experiencing these negative impacts.</p> <p>According to Hirschman ([<reflink idref="bib15" id="ref6">15</reflink>]), personality traits are a crucial antecedent factor of compulsive behaviors. Indeed, studies have shown that personality traits are related to compulsive use of technologies, such as mobile applications (Hsiao, [<reflink idref="bib18" id="ref7">18</reflink>]), YouTube (Klobas et al., [<reflink idref="bib25" id="ref8">25</reflink>]), Facebook (Hwang, [<reflink idref="bib20" id="ref9">20</reflink>]), and problematic social media use (Meynadier et al., [<reflink idref="bib30" id="ref10">30</reflink>]). At the same time, there is a significant association between personality traits and people's attitudes toward AI (Kaya et al., [<reflink idref="bib23" id="ref11">23</reflink>]). Therefore, it is reasonable to assume that personality traits are related to compulsive ChatGPT use. The present study aims to test this hypothetical relationship. Specifically, we focused on the Big Five model of personality to understand individual differences in compulsive use of ChatGPT among university students in China.</p> <hd id="AN0188150135-3">The Big Five Personality Traits</hd> <p>The Big Five model of personality has been extensively validated in diverse cultural contexts, including China, where studies have demonstrated its applicability in capturing key personality traits among Chinese individuals (e.g., Zhang et al., [<reflink idref="bib49" id="ref12">49</reflink>], [<reflink idref="bib48" id="ref13">48</reflink>]). The model is a widely recognized framework that categorizes human personality traits into five broad dimensions: openness, conscientiousness, extraversion, agreeableness, and neuroticism (Costa & McCrae, [<reflink idref="bib7" id="ref14">7</reflink>]). These traits provide a comprehensive overview of individual differences in behaviors, thought patterns, and emotions.</p> <p>Openness to experience is characterized by a tendency to be imaginative and curious (Costa & McCrae, [<reflink idref="bib7" id="ref15">7</reflink>]). Openness to experience is positively related with curiosity (Tan et al., [<reflink idref="bib40" id="ref16">40</reflink>]) and technology adoption (Tripathi et al., [<reflink idref="bib44" id="ref17">44</reflink>]). In other words, users with high openness to experience tend to explore and try technologies to satisfy their curiosity. Moreover, openness to experience also has a close association with the compulsive use of technology. For instance, Nwufo and Ike ([<reflink idref="bib34" id="ref18">34</reflink>]) found that openness to experience is positively correlated with internet addiction. The abundant online world is more appealing to those users with a high level of curiosity and high openness, which increases the likelihood of compulsive use of the internet (Rahmani & Lavasani, [<reflink idref="bib36" id="ref19">36</reflink>]).</p> <p>People high in conscientiousness are organized and diligent (Costa & McCrae, [<reflink idref="bib7" id="ref20">7</reflink>]). Conscientiousness has been found to negatively influence impulsivity through enhancing self-control (Mao et al., [<reflink idref="bib28" id="ref21">28</reflink>]). In other words, individuals with lower levels of conscientiousness often struggle to regulate their thoughts and behaviors, which can increase their susceptibility to compulsive behaviors. Lachmann et al. ([<reflink idref="bib26" id="ref22">26</reflink>]) found that conscientiousness is negatively related to internet and smartphone addiction. Individuals who are highly conscientious are better able to control their smartphone usage and avoid overuse.</p> <p>Extraversion refers to the disposition of an individual to search for social interaction, be engaged with the external world, and draw energy from the company of others. Individuals who are high in extraversion tend to be outgoing, talkative, and assertive, whereas individuals low in this trait are more reserved and introspective (Costa & McCrae, [<reflink idref="bib7" id="ref23">7</reflink>]). Extraversion has been found to have a positive relationship with the use and engagement of online social networking sites (Gosling et al., [<reflink idref="bib14" id="ref24">14</reflink>]). This suggests that individuals with high levels of extraversion are increasingly using technology to socialize and fulfill their need for social interaction. Their high use of technology increases the likelihood of compulsive technology use. In line with this, Hsiao et al. ([<reflink idref="bib19" id="ref25">19</reflink>]) conducted an online questionnaire survey on 546 mobile social and game application users in Taiwan and found that extroversion has a positive relationship with the compulsive use of social apps.</p> <p>Agreeableness reflects the tendency of a person to show compassion, cooperation, and harmony in social interactions. Individuals high in agreeableness are thereby tender minded, trustworthy, and avoid conflict, while less agreeable persons are those who can become more competitive and judgmental in interpersonal relationships (Costa & McCrae, [<reflink idref="bib7" id="ref26">7</reflink>]). Moreover, agreeableness is associated with a lower likelihood of real-life social isolation (Whaite et al., [<reflink idref="bib47" id="ref27">47</reflink>]). Individuals with high agreeableness are more focused on maintaining positive social interactions and relationships (Kallianou, [<reflink idref="bib22" id="ref28">22</reflink>]). Because of this, they are less likely to seek out technology as an escape or a way to replace real-life connections. Instead, they are more likely to engage in face-to-face communication and build meaningful relationships, which reduces their need for excessive or compulsive technology use. Research has demonstrated a negative relationship between agreeableness and compulsive internet use and internet addiction (Andreassen et al., [<reflink idref="bib1" id="ref29">1</reflink>]; Van der Aa et al., [<reflink idref="bib45" id="ref30">45</reflink>]) as well as Facebook addiction (Tang et al., [<reflink idref="bib42" id="ref31">42</reflink>]). Furthermore, agreeableness shows a moderate negative correlation with problematic smartphone use (Horwood & Anglim, [<reflink idref="bib16" id="ref32">16</reflink>]) and with social networking site use (Giota & Kleftaras, [<reflink idref="bib13" id="ref33">13</reflink>]).</p> <p>Neuroticism refers to the personality trait characterized by a tendency to experience psychological distress such as high levels of anxiety or depression (Costa & McCrae, [<reflink idref="bib7" id="ref34">7</reflink>]). Several empirical studies have shown that neuroticism has a close association with technology addiction. For instance, both Hsiao et al. ([<reflink idref="bib19" id="ref35">19</reflink>]) and Hsiao et al. ([<reflink idref="bib17" id="ref36">17</reflink>]) found that neuroticism has a positive relationship with the compulsive use of social apps. In addition, Mehroof and Griffiths ([<reflink idref="bib29" id="ref37">29</reflink>]) demonstrated that neuroticism has a positive relationship with online game addiction. This is because neurotics tend to favor emotion-focused coping strategies, such as distancing themselves from problems, avoiding issues, and engaging in wishful thinking (Bouchard, [<reflink idref="bib5" id="ref38">5</reflink>]), including activities like games that offer an escape from real-world challenges (Charlton & Danforth, [<reflink idref="bib6" id="ref39">6</reflink>]). In other words, individuals with high neuroticism tend to seek escape from their troubles by indulging in technology, which may increase their likelihood of engaging in compulsive behaviors.</p> <hd id="AN0188150135-4">The Present Study</hd> <p>The primary aim of this study is to explore variations in compulsive ChatGPT use by investigating how the relationship between the Big Five personality traits and this behavior in a group of Chinese university students. Understanding this phenomenon is particularly important in academic settings, where students often rely on ChatGPT to perform tasks, such as summarizing English-language research articles and refining their written work in English (Stojanov et al., [<reflink idref="bib39" id="ref40">39</reflink>]). These capabilities are highly valued within the academic sphere, but excessive dependence on such tools may hinder the development of essential skills like independent problem-solving and critical analysis, underscoring the need to examine the underlying drivers of compulsive usage. Based on the literature reviewed above, it was hypothesized that:</p> <hd id="AN0188150135-5">H1</hd> <p>Openness to experience has a positive relationship with compulsive use of ChatGPT.</p> <hd id="AN0188150135-6">H2</hd> <p>Conscientiousness has a negative relationship with compulsive use of ChatGPT.</p> <hd id="AN0188150135-7">H3</hd> <p>Extraversion has a positive relationship with compulsive use of ChatGPT.</p> <hd id="AN0188150135-8">H4</hd> <p>Agreeableness has a negative relationship with compulsive use of ChatGPT.</p> <hd id="AN0188150135-9">H5</hd> <p>Neuroticism has a positive relationship with compulsive use of ChatGPT.</p> <p>This study is significant because it sheds light on the psychological factors influencing AI interaction. The results are also crucial for understanding compulsive AI tool usage in other countries, providing a foundation for developing international guidelines to promote balanced and effective use of AI technologies across different cultures.</p> <hd id="AN0188150135-10">Method</hd> <p></p> <hd id="AN0188150135-11">Participants and Procedure</hd> <p>Using G*Power (version 3.1.9.4), a sample size of 167 participants was determined to be sufficient to detect a small-to-medium effect size (<emph>f</emph><sups><emph>2</emph></sups> = 0.08), as indicated by a meta-analysis on the relationship between Big Five personality traits and problematic mobile phone use (Gao et al., [<reflink idref="bib12" id="ref41">12</reflink>]). This calculation assumes six predictors, a significance level of 0.05, and a statistical power of 0.80. The inclusion criteria require participants to be full-time university students, either undergraduate or postgraduate, and aged 18 or older.</p> <p>This study represents the first wave of a three-wave longitudinal project examining the impact of compulsive ChatGPT use. To optimize recruitment efficiency and ensure the participation of individuals likely to complete all three surveys, we employed a combination of convenience and snowball sampling methods. A total of 247 responses were collected at a university in Zhejiang Province, China, using online advertisements on social media platforms (e.g., WeChat Moments and WeChat Groups). Students interested in participating scanned the QR code on the recruitment flyer to sign up for the survey. Subsequently, all registered participants were invited to a designated classroom, where they completed the online questionnaire on their own devices on September 23, 2024. A few participants who could not attend this session completed it remotely. Before starting the survey, participants were required to read the study's information sheet and provide their consent to participate by selecting the 'I agree' option. This study has been approved by the institutional review board (Ref no: WKUIRB2024-115).</p> <p>The data cleaning process involved the exclusion of responses based on these reasons: incomplete responses (<emph>n</emph> = 8), inability to pass the attention-check test (i.e., did not choose the specified option as indicated in the item; <emph>n</emph> = 16), and unusually short or long response durations (<emph>n</emph> = 3). After these initial exclusions, extreme outliers were identified as responses with completion time exceeding ± 2 standard deviations from the mean. Based on this criterion, eight responses were flagged as outliers and removed. Additionally, five duplicated responses were identified and removed. In the end, 207 valid responses were retained, resulting in an effective response rate of 83.80%. The sample consisted of 32 male and 175 female students with a mean age of 19.84 (SD = 1.74 years). There were 193 (93.2%) undergraduate students, 13 (6.2%) master's students, and 1 (0.4%) PhD student.</p> <hd id="AN0188150135-12">Measurements</hd> <p>The following scales were presented in English and Chinese languages. Their reliability results were presented in the Results section.</p> <hd id="AN0188150135-13">Compulsive Use of ChatGPT (CUC; Duong, Ngo, et al., 2024)</hd> <p>The scale has four items measuring compulsive use of ChatGPT. It utilized a 5-point Likert scale ranging from 1 (<emph>Strongly disagree</emph>) to 5 (<emph>Strongly agree</emph>). A sample item was "<emph>I become restless or troubled if I am prohibited from using ChatGPT</emph>." A higher total score means using ChatGPT more frequently and uncontrollably. Confirmatory factor analysis supported the unidimensional structure: <emph>χ2</emph> (<reflink idref="bib2" id="ref42">2</reflink>) = 3.48, <emph>p</emph> = 0.176, Comparative Fit Index <emph>(CFI)</emph> = 0.993, Tucker–Lewis Index <emph>(TLI)</emph> = 0.979, Root mean square error of approximation <emph>(RMSEA)</emph> = 0.06, and Standardized root mean square residual <emph>(SRMR)</emph> = 0.022.</p> <hd id="AN0188150135-14">15-Item Chinese Big Five Personality Inventory (CBF-PI-15; Zhang et al., 2019)</hd> <p>The scale has 15 items measuring the Big Five personality traits. Each personality trait was assessed by three items using a 6-point Likert scale (1: Totally disagree; 6: Totally agree). A sample item for openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism dimensions was "I'm a person who loves to take risks and break the rules," "I like to plan things from the beginning," "I like to go to social and recreational parties," "I think most people are well intentioned," and "I often worry about trifles." Composite scores for each personality dimension were calculated by summing the corresponding item scores, with items 13 and 14 reverse scored. Higher composite scores indicate greater levels of the respective traits. The five-factor structure has been confirmed and validated in both Chinese and American samples (Zhang et al., [<reflink idref="bib49" id="ref43">49</reflink>]), as well as in Malaysian samples (Tan et al., [<reflink idref="bib41" id="ref44">41</reflink>]).</p> <hd id="AN0188150135-15">Data Analysis</hd> <p>All analyses were conducted using JASP version 0.19.1 (JASP Team, [<reflink idref="bib21" id="ref45">21</reflink>]). The descriptive statistics were first examined. Then, we referred to skewness and kurtosis values to examine the normality of the data. According to Kim ([<reflink idref="bib24" id="ref46">24</reflink>]), the normality assumption is violated if the z-score of the skewness or kurtosis value (i.e., dividing the absolute value by its standard error) exceeds 3.29 for a sample size between 50 and 300. Cronbach alpha (α) and McDonald's omega (ω) coefficients were then used to estimate the internal consistency of the measurement scores. Next, Pearson correlation was conducted to examine the relationship between the variables. Finally, a hierarchical multiple linear regression analysis with compulsive ChatGPT use as the outcome variable was conducted. Age was entered in the first step of the analysis, while the five personality traits were entered in the second step. This approach allows us to examine the relationship between the Big Five personality traits and compulsive ChatGPT use while controlling for the effect of age.</p> <hd id="AN0188150135-16">Results</hd> <p></p> <hd id="AN0188150135-17">Descriptive Statistics</hd> <p>Table 1 shows the descriptive statistics, reliability coefficients, and correlation coefficients for the variables. Inspection of the z-scores for skewness and kurtosis indicated that the normality assumption was not met for the variables age and agreeableness. However, analyses using the transformed values of these variables produced compatible results. Therefore, we have reported the results based on the original values.</p> <p>Table 1 Descriptive statistics, correlation, and reliability for variables</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Variable</p></th><th align="left"><p><italic>M</italic></p></th><th align="left"><p><italic>SD</italic></p></th><th align="left"><p>Skewness<sup>a</sup></p></th><th align="left"><p>kurtosis<sup>b</sup></p></th><th align="left"><p>1</p></th><th align="left"><p>2</p></th><th align="left"><p>3</p></th><th align="left"><p>4</p></th><th align="left"><p>5</p></th><th align="left"><p>6</p></th></tr></thead><tbody><tr><td align="left"><p>1. Compulsive use of ChatGPT</p></td><td char="." align="char"><p>2.56</p></td><td char="." align="char"><p>0.91</p></td><td char="." align="char"><p>0.11</p></td><td align="left"><p>− 0.62</p></td><td align="left"><p>.774</p><p>(.785)</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>2. Openness</p></td><td char="." align="char"><p>3.74</p></td><td char="." align="char"><p>1.23</p></td><td char="." align="char"><p>− 0.18</p></td><td align="left"><p>− 0.80</p></td><td align="left"><p>− 0.090</p></td><td align="left"><p>.932</p><p>(.934)</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>3. Conscientiousness</p></td><td char="." align="char"><p>4.17</p></td><td char="." align="char"><p>0.98</p></td><td char="." align="char"><p>− 0.08</p></td><td align="left"><p>− 0.50</p></td><td align="left"><p>.010</p></td><td align="left"><p>.140*</p></td><td align="left"><p>.714 (.722)</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>4. Extraversion</p></td><td char="." align="char"><p>3.55</p></td><td char="." align="char"><p>1.35</p></td><td char="." align="char"><p>− 0.07</p></td><td align="left"><p>− 0.86</p></td><td align="left"><p>−.096</p></td><td align="left"><p>.272***</p></td><td align="left"><p>− 0.038</p></td><td align="left"><p>.930 (.936)</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p>5. Agreeableness</p></td><td char="." align="char"><p>4.21</p></td><td char="." align="char"><p>1.07</p></td><td char="." align="char"><p>− 0.62</p></td><td align="left"><p>0.32</p></td><td align="left"><p>−.217**</p></td><td align="left"><p>.101</p></td><td align="left"><p>.180**</p></td><td align="left"><p>.246***</p></td><td align="left"><p>.859 (.859)</p></td><td align="left" /></tr><tr><td align="left"><p>6. Neuroticism</p></td><td char="." align="char"><p>3.71</p></td><td char="." align="char"><p>1.20</p></td><td char="." align="char"><p>− 0.25</p></td><td align="left"><p>− 0.48</p></td><td align="left"><p>.224**</p></td><td align="left"><p>−.264***</p></td><td align="left"><p>−.235***</p></td><td align="left"><p>−.204**</p></td><td align="left"><p>−.247***</p></td><td align="left"><p>.851 (.857)</p></td></tr><tr><td align="left"><p>7. Age</p></td><td char="." align="char"><p>19.85</p></td><td char="." align="char"><p>1.74</p></td><td char="." align="char"><p>1.29</p></td><td align="left"><p>2.77</p></td><td align="left"><p>.188**</p></td><td align="left"><p>−.202**</p></td><td align="left"><p>.063</p></td><td align="left"><p>− 096</p></td><td align="left"><p>− 009</p></td><td align="left"><p>− 091</p></td></tr></tbody></table> </ephtml> </p> <p> <emph>N</emph> = 207. <sups>a</sups>Skewness SE = 0.169; <sups>b</sups>Skewness SE = 0.337; Cronbach (Omega) coefficients were presented at the diagonal line <sups>*</sups><emph>p</emph> <.05; **<emph>p</emph> <.01; ***<emph>p</emph> <.001</p> <p>The compulsive use of ChatGPT and personality scores showed good internal consistency. The α coefficients ranged from 0.714 (Conscientiousness) to 0.932 (Openness), while the ω coefficients ranged from 0.722 (Conscientiousness) to 0.936 (Extraversion). Finally, Pearson correlation analysis showed that compulsive use of ChatGPT had a positive relationship with neuroticism and age and a negative relationship with agreeableness. Moreover, there was a negative relationship between age and openness.</p> <hd id="AN0188150135-18">Hierarchical Multiple Linear Regression</hd> <p>The regression analysis results are summarized in Table 2. The variance inflation factor (VIF) values suggest that multicollinearity is not a concern in the analysis. The relationship between age and compulsive ChatGPT use was tested in Step 1. The model was statistically significant, <emph>F</emph>(<reflink idref="bib1" id="ref47">1</reflink>, 204) = 7.52, <emph>p</emph> = 0.007, and explained 3.1% of the variance in compulsive use of ChatGPT. Age was found to have a positive relationship with compulsive use of ChatGPT, (unstandardized coefficient) <emph>B</emph> = 0.392, <emph>p</emph> = 0.007.</p> <p>Table 2 Summary of hierarchical multiple regression results</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left" /><th align="left" /><th align="left" /><th align="left" /><th align="left" /><th align="left" /><th align="left" colspan="2"><p>95% CI</p></th><th align="left" /></tr><tr><th align="left"><p>No</p></th><th align="left"><p>Variable</p></th><th align="left"><p><italic>B</italic></p></th><th align="left"><p><italic>SE</italic></p></th><th align="left"><p>β</p></th><th align="left"><p>t</p></th><th align="left"><p>p</p></th><th align="left"><p>Lower</p></th><th align="left"><p>Upper</p></th><th align="left"><p>VIF</p></th></tr></thead><tbody><tr><td align="left" colspan="10"><p>Step 1: <italic>F</italic>(1, 205) = 7.52, <italic>p</italic> =.007, Adjusted <italic>R</italic><sup>2</sup> =.031 (<italic>R</italic><sup>2</sup> =.035)</p></td></tr><tr><td align="left"><p>1</p></td><td align="left"><p>Age</p></td><td char="." align="char"><p>0.39</p></td><td char="." align="char"><p>0.14</p></td><td char="." align="char"><p>0.19</p></td><td char="." align="char"><p>2.74</p></td><td char="." align="char"><p>0.007</p></td><td char="." align="char"><p>0.11</p></td><td char="." align="char"><p>0.67</p></td><td char="." align="char"><p>1.00</p></td></tr><tr><td align="left" colspan="10"><p>Step 2: <italic>F</italic>(6, 200) = 4.81, <italic>p</italic> <.001, Adjusted <italic>R</italic><sup>2</sup> =.100 (<italic>R</italic><sup>2</sup> =.126), <italic>R</italic><sup>2</sup> change =.091, <italic>F</italic><sub>change</sub>(5, 200) = 4.15, <italic>p</italic> <.001</p></td></tr><tr><td align="left"><p>1</p></td><td align="left"><p>Age</p></td><td char="." align="char"><p>0.43</p></td><td char="." align="char"><p>0.14</p></td><td char="." align="char"><p>0.21</p></td><td char="." align="char"><p>3.00</p></td><td char="." align="char"><p>0.003</p></td><td char="." align="char"><p>0.15</p></td><td char="." align="char"><p>0.71</p></td><td char="." align="char"><p>1.08</p></td></tr><tr><td align="left"><p>2</p></td><td align="left"><p>Openness</p></td><td char="." align="char"><p>0.01</p></td><td char="." align="char"><p>0.07</p></td><td char="." align="char"><p>0.01</p></td><td char="." align="char"><p>0.18</p></td><td char="." align="char"><p>0.854</p></td><td char="." align="char"><p>− 0.13</p></td><td char="." align="char"><p>0.15</p></td><td char="." align="char"><p>1.21</p></td></tr><tr><td align="left"><p>3</p></td><td align="left"><p>Conscientiousness</p></td><td char="." align="char"><p>0.10</p></td><td char="." align="char"><p>0.09</p></td><td char="." align="char"><p>0.08</p></td><td char="." align="char"><p>1.16</p></td><td char="." align="char"><p>0.248</p></td><td char="." align="char"><p>− 0.07</p></td><td char="." align="char"><p>0.27</p></td><td char="." align="char"><p>1.11</p></td></tr><tr><td align="left"><p>4</p></td><td align="left"><p>Extraversion</p></td><td char="." align="char"><p>0.01</p></td><td char="." align="char"><p>0.06</p></td><td char="." align="char"><p>0.01</p></td><td char="." align="char"><p>0.18</p></td><td char="." align="char"><p>0.858</p></td><td char="." align="char"><p>− 0.11</p></td><td char="." align="char"><p>0.14</p></td><td char="." align="char"><p>1.18</p></td></tr><tr><td align="left"><p>5</p></td><td align="left"><p>Agreeableness</p></td><td char="." align="char"><p>− 0.20</p></td><td char="." align="char"><p>0.08</p></td><td char="." align="char"><p>− 0.18</p></td><td char="." align="char"><p>− 2.54</p></td><td char="." align="char"><p>0.012</p></td><td char="." align="char"><p>− 0.36</p></td><td char="." align="char"><p>− 0.05</p></td><td char="." align="char"><p>1.14</p></td></tr><tr><td align="left"><p>6</p></td><td align="left"><p>Neuroticism</p></td><td char="." align="char"><p>0.22</p></td><td char="." align="char"><p>0.07</p></td><td char="." align="char"><p>0.22</p></td><td char="." align="char"><p>3.07</p></td><td char="." align="char"><p>0.002</p></td><td char="." align="char"><p>0.08</p></td><td char="." align="char"><p>0.37</p></td><td char="." align="char"><p>1.21</p></td></tr></tbody></table> </ephtml> </p> <p> <emph>N</emph> = 207. <emph>B</emph> Unstandardized regression coefficient, <emph>SE </emph>Standard error, <emph>β </emph>Standardized regression coefficient; <emph>Lower </emph>95% confidence interval lower bound, <emph>Upper </emph>95% confidence interval upper bound, <emph>VIF </emph>Variance inflation factor</p> <p>The five personality trait scores were then entered to Step 2. Likewise, the model was significant, <emph>F</emph>(<reflink idref="bib6" id="ref48">6</reflink>, 200) = 4.81, <emph>p</emph> < 0.001, and explained 10.0% of the variance. The positive relationship between age and compulsive use of ChatGPT continued to occur (<emph>B</emph> = 0.428, <emph>p</emph> = 0.003). Moreover, two of the five personality traits were found to have a relationship with compulsive use of ChatGPT. Specifically, neuroticism was positively related to compulsive use of ChatGPT (<emph>B</emph> = 0.224, <emph>p</emph> = 0.002), while agreeableness was negatively associated with compulsive use (<emph>B</emph> = − 0.202, <emph>p</emph> = 0.012).</p> <hd id="AN0188150135-19">Discussion</hd> <p>The present study investigated whether Big Five personality traits—openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism—are related with compulsive use of ChatGPT among Chinese university students. Our results show mixed findings to the role of personality traits in compulsive ChatGPT use. Two of the five hypotheses of the present study are supported.</p> <p>Consistent with previous studies (Hsiao et al., [<reflink idref="bib17" id="ref49">17</reflink>], [<reflink idref="bib19" id="ref50">19</reflink>]) and supporting our H5, our results showed that neuroticism was positively associated with compulsive use ChatGPT. In other words, students high in neuroticism tend to use ChatGPT compulsively. This could be due to neurotic individuals often experience unstable emotion (Costa & McCrae, [<reflink idref="bib7" id="ref51">7</reflink>]), have poor emotional regulation (Nilsen et al., [<reflink idref="bib33" id="ref52">33</reflink>]), and they tend to utilize avoidance and suppression emotion regulation strategies (Barańczuk, [<reflink idref="bib3" id="ref53">3</reflink>]). As a result, students with high neuroticism may use ChatGPT compulsively as an external coping style to reduce their academic stress and anxiety.</p> <p>On the other hand, supporting H4, agreeableness was negatively related to compulsive use of ChatGPT, indicating that students high in agreeableness are less likely to use ChatGPT compulsively. The result aligns with previous research on the relationship between agreeableness and problematic technology use (Andreassen et al., [<reflink idref="bib1" id="ref54">1</reflink>]; Giota & Kleftaras, [<reflink idref="bib13" id="ref55">13</reflink>]; Horwood & Anglim, [<reflink idref="bib16" id="ref56">16</reflink>]). This is because individuals who are high in agreeableness tend to have interpersonal behaviors, such as trust, straightforwardness, altruism, and compliance (Costa Jr et al., [<reflink idref="bib8" id="ref57">8</reflink>]), making them to establish interpersonal relationships through real-world social interactions. A broad social network increases their chances of receiving help to solve problems, rather than relying on ChatGPT. Furthermore, highly agreeable individuals tend to be less lonely (Teppers et al., [<reflink idref="bib43" id="ref58">43</reflink>]) because they maintain good interpersonal relationships and are less likely to be ostracized, partly due to their ability to control selfish impulses through self-discipline (Stavrova et al., [<reflink idref="bib38" id="ref59">38</reflink>]). Consequently, individuals high in agreeableness also demonstrate better self-control in avoiding technology addiction (Özdemir et al., [<reflink idref="bib35" id="ref60">35</reflink>]).</p> <p>On the contrary, openness to experience, conscientiousness, and extraversion did not relate to the compulsive use of ChatGPT, which contradicts H1, H2, and H3. One of the plausible explanations for why H1 on openness to experience did not find support is the nature of one's interaction with ChatGPT. Individuals high in openness are curious; they like searching out and appreciate new experiences (McCrae & Costa, 1997). These individuals might try several forms of technology, though perhaps a little more intentionally and exploratory rather than compulsively.</p> <p>The lack of a significant relationship between conscientiousness and compulsive use of ChatGPT (H2) is somewhat surprising, as conscientious individuals are typically characterized by high self-discipline (MacCann et al., [<reflink idref="bib27" id="ref61">27</reflink>]) and stronger impulse control (Durak & Senol‐Durak, [<reflink idref="bib11" id="ref62">11</reflink>]). One possibility lies in the differing motivations for technology use: Conscientiousness is seemingly driven by achievement (Costa Jr. et al., [<reflink idref="bib8" id="ref63">8</reflink>]), whereby individuals high on this personality trait would be more likely to use ChatGPT for help in fulfilling academic or task-related needs rather than as a means of escapism from negative emotions. Because of that, conscientiousness may not really predict compulsive behavior, which usually arises in emotionally charged contexts, such as the use of social apps providing more immediate emotional gratification. ChatGPT is primarily a tool for students to seek information (e.g., obtaining feedback on written assignments) and solutions to problems (e.g., understanding complex concepts in their studies) (Stojanov et al., [<reflink idref="bib39" id="ref64">39</reflink>]). It likely does not offer the emotional reinforcement or level of escapism associated with other technologies. Lacking the aforementioned emotional appeals, this platform may be less likely to foster compulsive use among students high in conscientiousness.</p> <p>H3 predicts that extraversion would have a positive relationship with compulsive use of ChatGPT, because extraverts prefer social interaction and stimulation. But ChatGPT is an AI platform; it does not have the social richness of human contact. Indeed, research has indicated that extraverts tend to prefer more socially rewarding technologies such as sharing economy applications (Shanahan et al., [<reflink idref="bib37" id="ref65">37</reflink>]). Therefore, one reason extraversion did not emerge may be because while ChatGPT is interactive, extraverts do not get from it the social contact they desire.</p> <p>Our findings underscore the importance of considering personality traits when examining factors associated with compulsive AI use, with significant implications for educational settings. Specifically, personality traits such as neuroticism and agreeableness influence compulsive ChatGPT usage, with individuals high in neuroticism potentially relying on the platform to manage anxiety, while those high in agreeableness may engage with it differently. Age has shown a positive correlation with compulsive ChatGPT use, indicating that older students are more prone to such behavior. A possible explanation is that younger students, such as freshmen, may have had limited exposure to AI tools during high school and, as a result, might feel less inclined to use them due to unfamiliarity with their functionality.</p> <p>The rising popularity of domestic generative AI platforms, such as DeepSeek and Kimi, suggests that the compulsive use patterns identified in this study may extend beyond ChatGPT to these alternatives, given their similar functionality and widespread adoption among students. Notably, the unique design, regulatory landscape, and cultural context of domestic AI platforms in China may shape usage behaviors in distinct ways, warranting further investigation. This highlights the need to examine whether and how personality traits influence compulsive technology use across different AI tools. By exploring the relationship between personality traits and compulsive ChatGPT use, this study not only deepens our understanding of individual behavioral patterns but also provides valuable insights for educators. Educators can use these insights to identify students at risk for compulsive ChatGPT use based on their personality profiles and to design interventions to mitigate such behavior. For example, mindfulness meditation—shown to reduce anxiety and stress (Bamber & Schneider, [<reflink idref="bib2" id="ref66">2</reflink>])—could be introduced as part of a broader strategy to help neurotic individuals manage their emotions without over-relying on AI tools. Likewise, as students with lower levels of agreeableness may resist collaborative learning and misuse AI tools to bypass group efforts, teachers can organize structured group activities to foster collaboration among less agreeable students to ensure that AI tools enhance rather than hinder personal and academic growth.</p> <p>This study has several limitations that should be acknowledged. First, our sample was limited to Chinese students, which may restrict the generalizability of the findings. For example, in the Chinese educational context, students tend to perceive themselves as less creative compared to their Western counterparts (Wang & Greenwood, [<reflink idref="bib46" id="ref67">46</reflink>]). This difference in self-perception could influence the observed relationship between openness to experience and compulsive use of ChatGPT. Additionally, the reliance on self-report surveys introduces potential bias, as participants may not accurately assess their behaviors or personality traits. Likewise, the use of a cross-sectional design limits the ability to draw conclusions about causal relationships. While this design provides valuable insights into the correlation between Big Five personality traits and compulsive ChatGPT use, it does not allow us to explore the temporal dynamics or determine whether one variable influences the other over time. However, it is unlikely that compulsive use of ChatGPT would influence fundamental personality traits, such as those outlined in the Big Five model. Another limitation is the exclusive focus on the Big Five Personality model, which may overlook other frameworks, such as HEXACO or constructs like locus of control and self-efficacy that could offer additional insights. This narrow scope might miss important factors influencing compulsive ChatGPT use. Future researchers are recommended to address these limitations by incorporating more culturally diverse samples, employing longitudinal designs, assessing actual behaviors associated with the use of ChatGPT, and using other personality models. Such studies would help validate the universality of these findings and examine whether there is a causal relationship between Big Five personality traits and compulsive ChatGPT use.</p> <p>In conclusion, the present study provides a foundational understanding of the relationship between Big Five personality traits and compulsive ChatGPT use. While it offers initial insights, further research is needed to explore the underlying mechanisms of this relationship and to account for other personality factors, such as perfectionism, that may also play a role. Future studies should incorporate diverse cultural contexts and employ more rigorous methodologies to deepen our understanding of how personality influences technology use behaviors like compulsive ChatGPT use.</p> <hd id="AN0188150135-20">Author Contributions</hd> <p>Conceptualization, Chee-Seng Tan; Data curation, Yinyao Hu, Jiahui Qian, Shimeng Wang, Yihan Wang, and Hanyun Zhang; Formal analysis, Yinyao Hu, Chee-Seng Tan; Funding acquisition, Chee-Seng Tan; Methodology, Yinyao Hu, Chee-Seng Tan, Jiahui Qian, Shimeng Wang, Yihan Wang, and Hanyun Zhang; Project administration, Chee-Seng Tan; Writing—original draft, Yinyao Hu and Chee-Seng Tan; Writing—review & editing, Yinyao Hu, Chee-Seng Tan, Hanyun Zhang, Jiahui Qian, Shimeng Wang, and Yihan Wang. All authors have read and agreed to the published version of the manuscript.</p> <hd id="AN0188150135-21">Funding</hd> <p>This study was supported by the Wenzhou-Kean University Student Partnering with Faculty Research Programs (Project no: WKUSPF202421).</p> <hd id="AN0188150135-22">Data Availability</hd> <p>The data of the present study are available upon request to the corresponding author.</p> <hd id="AN0188150135-23">Code Availability</hd> <p>Not applicable.</p> <hd id="AN0188150135-24">Declarations</hd> <p></p> <hd id="AN0188150135-25">Conflict of interest</hd> <p>The authors have no conflicts of interest to declare relevant to this article's content.</p> <hd id="AN0188150135-26">Ethical Approval</hd> <p>All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national). Ethics approval was granted by the Wenzhou-Kean University Ethics Committee (Ref: WKUIRB2024-115).</p> <hd id="AN0188150135-27">Informed Consent</hd> <p>E-informed consent was obtained from all individual participants included in the study.</p> <hd id="AN0188150135-28">Generative AI and AI-Assisted Technologies in the Writing Process</hd> <p>During the preparation of this manuscript the authors used ChatGPT-4o to improve the spelling, grammar, clarity, conciseness, and overall readability of the text. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.</p> <hd id="AN0188150135-29">Publisher's Note</hd> <p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p> <ref id="AN0188150135-30"> <title> References </title> <blist> <bibl id="bib1" idref="ref29" type="bt">1</bibl> <bibtext> Andreassen CS, Griffiths MD, Gjertsen SR, Krossbakken E, Kvam S, Pallesen S. The relationships between behavioral addictions and the five-factor model of personality. Journal of Behavioral Addictions. 2013; 2; 2: 90-99. 10.1556/jba.2.2013.003</bibtext> </blist> <blist> <bibl id="bib2" idref="ref42" type="bt">2</bibl> <bibtext> Bamber MD, Schneider JK. Mindfulness-based meditation to decrease stress and anxiety in college students: A narrative synthesis of the research. Educational Research Review. 2016; 18: 1-32. 10.1016/j.edurev.2015.12.004</bibtext> </blist> <blist> <bibl id="bib3" idref="ref53" type="bt">3</bibl> <bibtext> Barańczuk U. The Five factor model of personality and social support: A meta-analysis. Journal of Research in Personality. 2019; 81: 38-46. 10.1016/j.jrp.2019.05.002</bibtext> </blist> <blist> <bibl id="bib4" idref="ref2" type="bt">4</bibl> <bibtext> Barrot JS. Using ChatGPT for second language writing: Pitfalls and potentials. Assessing Writing. 2023; 57. 10.1016/j.asw.2023.100745100745</bibtext> </blist> <blist> <bibl id="bib5" idref="ref38" type="bt">5</bibl> <bibtext> Bouchard G. Cognitive appraisals, neuroticism, and openness as correlates of coping strategies: An integrative model of adaptation to marital difficulties. Canadian Journal of Behavioural Science. 2003; 35; 1: 1. 10.1037/h0087181</bibtext> </blist> <blist> <bibl id="bib6" idref="ref39" type="bt">6</bibl> <bibtext> Charlton JP, Danforth ID. Validating the distinction between computer addiction and engagement: Online game playing and personality. Behaviour & Information Technology. 2010; 29; 6: 601-613. 10.1080/01449290903401978</bibtext> </blist> <blist> <bibl id="bib7" idref="ref14" type="bt">7</bibl> <bibtext> Costa PT, McCrae RR. Normal personality assessment in clinical practice: The NEO personality inventory. Psychological Assessment. 1992; 4; 1: 5. 10.1037/1040-3590.4.1.5</bibtext> </blist> <blist> <bibl id="bib8" idref="ref57" type="bt">8</bibl> <bibtext> Costa PT Jr, McCrae RR, Dye DA. Facet scales for agreeableness and conscientiousness: A revision of the NEO personality inventory. Personality and Individual Differences. 1991; 12; 9: 887-898. 10.1016/0191-8869(91)90177-D</bibtext> </blist> <blist> <bibl id="bib9" idref="ref4" type="bt">9</bibl> <bibtext> Duong CD, Ngo TVN, Khuc TA, Tran NM, Nguyen TPT. Unraveling the dark side of ChatGPT: A moderated mediation model of technology anxiety and technostress. Information Technology & People. 2024. 10.1108/ITP-11-2023-1151</bibtext> </blist> <blist> <bibtext> Duong CD, Vu TN, Ngo TVN, Do ND, Tran NM. Reduced student life satisfaction and academic performance: Unraveling the dark side of ChatGPT in the higher education context. International Journal of Human-Computer Interaction. 2024. 10.1080/10447318.2024.2356361</bibtext> </blist> <blist> <bibtext> Durak M, Senol-Durak E. Which personality traits are associated with cognitions related to problematic Internet use?. Asian Journal of Social Psychology. 2014; 17; 3: 206-218. 10.1111/ajsp.12056</bibtext> </blist> <blist> <bibtext> Gao, L, Zhai, S, Xie, H, Liu, Q, Niu, G, & Zhou, Z. (2022). Big five personality traits and problematic mobile phone use: A meta-analytic review. Current Psychology, 41, 3093–3110. https://doi.org/10.1007/s12144-020-00817-x</bibtext> </blist> <blist> <bibtext> Giota KG, Kleftaras G. The role of personality and depression in problematic use of social networking sites in Greece. Cyberpsychology. 2013. 10.5817/CP2013-3-6</bibtext> </blist> <blist> <bibtext> Gosling SD, Augustine AA, Vazire S, Holtzman N, Gaddis S. Manifestations of personality in online social networks: Self-reported Facebook-related behaviors and observable profile information. Cyberpsychology, Behavior, and Social Networking. 2011; 14; 9: 483-488. 10.1089/cyber.2010.0087</bibtext> </blist> <blist> <bibtext> Hirschman EC. The consciousness of addiction: Toward a general theory of compulsive consumption. Journal of Consumer Research. 1992; 19; 2: 155-179. 10.1086/209294</bibtext> </blist> <blist> <bibtext> Horwood S, Anglim J. Personality and problematic smartphone use: A facet-level analysis using the five factor model and HEXACO frameworks. Computers in Human Behavior. 2018; 85: 349-359. 10.1016/j.chb.2018.04.013</bibtext> </blist> <blist> <bibtext> Hsiao, K.-L, Lee, C.-H, Chiang, H.-S, Wang, J.-Y. (2016). Exploring the antecedents of technostress and compulsive mobile application usage: Personality perspectives. In J. Zhou & G. Salvendy (Eds.), Lecture notes in computer science: Vol. 9754. Human aspects of IT for the aged population. Design for aging (pp. 320–328). Springer. https://doi.org/10.1007/978-3-319-39943-0_31</bibtext> </blist> <blist> <bibtext> Hsiao KL. Compulsive mobile application usage and technostress: The role of personality traits. Online Information Review. 2017; 41; 2: 272-295. 10.1108/OIR-03-2016-0091</bibtext> </blist> <blist> <bibtext> Hsiao KL, Shu Y, Huang TC. Exploring the effect of compulsive social app usage on technostress and academic performance: Perspectives from personality traits. Telematics and Informatics. 2017; 34; 2: 679-690. 10.1016/j.tele.2016.11.001</bibtext> </blist> <blist> <bibtext> Hwang HS. The Influence of personality traits on the Facebook addiction. KSII Transactions on Internet and Information Systems (TIIS). 2017; 11; 2: 1032-1042. 10.3837/tiis.2017.02.022</bibtext> </blist> <blist> <bibtext> JASP Team (2024). JASP (Version 0.19.1) [Computer software].</bibtext> </blist> <blist> <bibtext> Kallianou MN. Agreeableness and conflict management in daily life: The impact of prosocial traits on psychological adaptation. Studies in Psychological Science. 2024; 2; 3: 56-65. 10.56397/SPS.2024.09.06</bibtext> </blist> <blist> <bibtext> Kaya F, Aydin F, Schepman A, Rodway P, Yetişensoy O, Demir Kaya M. The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. International Journal of Human-Computer Interaction. 2022; 40; 2: 497-514. 10.1080/10447318.2022.2151730</bibtext> </blist> <blist> <bibtext> Kim HY. Statistical notes for clinical researchers: Assessing normal distribution (2) using skewness and kurtosis. Restorative Dentistry & Endodontics. 2013; 38: 52-54. 10.5395/rde.2013.38.1.52</bibtext> </blist> <blist> <bibtext> Klobas JE, McGill TJ, Moghavvemi S, Paramanathan T. Compulsive YouTube usage: A comparison of use motivation and personality effects. Computers in Human Behavior. 2018; 87: 129-139. 10.1016/j.chb.2018.05.038</bibtext> </blist> <blist> <bibtext> Lachmann B, Duke É, Sariyska R, Montag C. Who's addicted to the smartphone and/or the Internet?. Psychology of Popular Media Culture. 2019; 8; 3: 182. 10.1037/ppm0000172</bibtext> </blist> <blist> <bibtext> MacCann C, Duckworth AL, Roberts RD. Empirical identification of the major facets of conscientiousness. Learning and Individual Differences. 2009; 19; 4: 451-458. 10.1016/j.lindif.2009.03.007</bibtext> </blist> <blist> <bibtext> Mao T, Pan W, Zhu Y, Yang J, Dong Q, Zhou G. Self-control mediates the relationship between personality trait and impulsivity. Personality and Individual Differences. 2018; 129: 70-75. 10.1016/j.paid.2018.03.013</bibtext> </blist> <blist> <bibtext> Mehroof M, Griffiths MD. Online gaming addiction: The role of sensation seeking, self-control, neuroticism, aggression, state anxiety, and trait anxiety. Cyberpsychology, Behavior, and Social Networking. 2010; 13; 3: 313-316. 10.1089/cyber.2009.0229</bibtext> </blist> <blist> <bibtext> Meynadier J, Malouff JM, Schutte NS, Loi NM. Meta-analysis of associations between five-factor personality traits and problematic social media use. Current Psychology. 2024; 43: 23016-23035. 10.1007/s12144-024-06052-y</bibtext> </blist> <blist> <bibtext> Michel-Villarreal R, Vilalta-Perdomo E, Salinas-Navarro DE, Thierry-Aguilera R, Gerardou FS. Challenges and opportunities of generative AI for higher education as explained by ChatGPT. Education Sciences. 2023; 13; 9: 856. 10.3390/educsci13090856</bibtext> </blist> <blist> <bibtext> Montenegro-Rueda M, Fernández-Cerero J, Fernández-Batanero JM, López-Meneses E. Impact of the implementation of ChatGPT in education: A systematic review. Computers. 2023; 12; 8: 153. 10.3390/computers12080153</bibtext> </blist> <blist> <bibtext> Nilsen FA, Bang H, Røysamb E. Personality traits and self-control: The moderating role of neuroticism. PLoS ONE. 2024; 19; 8: e0307871. 10.1371/journal.pone.0307871</bibtext> </blist> <blist> <bibtext> Nwufo IJ, Ike OO. Personality traits and internet addiction among adolescent students: The moderating role of family functioning. International Journal of Environmental Research and Public Health. 2024; 21; 5: 520. 10.3390/ijerph21050520</bibtext> </blist> <blist> <bibtext> Özdemir Y, Kuzucu Y, Ak Ş. Depression, loneliness and Internet addiction: How important is low self-control?. Computers in Human Behavior. 2014; 34: 284-290. 10.1016/j.chb.2014.02.009</bibtext> </blist> <blist> <bibtext> Rahmani S, Lavasani MG. The relationship between internet dependency with sensation seeking and personality. Procedia Social and Behavioral Sciences. 2011; 30: 272-277. 10.1016/j.sbspro.2011.10.054</bibtext> </blist> <blist> <bibtext> Shanahan, D. E, Russell, C. A, Granados, N. F. (2023). Extraversion, technology proclivity, and participation in technology-mediated, sharing economy markets: An abstract. In B Jochims & J Allen. (Eds.), Developments in marketing science: Proceedings of the academy of marketing science. Optimistic marketing in challenging times: Serving ever-shifting customer needs (pp. 397–398). Springer. https://doi.org/10.1007/978-3-031-24687-6_169</bibtext> </blist> <blist> <bibtext> Stavrova O, Ren D, Pronk T. Low self-control: A hidden cause of loneliness?. Personality and Social Psychology Bulletin. 2022; 48; 3: 347-362. 10.1177/01461672211007228</bibtext> </blist> <blist> <bibtext> Stojanov A, Liu Q, Koh JHL. University students' self-reported reliance on ChatGPT for learning: A latent profile analysis. Computers and Education: Artificial Intelligence. 2024; 6: 100243. 10.1016/j.caeai.2024.100243</bibtext> </blist> <blist> <bibtext> Tan C-S, Hashim IHM, Pheh K-S, Pratt C, Chung M-H, Setyowati A. The mediating role of openness to experience and curiosity in the relationship between mindfulness and meaning in life: Evidence from four countries. Current Psychology. 2023; 42: 327-337. 10.1007/s12144-021-01430-2</bibtext> </blist> <blist> <bibtext> Tan C-S, Swami V, Cheng S-M, Ching W-C. Personality and positive body image associations between the big five facets and body appreciation in Malaysian adults. Journal of Individual Differences. 2024. 10.1027/1614-0001/a000430</bibtext> </blist> <blist> <bibtext> Tang JH, Chen MC, Yang CY, Chung TY, Lee YA. Personality traits, interpersonal relationships, online social support, and Facebook addiction. Telematics and Informatics. 2016; 33; 1: 102-108. 10.1016/j.tele.2015.06.003</bibtext> </blist> <blist> <bibtext> Teppers E, Klimstra TA, Damme CV, Luyckx K, Vanhalst J, Goossens L. Personality traits, loneliness, and attitudes toward aloneness in adolescence. Journal of Social and Personal Relationships. 2013; 30; 8: 1045-1063. 10.1177/0265407513481445</bibtext> </blist> <blist> <bibtext> Tripathi N, Upadhyay S, Das H. Forced transition to technology: Role of self-efficacy and big five personality variables in the adoption of technology. International Journal of Educational Management. 2022; 36; 7: 1131-1151. 10.1108/IJEM-02-2022-0065</bibtext> </blist> <blist> <bibtext> Van der Aa N, Overbeek G, Engels RC, Scholte RH, Meerkerk GJ, Van den Eijnden RJ. Daily and compulsive internet use and well-being in adolescence: A diathesis-stress model based on big five personality traits. Journal of Youth and Adolescence. 2009; 38: 765-776. 10.1007/s10964-008-9298-3</bibtext> </blist> <blist> <bibtext> Wang B, Greenwood KM. Chinese students' perceptions of their creativity and their perceptions of Western students' creativity. Educational Psychology. 2013; 33; 5: 628-643. 10.1080/01443410.2013.826345</bibtext> </blist> <blist> <bibtext> Whaite EO, Shensa A, Sidani JE, Colditz JB, Primack BA. Social media use, personality characteristics, and social isolation among young adults in the United States. Personality and Individual Differences. 2018; 124: 45-50. 10.1016/j.paid.2017.10.030</bibtext> </blist> <blist> <bibtext> Zhang B, Li YM, Li J, Luo J, Ye Y, Yin L, Chen Z, Soto CJ, John OP. The big five inventory–2 in China: A comprehensive psychometric evaluation in four diverse samples. Assessment. 2022; 29; 6: 1262-1284. 10.1177/10731911211008245</bibtext> </blist> <blist> <bibtext> Zhang X, Wang MC, He L, Jie L, Deng J. The development and psychometric evaluation of the Chinese big five personality inventory-15. PLoS ONE. 2019; 14; 8: Article e0221621. 10.1371/journal.pone.0221621</bibtext> </blist> </ref> <aug> <p>By Yinyao Hu; Chee-Seng Tan; Shimeng Wang; Hanyun Zhang; Jiahui Qian and Yihan Wang</p> <p>Reported by Author; Author; Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib32" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib31" firstref="ref3"></nolink> <nolink nlid="nl3" bibid="bib15" firstref="ref6"></nolink> <nolink nlid="nl4" bibid="bib18" firstref="ref7"></nolink> <nolink nlid="nl5" bibid="bib25" firstref="ref8"></nolink> <nolink nlid="nl6" bibid="bib20" firstref="ref9"></nolink> <nolink nlid="nl7" bibid="bib30" firstref="ref10"></nolink> <nolink nlid="nl8" bibid="bib23" firstref="ref11"></nolink> <nolink nlid="nl9" bibid="bib49" firstref="ref12"></nolink> <nolink nlid="nl10" bibid="bib48" firstref="ref13"></nolink> <nolink nlid="nl11" bibid="bib40" firstref="ref16"></nolink> <nolink nlid="nl12" bibid="bib44" firstref="ref17"></nolink> <nolink nlid="nl13" bibid="bib34" firstref="ref18"></nolink> <nolink nlid="nl14" bibid="bib36" firstref="ref19"></nolink> <nolink nlid="nl15" bibid="bib28" firstref="ref21"></nolink> <nolink nlid="nl16" bibid="bib26" firstref="ref22"></nolink> <nolink nlid="nl17" bibid="bib14" firstref="ref24"></nolink> <nolink nlid="nl18" bibid="bib19" firstref="ref25"></nolink> <nolink nlid="nl19" bibid="bib47" firstref="ref27"></nolink> <nolink nlid="nl20" bibid="bib22" firstref="ref28"></nolink> <nolink nlid="nl21" bibid="bib45" firstref="ref30"></nolink> <nolink nlid="nl22" bibid="bib42" firstref="ref31"></nolink> <nolink nlid="nl23" bibid="bib16" firstref="ref32"></nolink> <nolink nlid="nl24" bibid="bib13" firstref="ref33"></nolink> <nolink nlid="nl25" bibid="bib17" firstref="ref36"></nolink> <nolink nlid="nl26" bibid="bib29" firstref="ref37"></nolink> <nolink nlid="nl27" bibid="bib39" firstref="ref40"></nolink> <nolink nlid="nl28" bibid="bib12" firstref="ref41"></nolink> <nolink nlid="nl29" bibid="bib41" firstref="ref44"></nolink> <nolink nlid="nl30" bibid="bib21" firstref="ref45"></nolink> <nolink nlid="nl31" bibid="bib24" firstref="ref46"></nolink> <nolink nlid="nl32" bibid="bib33" firstref="ref52"></nolink> <nolink nlid="nl33" bibid="bib43" firstref="ref58"></nolink> <nolink nlid="nl34" bibid="bib38" firstref="ref59"></nolink> <nolink nlid="nl35" bibid="bib35" firstref="ref60"></nolink> <nolink nlid="nl36" bibid="bib27" firstref="ref61"></nolink> <nolink nlid="nl37" bibid="bib11" firstref="ref62"></nolink> <nolink nlid="nl38" bibid="bib37" firstref="ref65"></nolink> <nolink nlid="nl39" bibid="bib46" firstref="ref67"></nolink>
Header DbId: eric
DbLabel: ERIC
An: EJ1484459
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Who Is Hooked on AI? The Role of the Big Five Personality Traits in Compulsive ChatGPT Use among Chinese Students
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Yinyao+Hu%22">Yinyao Hu</searchLink> (ORCID <externalLink term="http://orcid.org/0009-0004-2868-2274">0009-0004-2868-2274</externalLink>)<br /><searchLink fieldCode="AR" term="%22Chee-Seng+Tan%22">Chee-Seng Tan</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-2474-6942">0000-0003-2474-6942</externalLink>)<br /><searchLink fieldCode="AR" term="%22Shimeng+Wang%22">Shimeng Wang</searchLink> (ORCID <externalLink term="http://orcid.org/0009-0006-8802-4200">0009-0006-8802-4200</externalLink>)<br /><searchLink fieldCode="AR" term="%22Hanyun+Zhang%22">Hanyun Zhang</searchLink> (ORCID <externalLink term="http://orcid.org/0009-0003-0256-0271">0009-0003-0256-0271</externalLink>)<br /><searchLink fieldCode="AR" term="%22Jiahui+Qian%22">Jiahui Qian</searchLink> (ORCID <externalLink term="http://orcid.org/0009-0002-9707-3910">0009-0002-9707-3910</externalLink>)<br /><searchLink fieldCode="AR" term="%22Yihan+Wang%22">Yihan Wang</searchLink> (ORCID <externalLink term="http://orcid.org/0009-0003-9418-5658">0009-0003-9418-5658</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Asia-Pacific+Education+Researcher%22"><i>Asia-Pacific Education Researcher</i></searchLink>. 2025 34(5):1899-1907.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 9
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Personality+Traits%22">Personality Traits</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Neurosis%22">Neurosis</searchLink><br /><searchLink fieldCode="DE" term="%22Extraversion+Introversion%22">Extraversion Introversion</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1007/s40299-025-01001-0
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0119-5646<br />2243-7908
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The rapid growth of artificial intelligence (AI) technologies, such as ChatGPT, presents both opportunities and challenges. While some studies suggest that AI use can be beneficial, others have identified detrimental effects on performance. Drawing on literature that explores the relationship between personality traits and compulsive technology use, this study investigated whether the Big Five personality traits contribute to compulsive ChatGPT use. In this cross-sectional study, 207 university students in China completed a survey measuring compulsive ChatGPT use, Big Five personality traits, and demographic information. Hierarchical multiple regression analysis revealed that compulsive ChatGPT use was positively associated with neuroticism and negatively associated with agreeableness, after controlling for age. No significant relationships were found between compulsive ChatGPT use and openness, conscientiousness, or extraversion. This study provides empirical evidence of the role of personality traits in compulsive ChatGPT use. The findings may help educators identify students at risk for compulsive ChatGPT use, particularly those high in neuroticism, and suggest directions for potential interventions.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1484459
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1484459
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s40299-025-01001-0
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 9
        StartPage: 1899
    Subjects:
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Technology Uses in Education
        Type: general
      – SubjectFull: Personality Traits
        Type: general
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: College Students
        Type: general
      – SubjectFull: Neurosis
        Type: general
      – SubjectFull: Extraversion Introversion
        Type: general
      – SubjectFull: China
        Type: general
    Titles:
      – TitleFull: Who Is Hooked on AI? The Role of the Big Five Personality Traits in Compulsive ChatGPT Use among Chinese Students
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Yinyao Hu
      – PersonEntity:
          Name:
            NameFull: Chee-Seng Tan
      – PersonEntity:
          Name:
            NameFull: Shimeng Wang
      – PersonEntity:
          Name:
            NameFull: Hanyun Zhang
      – PersonEntity:
          Name:
            NameFull: Jiahui Qian
      – PersonEntity:
          Name:
            NameFull: Yihan Wang
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 10
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 0119-5646
            – Type: issn-electronic
              Value: 2243-7908
          Numbering:
            – Type: volume
              Value: 34
            – Type: issue
              Value: 5
          Titles:
            – TitleFull: Asia-Pacific Education Researcher
              Type: main
ResultId 1