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Citation: Xiao, Y., & Zheng, L. (2025).
Can ChatGPT Boost Students’
Employment Confidence? A
Pioneering Booster for Career
Readiness. Behavioral Sciences,15(3),
362. https://doi.org/10.3390/
bs15030362
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Article
Can ChatGPT Boost Students’ Employment Confidence?
A Pioneering Booster for Career Readiness
Yu Xiao 1and Li Zheng 2,*
1Institute of Education, Tsinghua University, Beijing 100084, China; xiaoyu921@tsinghua.edu.cn
2School of Education, Shanghai Normal University, Shanghai 200234, China
*Correspondence: lizheng@shnu.edu.cn
Abstract: This study examines the impact of ChatGPT on university students’ employment
confidence, utilizing comprehensive methodologies such as regression analysis, Inverse
Probability Weighting (IPW), and Structural Equation Modeling (SEM). The results indicate
that the regular use of ChatGPT significantly enhances students’ confidence in securing
employment, with stronger effects observed among undergraduate students and those in
social sciences. Additionally, this study reveals that students’ experience with ChatGPT
plays a partial mediating role in this effect, underscoring the importance of user interaction
in realizing the benefits of AI tools. These findings suggest that ChatGPT not only improves
cognitive abilities and career-related knowledge but also boosts students’ proactive job-
seeking behaviors, fostering increased job market readiness. The implications are far-
reaching, highlighting how AI tools can enhance career development support, particularly
for students at earlier stages of their academic journey. As AI technologies continue to
influence education, this study offers valuable insights into how such tools can effectively
prepare students for the job market, potentially contributing to future research and shaping
educational practices in ways that address employment challenges.
Keywords: ChatGPT; university student; employment confidence; inverse probability
weighting; structural equation modeling
1. Introduction
The release of ChatGPT (https://chatgpt.com/) in November 2022 marked a signifi-
cant milestone in the development of artificial intelligence (AI), catapulting AI technology
into the global spotlight. With millions of users across the globe and extensive media cover-
age, AI-powered tools saw unprecedented growth, creating new opportunities in education,
business, and beyond. ChatGPT, a language generation model developed by OpenAI,
rapidly gained global attention for its natural language processing capabilities. This break-
through in AI has sparked a revolution across various sectors
(García-Alonso et al.,2024
;
Pérez-Núñez,2023;Pack & Maloney,2023). As AI technology continues to evolve, AI
tools, especially generative models like ChatGPT, have infiltrated fields such as educa-
tion and career development. These tools are revolutionizing academic and professional
landscapes by enabling researchers, educators, and professionals to streamline workflows
and enhance productivity, creativity, and access to valuable insights (Tang et al.,2024;
Vinchon et al.,2024;J. Zhang et al.,2024).
Employment is a significant concern for college students, as many struggle to secure
jobs after graduation. Recent studies highlight that the employment rate for recent col-
lege graduates in the U.S. has been declining, with only 40% of full-time undergraduate
Behav. Sci. 2025,15, 362 https://doi.org/10.3390/bs15030362
Behav. Sci. 2025,15, 362 2 of 20
students employed in 2020 (National Center for Education Statistics,2022). Additionally,
the unemployment rate for recent graduates increased to 5.3% in the third quarter of 2024
(
Federal Reserve Bank of New York,2024
). As a result, many students feel uncertain about
their future career prospects; a survey revealed that nearly 66% of college students lack
confidence in their ability to find a job after graduation (Inside Higher Ed,2018). For college
students, employment confidence directly affects students’ job-seeking attitudes, enthusi-
asm, and ultimate employment outcomes. Students with higher job confidence are more
proactive in their job search and tend to secure employment more quickly (
Aufa et al.,2024;
Chen et al.,2023;Guan et al.,2013). Job search self-efficacy, a key component of employ-
ment confidence, has been shown to positively influence the number of job offers received
(Liu et al.,2014;Moynihan et al.,2003). In addition, self-confidence is a predictor of job
readiness, which is essential for effective job-seeking and achieving favorable employment
outcomes (Ristiani & Lusianingrum,2022;D. Wang et al.,2022).
ChatGPT has emerged as a versatile tool in higher education, offering significant
benefits beyond academic support. Research indicates that ChatGPT can enhance students’
cognitive skills and career-relevant knowledge, with frequent usage positively impacting
these areas, as high-quality ChatGPT outputs significantly improve cognitive skills and
career-relevant knowledge (Essel et al.,2024;Suriano et al.,2025;Urban et al.,2024). In
addition, ChatGPT provides personalized learning experiences that support cognitive
development and career-relevant knowledge acquisition (Dahri et al.,2024). Beyond
academics, ChatGPT assists students in career planning, job-seeking skills, and personal
development, making it a comprehensive tool for student success (Bhullar et al.,2024). This
multifaceted support helps students prepare for the job market and enhances their overall
readiness for professional challenges.
Gender stereotypes, education systems, and economic conditions all shape employ-
ment confidence. Research shows that male candidates are often favored over equally
qualified female candidates, particularly in STEM fields, where women report lower confi-
dence despite high career commitment (Emeka,2024;Ananthram et al.,2023). Education
systems also play a crucial role—countries with vocational training models, like Germany
and Switzerland, provide clear career pathways, whereas academic-driven systems, such
as in the U.S., may leave students with fewer practical job skills (Sweet,2012). Addition-
ally, employment confidence varies by economic development; in developing countries,
job market instability lowers confidence, while in developed economies, labor shortages
create better opportunities for graduates (James,2021). Given these challenges, AI tools,
like ChatGPT, could help bridge gaps by providing career guidance, personalized job
recommendations, and skill development support, making employment pathways more
accessible and equitable.
Negative discussions surrounding AI have been found to significantly reduce students’
confidence in their expected earnings, making them feel more vulnerable to job displace-
ment and its potential impact on career prospects (Huseynov,2023;Thomson et al.,2024).
While many students acknowledge ChatGPT’s capabilities, concerns persist regarding
academic integrity violations and the changing job market, contributing to uncertainty
about AI’s long-term influence on career opportunities (Al-Abdullatif & Alsubaie,2024;
García-López et al.,2025;Yu et al.,2024).
Additionally, overreliance on ChatGPT has been linked to declines in creativity
and motivation, affecting students’ problem-solving abilities and independent thinking
(Muñoz et al.,2023;
Toma & Yánez-Pérez,2024). This decline in creativity and motivation
negatively impacts their academic performance and may result in a deficiency of critical job
skills required for the future job market (Tanvir et al.,2023). These findings highlight the
Behav. Sci. 2025,15, 362 3 of 20
dual impact of AI—while it can enhance career confidence, excessive reliance may hinder
skill acquisition—necessitating a balanced approach to AI integration in education.
While ChatGPT usage directly provides access to knowledge and assistance in task
completion, the experience of using ChatGPT, including perceived ease, reliability, and sat-
isfaction, may play a critical mediating role. This mediating effect is reflected in how users
translate technological interaction into self-efficacy and optimism in their employability. Re-
search suggests that user trust and experience with ChatGPT significantly influence behav-
ioral outcomes, such as adoption and effectiveness (
Choudhury & Shamszare,2023
). More-
over, satisfaction and interaction quality with ChatGPT have been identified as crucial fac-
tors shaping users’ attitudes toward its integration in diverse sectors
(Sökmen et al.,2024).
Based on the current situation, understanding how AI tools, like ChatGPT, influence
students’ confidence in securing employment provides valuable insights for educators,
technology developers, and policymakers. This knowledge can help them create more
effective strategies to address the challenges posed by the evolving job market, ensuring
that students are better equipped with the skills and mindset needed to navigate future
employment opportunities. By recognizing both the positive and negative impacts of AI,
stakeholders can work together to optimize career preparation programs and foster a more
resilient workforce.
2. Literature
2.1. The Role of AI Tools in Education
ChatGPT has truly transformed the landscape of personalized education by delivering
real-time, adaptive learning experiences that are meticulously designed to meet the unique
needs of each student. ChatGPT not only heightens student engagement but also signifi-
cantly boosts academic performance (Deng et al.,2024;Heung & Chiu,2025;
Lo et al.,2024)
.
By customizing educational content to align with individual learning requirements, Chat-
GPT paves the way for highly personalized educational journeys, targeting specific areas
where students need improvement and ensuring that the learning process is more impactful
and efficient (Akiba & Fraboni,2023;Kabudi et al.,2021;S. Wang et al.,2024;Li,2023;
Sreen & Majid,2024). Additionally, ChatGPT extends its educational support beyond mere
academic content, offering comprehensive assistance in writing, tutoring, and providing
personalized feedback that effectively addresses student doubts and cultivates a more
profound comprehension of intricate subjects (Royani et al.,2024;Sok & Heng,2024).
ChatGPT significantly extends its utility in the realm of career planning and guidance,
offering tailored advice on job searches, career exploration, and professional development.
ChatGPT not only aids in crafting resumes and preparing for interviews but also sup-
ports skill development, thereby becoming an invaluable asset for students mapping out
their professional trajectories. By integrating ChatGPT into career advising, it democra-
tizes access to expert guidance, thereby enhancing job readiness for students from varied
backgrounds (Akiba & Fraboni,2023;Crawford et al.,2023). Additionally, AI tools, like
ChatGPT and Pathfinder, are employed in educational and job search settings, provid-
ing customized university and career guidance and optimizing the job search process
with personalized recommendations and effective strategies (Deshmukh & Bajaj,2024;
Jain et al.,2024;Jawhar et al.,2024;Rajaram et al.,2024).
2.2. Students’ Employment Confidence
Employment confidence, a pivotal metric affecting career decisions and labor market
entry, reflects individuals’ self-assessment of employability skills, job market knowledge,
and their ability to meet employer expectations (Barron & Gravert,2022;
Qenani et al.,2014
).
High employment confidence correlates with better job prospects and psychological well-
Behav. Sci. 2025,15, 362 4 of 20
being during career transitions, spurring proactive career preparation and readiness for job
opportunities, which, in turn, enhances the likelihood of career success
(Creed et al.,2003;
R. Zhang & Jen,2024
). This confidence is shaped by self-confidence, psychological well-
being, pre-entry work experience, family support, and self-construal, with psychologi-
cal well-being being a dominant predictor (Khairunnisa et al.,2022;Bennett et al.,2023;
Inavatin et al.,2020).
Technology’s impact on employment confidence is multifaceted. Innovations in tech-
nology can bolster employment confidence by enhancing SMEs’ competitiveness and mar-
ket adaptability, creating new opportunities and economic growth (
Chege & Wang,2020
;
Meier et al.,2025;S. Wang & Zhang,2024), and by increasing productivity and creating
new roles through automation (Aghion et al.,2021). However, technology also has negative
implications, as rapid advancements in automation and robotics are linked to job insecurity
and anxiety, particularly in sectors where technological change outpaces workers’ upskilling
(Nam,2019;Nikolova et al.,2024;Yam et al.,2023). Moreover, technological uncertainty
and the potential for job displacement can lead to lower confidence levels, especially among
workers with limited adaptability to new tools and systems (Brougham & Haar,2020).
2.3. Differences in Technology Use Across Academic Levels and Fields
Studies reveal varying technology use among academic levels. Undergraduates
commonly use devices like smartphones, laptops, and desktops mainly for social and
general academic purposes but are less adept at using advanced digital tools for com-
plex tasks
(Cohen et al.,2022
;Kwon et al.,2013;Sapci et al.,2021). Graduate students
take a more strategic approach, prioritizing system and service quality for accessing
high-quality resources, like digital libraries, and seek additional training (Xu & Du,2019;
Shaw & Giacquinta,2000
). Doctoral students focus on using technology for advanced re-
search, including data management and virtual collaboration, though cultural mismatches
can be problematic (Boulton,2015). In essence, technology use patterns among students re-
flect their distinct academic needs and priorities at different stages
(Rashid & Asghar,2016).
Research reveals that students in social sciences, applied sciences, and arts and hu-
manities exhibit different technology use patterns for academic purposes. Social science
students frequently use technology, especially social media, for communication and social-
ization more than those in other fields (Campos et al.,2014). In contrast, applied science
students rely heavily on technology for data collection, analysis, and solving technical prob-
lems, consistent with their practical and research-intensive curriculum (
Moses et al.,2014
).
Arts and humanities students see technology as less critical but still important for access-
ing resources and creative work (Trusz,2020). These varying patterns underscore how
disciplinary norms and needs shape students’ technology engagement.
2.4. Ethical Considerations of AI
One of the most debated ethical challenges of AI is its potential to facilitate academic
dishonesty. AI-powered tools, including ChatGPT, can generate essays, solve mathematical
problems, and even assist in coding assignments, raising concerns about students bypassing
traditional learning processes. While AI can be a valuable educational resource, its misuse
has led to increasing calls for institutions to establish stricter policies on AI-assisted work.
Kocak (2024) conducted a comprehensive review on publication ethics in the era of AI, fo-
cusing on the risks and challenges posed by AI-generated content in academic writing. The
study identified key ethical issues such as bias, distortion, irrelevance, misrepresentation,
and plagiarism in AI-generated texts. It emphasized that even when not used maliciously,
AI outputs may unintentionally introduce inaccuracies due to flawed training data and
lack of critical reasoning. Additionally, Koçak examined the need for AI disclosure policies,
Behav. Sci. 2025,15, 362 5 of 20
arguing that academic institutions must require authors to explicitly state if AI-assisted
tools were used in their research and writing processes. The study also discussed retraction
policies for AI-generated content, highlighting recent cases where AI-created misinforma-
tion led to article retractions. Based on these findings, Koçak proposed the development of
new ethical standards and transparency guidelines to regulate AI’s role in scholarly work.
AI’s reliance on massive datasets for training raises serious legal and ethical questions
regarding copyright infringement. AI models, including generative AI like ChatGPT,
often use copyrighted materials without explicit permission from authors, leading to legal
disputes over intellectual property rights. Samuelson (2023) analyzed how generative AI
interacts with existing copyright laws, particularly in the U.S. and Europe, highlighting key
challenges such as whether AI training on copyrighted content constitutes fair use, whether
AI-generated outputs qualify as derivative works under copyright law, and who should
bear legal responsibility for copyright violations—AI developers, users, or platforms. The
study found that legal precedents remain unclear, with no consensus on whether using
copyrighted material for AI training falls under fair use, creating significant regulatory
uncertainty. Samuelson also emphasized the urgent need for updated copyright policies
that define AI’s role in content creation, advocating for compensation models for creators
whose works are used in AI training and stronger transparency requirements for AI dataset
sources. Without clear legal frameworks, courts will struggle to regulate AI-generated
works, potentially leading to widespread copyright disputes that challenge both intellectual
property rights and AI innovation.
Beyond legal and academic concerns, AI also poses a significant environmental chal-
lenge due to the high energy consumption required for training and deploying large AI
models. AI systems, particularly those used in healthcare and finance, contribute to sub-
stantial carbon emissions, raising ethical concerns about their sustainability. Richie (2022)
emphasized that discussions on AI ethics often overlook its carbon footprint, arguing that
environmental sustainability should be a core consideration in AI policy-making. The
study proposed a framework for sustainable AI that integrates health, justice, and resource
conservation principles into AI development. As AI adoption expands, researchers have
called for energy-efficient AI models and greater transparency in AI-driven industries to
mitigate environmental damage.
These ethical considerations are particularly relevant in discussions about AI’s role in
shaping students’ career confidence and preparedness. While AI can possibly support skill
development and career readiness, issues of academic dishonesty, copyright infringement,
and environmental responsibility must be carefully managed to ensure AI serves as an
equitable and sustainable tool.
3. Research Questions
As highlighted in the introduction, the release of ChatGPT marked a turning point in
the adoption of artificial intelligence (AI) in education and career development. While Chat-
GPT and similar AI tools have demonstrated the potential to enhance learning, streamline
workflows, and support career readiness, significant challenges remain. The introduction
underscores pressing concerns, such as declining employment confidence among college
students and the critical role of self-efficacy in career preparation and success. It also
emphasizes how AI tools like ChatGPT can address these issues through personalized
support and skill enhancement.
The literature review further detailed how ChatGPT has been shown to enhance cogni-
tive skills, career-relevant knowledge, and self-efficacy. Additionally, studies highlight the
importance of user experience as a mediator in determining the effectiveness of AI tools.
However, despite these insights, gaps remain in understanding how ChatGPT influences
Behav. Sci. 2025,15, 362 6 of 20
employment confidence among diverse college student populations. Specifically, there
is limited exploration of how these effects vary across academic levels (undergraduate,
graduate, and doctoral students) and academic fields (social sciences, applied sciences,
and arts and humanities). Furthermore, while user experience is acknowledged as crit-
ical, its specific mediating role in ChatGPT usage and employment confidence remains
underexplored.
To address these gaps, this study investigates the following research questions (see
Figure 1):
Behav. Sci. 2025, 15, x FOR PEER REVIEW 6 of 21
there is limited exploration of how these effects vary across academic levels (undergrad-
uate, graduate, and doctoral students) and academic fields (social sciences, applied sci-
ences, and arts and humanities). Furthermore, while user experience is acknowledged as
critical, its specific mediating role in ChatGPT usage and employment confidence remains
underexplored.
To address these gaps, this study investigates the following research questions (see
Figure 1):
RQ1: How does ChatGPT usage influence college students’ employment confidence?
RQ2: How does ChatGPT usage influence college students’ employment confidence
across academic levels and fields?
RQ3: What roles do ChatGPT user experiences play in mediating its influence on col-
lege students’ employment confidence?
Figure 1. Research framework illustrating the three research questions.
4. Methods
4.1. Data
This study utilized data from the Global ChatGPT Student Survey conducted by the
Faculty of Public Administration, University of Ljubljana, aiming to understand how
ChatGPT shapes higher education students’ experiences and learning outcomes by spe-
cifically analyzing how students with diverse cultural backgrounds view ChatGPT. In to-
tal, over 23,000 higher education students from 109 countries and territories—all at least
18 years old and currently enrolled in a higher education institution—were surveyed
about their early experiences using ChatGPT in academic contexts. The survey employed
an online questionnaire, administered via the 1KA (One Click Survey) platform, that com-
prised 42 primarily closed-ended questions (along with some open-ended ones) targeting
higher education students’ initial experiences with ChatGPT. The questions were
Figure 1. Research framework illustrating the three research questions.
RQ1: How does ChatGPT usage influence college students’ employment confidence?
RQ2: How does ChatGPT usage influence college students’ employment confidence
across academic levels and fields?
RQ3: What roles do ChatGPT user experiences play in mediating its influence on
college students’ employment confidence?
4. Methods
4.1. Data
This study utilized data from the Global ChatGPT Student Survey conducted by the
Faculty of Public Administration, University of Ljubljana, aiming to understand how Chat-
GPT shapes higher education students’ experiences and learning outcomes by specifically
analyzing how students with diverse cultural backgrounds view ChatGPT. In total, over
23,000 higher education students from 109 countries and territories—all at least 18 years
old and currently enrolled in a higher education institution—were surveyed about their
early experiences using ChatGPT in academic contexts. The survey employed an online
questionnaire, administered via the 1KA (One Click Survey) platform, that comprised
Behav. Sci. 2025,15, 362 7 of 20
42 primarily closed-ended questions (along with some open-ended ones) targeting higher
education students’ initial experiences with ChatGPT. The questions were organized into
themes exploring usage frequency, motivations for using ChatGPT, satisfaction and attitude
toward ChatGPT as a learning tool, anticipated labor market implications, etc. Measured
predominantly on 5-point Likert scales (e.g., “strongly disagree” to “strongly agree”), com-
plemented by single-choice and open-ended items, this structure allowed for a detailed
examination of how students across diverse cultural contexts perceive ChatGPT’s impact
on their learning processes and future career prospects. The demographic characteristics of
the participants are presented in Table 1.
Table 1. Demographic table.
Demographic Characteristic Declaration N Percentage
Gender
Male 9279 40.59%
Female 13,247 57.95%
Other 103 0.45%
Prefer not to disclose 230 1.01%
Area
Urban 11,326 64.33%
Suburban 3481 19.77%
Rural 2799 15.90%
Student status Full-time 19,266 85.27%
Part-time 3327 14.73%
Study level
Undergraduate 18,784 83.31%
Postgraduate 2856 12.67%
Doctoral 906 4.02%
Study field
Arts and humanities 2696 12.01%
Social sciences 9290 41.37%
Applied sciences 7764 34.58%
Natural and life sciences 2705 12.05%
Economic status
Significantly below-average 1172 6.65%
Below-average 3480 19.74%
Average 9830 55.75%
Above-average 2728 15.47%
Significantly above-average 423 2.40%
Variables Mean S.D. Max Min
Age 23.13 6.82 100 18
4.2. Variables
4.2.1. Dependent Variables
This study selects students’ confidence about getting a job as the dependent variable.
In the Global ChatGPT Student Survey, students’ confidence about getting a job was
measured with the question: “Do you feel confident about getting a job after you complete
your studies?”. The specific items of the dependent variable are presented in Table 2.
Table 2. List of survey items and corresponding constructs.
Dependent Variable Question/Items Options
Confidence about getting a job
Do you feel confident about getting a
job after you complete your studies?
1 = Not at all confident
2 = Slightly confident
3 = Moderately confident
4 = Very confident
5 = Extremely confident
Behav. Sci. 2025,15, 362 8 of 20
Table 2. Cont.
Dependent Variable Question/Items Options
Independent variable Question/Items Options
ChatGPT use Have you ever used ChatGPT? 1 = Yes
0 = No
ChatGPT frequency To what extent do you use ChatGPT
in general?
1 = Rarely
2 = Occasionally
3 = Moderately
4 = Considerably
5 = Extensively
Control variable Question/Items Options
Gender What is your gender? 1 = Male
0 = Female
Age How old are you (in years)?
Area Which of the characteristics below
describes the area you live in?
1 = Rural
2 = Suburban
3 = Urban
Student status What is your student status? 1 = Full-time
0 = Part-time
Economic status What is your economic status?
1 = Rarely
2 = Occasionally
3 = Moderately
4 = Considerably
5 = Extensively
Government funded Is your institution
publicly/government funded?
1 = Yes
0 = No
Mediating variable Question/Items Options
ChatGPT experience What is your experience with
ChatGPT?
1 = Very bad
2 = Bad
3 = Neutral
4 = Good
5 = Very good
4.2.2. Independent Variables
This study introduces two independent variables, ChatGPT use and ChatGPT fre-
quency, to describe participants’ utilization of ChatGPT. ChatGPT use is coded as 1 = Yes
and 0 = No to indicate whether the participant has used ChatGPT. ChatGPT frequency is
established to represent the frequency of ChatGPT use by the participants. The specific
items of the independent variables are presented in Table 2.
4.2.3. Control Variables
Research has confirmed that some demographic, economic, and social variables have
an impact on individuals’ confidence in employment (Carlin et al.,2018;Zheng et al.,2022).
Thus, this study employs gender (since only a few respondents selected “other” and “prefer
not to disclose”, we are treating these responses as missing values), age, area, student
status, economic status, and government funded as the control variables. The specific items
of the control variables are presented in Table 2.
Behav. Sci. 2025,15, 362 9 of 20
4.2.4. Mediating Variable
This study employs students’ experience with ChatGPT as the mediating variable. In
the Global ChatGPT Student Survey, students’ experience with ChatGPT was measured
by the question: “What is your experience with ChatGPT?”. The specific items of the
mediating variable are presented in Table 2.
4.3. Models
To explore the effect of ChatGPT on students’ employment confidence, this study
constructs a regression model using STATA 17:
Con f i dence =β0+β1·ChatGPTuse ×Ch atGP T f reque ncy +β2·Gender +β3·Age
+β4·Area +β5·SS +β6·ES +β7·GF +ε(1)
ChatGPTuse represents students’ utilization of ChatGPT; ChatGPTfrequency repre-
sents the frequency of ChatGPT use by students; Gender represents students’ gender; Age
represents students’ age; Area represents the area students live in; SS represents student
status; ES represents students’ economic status; GF represents whether students’ institution
is funded by the government;
β0
is the intercept;
β1
–
β7
are the regression coefficients, and
εis the error term.
To address the endogeneity of the effect of ChatGPT use on students’ views on AI, this
study employs Inverse Probability Weighting (IPW) to conduct a robustness test. IPW is a
commonly used method in statistical analyses and causal inference to deal with selection
bias or confounding variables in observational data. Its basic idea is to assign a weight to
each observation so that the distribution of the treatment group and the control group on
the confounding variable is balanced so that the treatment effect can be estimated more
accurately (Cattaneo,2010).
Subsequently, this study constructed a Structural Equation Modeling (SEM) framework
(see Figure 2) to delve into the underlying mechanisms by which ChatGPT use influences
students’ employment confidence utilizing STATA 18. To evaluate the model’s goodness of
fit, the study employed the Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), Root
Mean Squared Error of Approximation (RMSEA), and Standardized Root Mean Squared
Residual (SRMR) as metrics. An SEM model is considered to have an excellent fit when the
CFI and TLI approach 1, while an RMSEA value below 0.08 and an SRMR value below 0.05
are indicative of an acceptable fit (Kline,2005;Hair et al.,2006;Markus,2012). The SEM
model developed in this study yielded fit indices of CFI 0.999, TLI 0.979, RMSEA 0.017, and
SRMR 0.003, indicating that the model provides an adequate representation of the intrinsic
relationships embedded within the dataset.
Behav. Sci. 2025, 15, x FOR PEER REVIEW 9 of 21
4.3. Models
To explore the effect of ChatGPT on students’ employment confidence, this study
constructs a regression model using STATA 17:
𝐶𝑜𝑛𝑓𝑖𝑑𝑒𝑛𝑐𝑒 𝛽𝛽
𝐶ℎ𝑎𝑡𝐺𝑃𝑇𝑢𝑠𝑒 𝐶ℎ𝑎𝑡𝐺𝑃𝑇𝑓𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦 𝛽𝐺𝑒𝑛𝑑𝑒𝑟𝛽
𝐴
𝑔𝑒
𝛽
𝐴
𝑟𝑒𝑎 𝛽𝑆𝑆𝛽
𝐸𝑆𝛽
𝐺𝐹𝜀 (1)
ChatGPTuse represents students’ utilization of ChatGPT; ChatGPTfrequency repre-
sents the frequency of ChatGPT use by students; Gender represents students’ gender; Age
represents students’ age; Area represents the area students live in; SS represents student
status; ES represents students’ economic status; GF represents whether students’ institu-
tion is funded by the government; β
0
is the intercept; β
1
–β
7
are the regression coefficients,
and ε is the error term.
To address the endogeneity of the effect of ChatGPT use on students’ views on AI,
this study employs Inverse Probability Weighting (IPW) to conduct a robustness test. IPW
is a commonly used method in statistical analyses and causal inference to deal with selec-
tion bias or confounding variables in observational data. Its basic idea is to assign a weight
to each observation so that the distribution of the treatment group and the control group
on the confounding variable is balanced so that the treatment effect can be estimated more
accurately (Caaneo, 2010).
Subsequently, this study constructed a Structural Equation Modeling (SEM) frame-
work (see Figure 2) to delve into the underlying mechanisms by which ChatGPT use in-
fluences students’ employment confidence utilizing STATA 18. To evaluate the model’s
goodness of fit, the study employed the Comparative Fit Index (CFI), Tucker–Lewis Index
(TLI), Root Mean Squared Error of Approximation (RMSEA), and Standardized Root
Mean Squared Residual (SRMR) as metrics. An SEM model is considered to have an ex-
cellent fit when the CFI and TLI approach 1, while an RMSEA value below 0.08 and an
SRMR value below 0.05 are indicative of an acceptable fit (Kline, 2005; Hair et al., 2006;
Markus, 2012). The SEM model developed in this study yielded fit indices of CFI 0.999,
TLI 0.979, RMSEA 0.017, and SRMR 0.003, indicating that the model provides an adequate
representation of the intrinsic relationships embedded within the dataset.
Figure 2. SEM pathway linking ChatGPT use to students’ employment confidence.
5. Findings
5.1. Results of Regression Model
The results of the regression model (see Table 3) reveal that the interaction of
ChatGPT use and ChatGPT frequency exerts a significant positive effect on students’ em-
ployment confidence (β = 0.050, p < 0.001), indicating that students who use ChatGPT have
higher employment confidence than those who do not, and the more frequently they use
Figure 2. SEM pathway linking ChatGPT use to students’ employment confidence.
Behav. Sci. 2025,15, 362 10 of 20
5. Findings
5.1. Results of Regression Model
The results of the regression model (see Table 3) reveal that the interaction of ChatGPT
use and ChatGPT frequency exerts a significant positive effect on students’ employment
confidence (
β
= 0.050, p< 0.001), indicating that students who use ChatGPT have higher
employment confidence than those who do not, and the more frequently they use ChatGPT,
the higher their employment confidence. Additionally, age (
β= 0.015
,p< 0.001), area
(
β= 0.028
,p= 0.045), student status (
β
= 0.136,
p< 0.001
), and economic status (
β
= 0.124,
p< 0.001
) have positive effects on students’ employment confidence, indicating that older
students, those from urban areas, full-time students, and students with higher economic
status have higher employment confidence. Moreover, gender (
β
= 0.091, p< 0.001) has a
positive effect on students’ employment confidence, indicating that male students tend to
have higher employment confidence than female students.
Table 3. Results of regression analysis.
Independent Variable βS.E. t p 95% Conf. Interval
ChatGPTuse ×ChatGPTfrequency 0.050 0.010 5.15 <0.001 0.031 0.069
Gender 0.091 0.021 4.25 <0.001 0.049 0.132
Age 0.015 0.002 8.25 <0.001 0.011 0.019
Area 0.028 0.014 2.01 0.045 0.001 0.056
Student status 0.136 0.033 4.08 <0.001 0.071 0.201
Economic status 0.124 0.013 9.72 <0.001 0.099 0.149
Government fund −0.023 0.026 −0.87 0.386 −0.073 0.028
Constant 2.290 0.082 27.86 <0.001 2.129 2.451
N = 9384
R2= 0.0249
5.2. Results of Robustness Test
This study uses ChatGPT use (1 = Yes, 0 = No) as the grouping variable and the
construct-treated group and control group through IPW to test the robustness of the effect
of ChatGPT use on students’ employment confidence. The results of IPW (see Table 4)
reveal that the use of ChatGPT has a significant positive average treatment effect on the
treated (ATET) for students’ employment confidence (
β
= 0.044, p= 0.035), indicating that
the findings of this study are robust.
Table 4. Results of IPW.
βS.D. z p 95% Conf. Interval
ATET
chatgptuse: yes vs. no 0.044 0.021 2.11 0.035 0.003 0.084
Pomean
chatgptuse: no 3.292 0.018 184.38 <0.001 3.257 3.327
N = 13,635
5.3. Results of Heterogeneity Analysis
This study conducted a heterogeneity analysis of the effect of ChatGPT use on students’
employment confidence by categorizing students into different subgroups based on their
study level and study field. The results of the heterogeneity analysis (see Table 5) suggest
that the positive effect of ChatGPT use on undergraduate students’ employment confidence
is statistically significant (
β
= 0.048, p< 0.001), whereas the effects on graduate students’
Behav. Sci. 2025,15, 362 11 of 20
and doctoral students’ employment confidence are not statistically significant (p> 0.05).
Additionally, the positive effect of ChatGPT use on students from social sciences (
β
= 0.048,
p= 0.003) is significantly higher compared to students from applied sciences (
β
= 0.040,
p= 0.009
), while the effects on students from arts and humanities and natural and life
sciences are not statistically significant (p> 0.05).
Table 5. Results of heterogeneity analysis.
Subgroup Independent Variable βS.E. t p 95% Conf. Interval
Undergraduate
(N = 7838,
R2= 0.0183)
ChatGPTuse ×ChatGPTfrequency 0.048 0.011 4.52 <0.001 0.027 0.069
Gender 0.083 0.023 3.58 <0.001 0.037 0.128
Age 0.013 0.003 4.87 <0.001 0.008 0.018
Area 0.024 0.015 1.61 0.108 −0.005 0.054
Student status 0.206 0.038 5.43 <0.001 0.131 0.280
Economic status 0.102 0.014 7.32 <0.001 0.075 0.129
Government fund −0.023 0.028 −0.84 0.403 −0.078 0.032
Constant 2.343 0.098 24.03 <0.001 2.152 2.534
Graduate
(N = 1095,
R2= 0.0426)
ChatGPTuse ×ChatGPTfrequency 0.038 0.029 1.31 0.189 −0.019 0.094
Gender 0.202 0.065 3.12 0.002 0.075 0.329
Age −0.000 0.004 −0.08 0.933 −0.009 0.008
Area 0.053 0.043 1.21 0.226 −0.033 0.138
Student status −0.126 0.085 −1.49 0.136 −0.293 0.040
Economic status 0.196 0.038 5.15 <0.001 0.121 0.270
Government fund −0.035 0.082 −0.43 0.670 −0.197 0.127
Constant 2.753 0.238 11.57 <0.001 2.286 3.220
Doctoral
(N = 414,
R2= 0.0625)
ChatGPTuse ×ChatGPTfrequency 0.077 0.048 1.60 0.111 −0.018 0.171
Gender 0.119 0.105 1.13 0.260 −0.088 0.325
Age 0.004 0.006 0.68 0.495 −0.008 0.016
Area −0.026 0.081 −0.33 0.743 −0.185 0.132
Student status −0.052 0.138 −0.38 0.706 −0.323 0.219
Economic status 0.277 0.068 4.05 <0.001 0.142 0.411
Government fund −0.215 0.142 −1.52 0.130 −0.493 0.064
Constant 2.712 0.423 6.41 <0.001 1.880 3.544
Arts and
humanities
(N = 1132,
R2= 0.0188)
ChatGPTuse ×ChatGPTfrequency 0.037 0.030 1.22 0.222 −0.022 0.095
Gender −0.041 0.068 −0.60 0.546 −0.174 0.092
Age 0.021 0.005 4.11 <0.001 0.011 0.032
Area 0.027 0.040 0.66 0.508 −0.052 0.105
Student status 0.070 0.090 0.78 0.438 −0.107 0.246
Economic status −0.023 0.037 −0.63 0.531 −0.095 0.049
Government fund −0.124 0.088 −1.41 0.159 −0.296 0.049
Constant 2.522 0.236 10.70 <0.001 2.060 2.984
Social sciences
(N = 3761,
R2= 0.0282)
ChatGPTuse ×ChatGPTfrequency 0.048 0.016 2.99 0.003 0.016 0.079
Gender 0.023 0.035 0.67 0.500 −0.044 0.091
Age 0.017 0.003 6.53 <0.001 0.012 0.022
Area 0.028 0.021 1.31 0.189 −0.014 0.069
Student status 0.174 0.050 3.51 <0.001 0.077 0.272
Economic status 0.136 0.020 6.75 <0.001 0.097 0.176
Government fund −0.023 0.040 −0.57 0.566 −0.100 0.055
Constant 2.164 0.123 17.52 <0.001 1.922 2.406
Behav. Sci. 2025,15, 362 12 of 20
Table 5. Cont.
Subgroup Independent Variable βS.E. t p 95% Conf. Interval
Applied
sciences
(N = 3435,
R2= 0.0271)
ChatGPTuse ×ChatGPTfrequency 0.040 0.015 2.62 0.009 0.010 0.070
Gender 0.085 0.034 2.47 0.013 0.018 0.152
Age 0.015 0.003 4.24 <0.001 0.008 0.022
Area 0.012 0.025 0.47 0.642 −0.037 0.060
Student status 0.048 0.061 0.79 0.428 −0.071 0.167
Economic status 0.160 0.021 7.61 <0.001 0.119 0.201
Government fund 0.003 0.041 0.07 0.948 −0.078 0.083
Constant 2.439 0.148 16.46 <0.001 2.148 2.729
Natural and
life sciences
(N = 1007
R2= 0.0113)
ChatGPTuse ×ChatGPTfrequency 0.027 0.030 0.89 0.375 −0.032 0.086
Gender 0.100 0.066 1.52 0.130 −0.030 0.230
Age 0.002 0.006 0.32 0.747 −0.009 0.013
Area −0.002 0.042 −0.05 0.958 −0.084 0.080
Student status 0.122 0.102 1.20 0.231 −0.078 0.322
Economic status 0.102 0.040 2.56 0.011 0.024 0.180
Government fund 0.001 0.093 0.01 0.991 −0.181 0.183
Constant 2.851 0.253 11.25 <0.001 2.353 3.348
5.4. Results of SEM
The results of SEM (see Figure 3) show that ChatGPT use has a significant positive
direct effect on students’ employment confidence (
β
= 0.062, p< 0.001). The indirect effect
of ChatGPT use on students’ employment confidence through ChatGPT experience is 0.033
(0.274
×
0.119), and this coefficient is statistically significant (p< 0.001). These findings
suggest that ChatGPT experience partially mediates the relationship between ChatGPT use
and students’ employment confidence.
Behav. Sci. 2025, 15, x FOR PEER REVIEW 12 of 21
0.033 (0.274 × 0.119), and this coefficient is statistically significant (p < 0.001). These find-
ings suggest that ChatGPT experience partially mediates the relationship between
ChatGPT use and students’ employment confidence.
Figure 3. Results of SEM.
6. Discussion
6.1. RQ1: The Role of ChatGPT in Enhancing Employment Confidence
The finding that ChatGPT usage significantly enhances students’ employment confi-
dence aligns with broader discussions in the literature about the transformative role of AI
tools in education and career readiness. ChatGPT, as a generative AI model, has demon-
strated the ability to provide personalized, real-time support, which enhances students’
cognitive and career-relevant skills (Essel et al., 2024; Dahri et al., 2024). These capabilities,
coupled with frequent usage, allow students to engage more deeply with learning mate-
rials and career preparation, fostering confidence in their employability. Additionally, this
result resonates with studies emphasizing the importance of self-efficacy and job readi-
ness in career success (Liu et al., 2014; Ristiani & Lusianingrum, 2022). As ChatGPT assists
students in practical tasks such as resume building, interview preparation, and job search
strategies, it contributes to building the skills necessary for navigating the job market ef-
fectively (Bhullar et al., 2024).
While ChatGPT offers substantial benefits, some studies also underscore critical
drawbacks that warrant caution. Overreliance on ChatGPT may inadvertently diminish
students’ intrinsic motivation and creativity, as observed in a reduced problem-solving
initiative among frequent users (Muñoz et al., 2023; Toma & Yánez-Pérez, 2024). This
aligns with concerns that AI tools could foster dependency, weakening the development
of independent critical thinking—a skill vital for workplace adaptability. Furthermore, the
tool’s uneven efficacy across disciplines risks reinforcing existing inequities; for instance,
students in creativity-driven fields, like arts or empirical sciences, may perceive ChatGPT
as irrelevant or even detrimental to their skill development (Trusz, 2020). Ethical dilem-
mas, such as academic integrity violations and job displacement anxieties, further compli-
cate its adoption (Bin-Nashwan et al., 2023; Huseynov, 2023).
These findings highlight the pivotal role of access and usage frequency, suggesting
that the equitable distribution of AI tools in education is essential to maximize their im-
pact. At the same time, they advocate for balanced integration strategies that address both
AI’s potential and its sociocognitive risks, ensuring students cultivate resilience alongside
technological proficiency. To optimize ChatGPT’s role in career development, educational
institutions must implement structured guidelines that promote its use as a supplement
rather than a substitute for critical thinking and independent learning.
Figure 3. Results of SEM.
6. Discussion
6.1. RQ1: The Role of ChatGPT in Enhancing Employment Confidence
The finding that ChatGPT usage significantly enhances students’ employment confi-
dence aligns with broader discussions in the literature about the transformative role of AI
tools in education and career readiness. ChatGPT, as a generative AI model, has demon-
strated the ability to provide personalized, real-time support, which enhances students’
cognitive and career-relevant skills (Essel et al.,2024;Dahri et al.,2024). These capabilities,
coupled with frequent usage, allow students to engage more deeply with learning materials
and career preparation, fostering confidence in their employability. Additionally, this result
resonates with studies emphasizing the importance of self-efficacy and job readiness in
career success (Liu et al.,2014;Ristiani & Lusianingrum,2022). As ChatGPT assists students
in practical tasks such as resume building, interview preparation, and job search strategies,
Behav. Sci. 2025,15, 362 13 of 20
it contributes to building the skills necessary for navigating the job market effectively
(Bhullar et al.,2024).
While ChatGPT offers substantial benefits, some studies also underscore critical draw-
backs that warrant caution. Overreliance on ChatGPT may inadvertently diminish students’
intrinsic motivation and creativity, as observed in a reduced problem-solving initiative
among frequent users (Muñoz et al.,2023;Toma & Yánez-Pérez,2024). This aligns with con-
cerns that AI tools could foster dependency, weakening the development of independent
critical thinking—a skill vital for workplace adaptability. Furthermore, the tool’s uneven
efficacy across disciplines risks reinforcing existing inequities; for instance, students in
creativity-driven fields, like arts or empirical sciences, may perceive ChatGPT as irrelevant
or even detrimental to their skill development (Trusz,2020). Ethical dilemmas, such as aca-
demic integrity violations and job displacement anxieties, further complicate its adoption
(Bin-Nashwan et al.,2023;Huseynov,2023).
These findings highlight the pivotal role of access and usage frequency, suggesting
that the equitable distribution of AI tools in education is essential to maximize their impact.
At the same time, they advocate for balanced integration strategies that address both
AI’s potential and its sociocognitive risks, ensuring students cultivate resilience alongside
technological proficiency. To optimize ChatGPT’s role in career development, educational
institutions must implement structured guidelines that promote its use as a supplement
rather than a substitute for critical thinking and independent learning.
6.2. RQ2: Differences Across Academic Levels and Fields
The findings that ChatGPT has the most significant positive impact on undergrad-
uate students and those in social sciences align with the existing literature on the role of
technology in education. Undergraduates often rely heavily on technology for general
academic and social activities (Kwon et al.,2013). This group may benefit more from Chat-
GPT’s accessibility and ease of use, which enhance their cognitive skills and career-relevant
knowledge (Essel et al.,2024;Urban et al.,2024). These findings highlight how ChatGPT
bridges gaps in academic support, particularly for younger students who might be less
experienced with complex job market dynamics (Dahri et al.,2024).
However, the lack of statistically significant effects for graduate and doctoral students
suggests that ChatGPT’s capabilities may not align as closely with the advanced, specialized
needs of these groups. Graduate and doctoral students often prioritize targeted academic
technology that supports in-depth research and professional development (Xu & Du,2019).
This gap underscores a limitation in how ChatGPT meets the nuanced demands of higher
academic levels, emphasizing the need for more advanced features tailored to these users.
The findings also reveal distinct patterns across academic fields. The strongest positive
impact on social sciences students may be attributed to advanced technologies’ ability
to facilitate communication-intensive tasks, such as drafting, editing, and synthesizing
information (Campos et al.,2014). In applied sciences, where the effects are moderate
but significant, students likely benefit from these tools’ capabilities in problem-solving
and analytical processes (Moses et al.,2014). However, the lack of significant effects for
arts, humanities, and natural sciences students suggests a mismatch between these fields’
reliance on creativity or empirical research and the functionalities currently offered by such
tools (Trusz,2020).
These results also align with broader discussions on how advanced technologies impact
career confidence. Their positive effects may stem from the ability to enhance self-efficacy
in job-seeking tasks, a critical component of employment confidence (
Liu et al.,2014
). How-
ever, the absence of effects in certain groups highlights the importance of user trust, satisfac-
tion, and field-specific applicability (
Choudhury & Shamszare,2023)
. For instance, students
Behav. Sci. 2025,15, 362 14 of 20
from underrepresented fields may not perceive technological tools as fully addressing their
unique challenges, reflecting disparities in access and relevance of such innovations.
6.3. RQ3: The Mediating Role of User Experience in Enhancing Employment Confidence
The findings that users experience with ChatGPT partially mediate its impact on
employment confidence, highlighting the critical role of interaction quality in leveraging
AI tools for career readiness. This aligns with research emphasizing the importance of
user trust and experience in determining the effectiveness of advanced technologies across
various domains (Choudhury & Shamszare,2023). A positive user experience facilitates
greater self-efficacy, as students are more likely to feel confident in their ability to apply
AI-generated insights in job-seeking tasks and career development.
This mediation effect also reflects broader discussions in the literature on how AI
tools can enhance psychological factors tied to employability. Employment confidence
is strongly influenced by self-efficacy and the perception of one’s skills and readiness to
navigate the job market (Liu et al.,2014). ChatGPT’s ease of use and reliable outputs may
contribute to these psychological drivers, enabling students to feel more equipped to tackle
job-related challenges. Moreover, satisfaction derived from ChatGPT’s responsiveness
and accuracy might translate into greater enthusiasm and optimism about employment
prospects, as evidenced in research on adaptive learning environments and career tools
(Bhullar et al.,2024;Essel et al.,2024).
6.4. Theoretical Explanations and Analytical Hypotheses for Future Research
Our findings indicate that ChatGPT usage is positively associated with students’
employment confidence, which can be understood through the lens of Social Cognitive
Theory (Bandura,1986). This theory suggests that individuals’ beliefs in their ability to
succeed influence their behavior, and ChatGPT may enhance self-efficacy by providing
immediate career advice, resume feedback, and interview preparation tools. Research
has shown that AI-driven platforms, such as ChatGPT, can improve users’ self-efficacy by
facilitating learning, reducing uncertainty, and increasing perceived competence in various
domains (Patil & Pramod,2024).
While our study primarily adopts a descriptive approach, it opens avenues for
hypothesis-driven research. For instance, we hypothesize that the relationship between
ChatGPT use and employment confidence may be mediated by perceived self-efficacy
and moderated by students’ prior career knowledge. Studies have suggested that the
effectiveness of AI tools in enhancing self-efficacy varies depending on users’ familiarity
with career-related decision-making and digital literacy (Bui & Duong,2024). Future re-
search employing experimental or longitudinal designs could validate these relationships,
providing deeper insights into the mechanisms underlying AI-assisted career support.
6.5. Context-Specific Insights and Generalizability
The findings of this study reflect both contextual nuances and broader trends relevant
to AI-driven career development. While ChatGPT’s benefits are widely recognized, their
effects may differ based on regional labor markets and AI adoption levels. For instance, in
developing economies, where job market instability is a key concern, ChatGPT’s potential
to enhance employment confidence may be constrained by limited access to digital career
tools (James,2021). However, studies show that AI can play a transformative role by
improving self-efficacy and job readiness, particularly for students who lack traditional
career support resources (Patil & Pramod,2024). This suggests that while AI’s effectiveness
varies across contexts, its potential to enhance career development is broadly applicable. To
foster international relevance, future research should explore how AI’s role in employment
confidence differs across diverse educational and economic landscapes. Comparative
Behav. Sci. 2025,15, 362 15 of 20
studies on AI adoption in career services across regions with varying labor market dynamics
would provide deeper insights into its global applicability and limitations.
7. Conclusions
Addressing RQ1: How does ChatGPT usage influence college students’ employment
confidence?
This study advances empirical evidence by establishing that ChatGPT usage directly
enhances college students’ employment confidence, particularly through regular and sus-
tained engagement. The causal relationship, rigorously validated via Inverse Probability
Weighting (IPW), demonstrates a significant positive average treatment effect, confirming
that students who actively use ChatGPT exhibit greater confidence in securing employ-
ment than non-users. This finding extends the prior literature on AI in education (e.g.,
Hakiki et al.,2023
) by isolating ChatGPT’s unique role in career preparedness, moving be-
yond generic discussions of AI tools to quantify its specific impact on confidence building.
Importantly, this study identifies frequency of use as a critical moderator, suggesting that
habitual interaction amplifies benefits—a nuance absent in earlier research.
Addressing RQ2: How does ChatGPT usage influence employment confidence across
academic levels and fields?
By conducting a heterogeneity analysis, this work reveals that ChatGPT’s impact is
not uniform but varies significantly across academic levels and disciplines. Undergraduate
students and those in social sciences derive disproportionately higher confidence gains
compared to graduate students or those in technical fields. This finding challenges the
assumption of universal AI tool efficacy and highlights the need for context-specific in-
tegration strategies. For instance, tailored ChatGPT applications in social sciences (e.g.,
simulating job interviews or resume-building exercises) may explain the heightened bene-
fits, whereas technical fields might require more specialized AI adaptations. These insights
refine frameworks for AI adoption in education, emphasizing disciplinary and academic-
level disparities—a dimension underexplored in existing studies (Chen et al.,2023).
Addressing RQ3: What roles do ChatGPT user experiences play in mediating its
influence on employment confidence?
The Structural Equation Modeling (SEM) results provide novel insights into the medi-
ating mechanism, showing that positive ChatGPT experiences (e.g., perceived usefulness,
ease of use, and response quality) partially mediate the relationship between usage and
confidence. This aligns with and extends the user experience theories of Ngo et al. (2024),
demonstrating that confidence gains depend not only on tool adoption but also on sub-
jective satisfaction. For example, students who found ChatGPT intuitive and reliable
reported stronger confidence improvements, underscoring the importance of optimizing
AI interfaces and outputs. This mediation analysis bridges a critical gap in the literature,
shifting the focus from mere usage metrics to qualitative user experiences as drivers of AI’s
educational value.
8. Implication
The findings of this study carry several important implications for educational in-
stitutions, policymakers, and students. Firstly, the significant positive effect of ChatGPT
use on students’ employment confidence underscores the potential of AI tools to enhance
educational experiences and prepare students for the job market. Research indicates that
ChatGPT enhances learning outcomes by providing personalized feedback, simplifying
complex topics, and increasing accessibility to education for underserved populations
(Altarawneh,2023). Additionally, ChatGPT contributes to student satisfaction and engage-
Behav. Sci. 2025,15, 362 16 of 20
ment, as its ease of use and perceived usefulness encourage continued utilization and
support knowledge acquisition (Ngo et al.,2024).
Secondly, this study’s indication that the benefits of ChatGPT use are more pronounced
for undergraduate students and those in the social sciences suggests that AI interventions
may need to be tailored to different educational levels and disciplines. This could lead to
more targeted AI education programs that address the specific needs of various student
populations. AI-based personalized e-learning systems can deliver adaptive and adaptable
content tailored to individual learners’ comprehension and preferences, enhancing learning
outcomes and addressing diverse educational needs (Murtaza et al.,2022).
Furthermore, the findings highlight the importance of addressing potential dispar-
ities in AI literacy, as students from urban areas and those with higher economic status
appear to benefit more from ChatGPT use. The development and application of AI sys-
tems often reveal disparities, especially when access to technology and knowledge is
unequally distributed, making it crucial to bridge these gaps for more equitable AI benefits
(
Schiff et al.,2021
). This also underscores the need for equitable access to AI tools and
resources in education.
Lastly, the partial mediation of ChatGPT experience suggests that hands-on inter-
action with AI can be a valuable component in building employment confidence. Gen-
erative AI tools, like ChatGPT, offer tailored and immersive learning experiences that
simulate professional challenges, fostering skills and confidence necessary for career suc-
cess (
Mittal et al.,2024
). Therefore, educational institutions should focus on providing
opportunities for students to engage meaningfully with AI to develop the necessary skills
and experiences for their future careers.
9. Limitations and Future Directions
This study has several limitations that should be considered. Firstly, the data were
collected through self-reported surveys, which may be subject to biases, such as social
desirability and recall bias. Secondly, the sample may not be fully representative of the
global student population, as it was drawn from a specific set of universities. Thirdly,
while the study controls for various demographic factors, it does not account for all poten-
tial confounding variables that could influence employment confidence, such as cultural
background or personal networks. Lastly, the current study focuses on the relationship
between ChatGPT use and employment confidence without examining the quality of the
interactions with ChatGPT or the specific ways in which students are using the tool.
In terms of future directions, it would be valuable to investigate the long-term impacts
of AI tools, like ChatGPT, on students’ career trajectories and job performance. Additionally,
research could explore the development of tailored AI educational programs that cater to
the diverse needs of students from different disciplines and backgrounds. Furthermore,
the potential ethical implications of AI use in education should be examined to ensure that
these tools are used responsibly and equitably.
Author Contributions: Conceptualization, Y.X. and L.Z.; methodology, Y.X. and L.Z.; software, L.Z.;
validation, Y.X. and L.Z.; formal analysis, Y.X. and L.Z.; investigation, Y.X. and L.Z.; resources, Y.X.;
data curation, L.Z.; writing—original draft preparation, Y.X. and L.Z.; writing—review and editing,
Y.X. and L.Z.; visualization, Y.X. and L.Z.; supervision, Y.X.; project administration, Y.X. All authors
have read and agreed to the published version of the manuscript.
Funding: This work is supported by the National Social Science Fund of China (Grant
No. AIA220013
).
Institutional Review Board Statement: This study was conducted in accordance with the Declaration
of Helsinki and approved by the Faculty of Medicine, University of Oran, Oran, Algeria (protocol
code NBC/2023.01.318 and 24 October 2023).
Behav. Sci. 2025,15, 362 17 of 20
Informed Consent Statement: Informed consent was obtained from all subjects involved in
the study.
Data Availability Statement: The data presented in this study are available on http://doi.org/
10.17632/ymg9nsn6kn.1.
Conflicts of Interest: The authors declare no conflicts of interest.
References
Aghion, P., Antonin, C., Bunel, S., & Jaravel, X. (2021). The direct and indirect effects of automation on employment: A survey of the recent
literature [Unpublished manuscript].
Akiba, D., & Fraboni, M. (2023). AI-supported academic advising: Exploring ChatGPT’s current state and future potential toward
student empowerment. Education Sciences,13(9), 885. [CrossRef]
Al-Abdullatif, A. M., & Alsubaie, M. A. (2024). ChatGPT in learning: Assessing students’ use intentions through the lens of perceived
value and the influence of AI literacy. Behavioral Sciences,14(9), 845. [CrossRef]
Altarawneh, H. (2023). ChatGpt impact on student educational performance: A conceptual analysis. EAI Endorsed Transactions on
e-Learning,9, 1–5. [CrossRef]
Ananthram, S., Bawa, S., Bennett, D., & Gill, C. (2023). Perceived employability and career readiness among STEM students: Does
gender matter? Higher Education Research & Development,43(1), 267–283. [CrossRef]
Aufa, M. F. I., Muslimah, U., & Iswinarti, I. (2024). Factors influencing work readiness in students and college students: A systematic
review. International Journal of Research Publication and Reviews,5(5), 1023–1027. [CrossRef]
Bandura, A. (1986). Social foundations of thought and action. Prentice-Hall, Inc.
Barron, K., & Gravert, C. (2022). Confidence and career choices: An experiment. The Scandinavian Journal of Economics,124(1), 35–68.
[CrossRef]
Bennett, D., Knight, E., & Li, I. (2023). The impact of pre-entry work experience on university students’ perceived employability. Journal
of Further and Higher Education,47, 1140–1154. [CrossRef]
Bhullar, P. S., Joshi, M., & Chugh, R. (2024). ChatGPT in higher education—A synthesis of the literature and a future research agenda.
Education and Information Technologies,29, 21501–21522. [CrossRef]
Bin-Nashwan, S. A., Sadallah, M., & Bouteraa, M. (2023). Use of ChatGPT in academia: Academic integrity hangs in the balance.
Technology in Society,75, 102370. [CrossRef]
Boulton, H. (2015, September 28–October). Supporting transnational students in the transition to doctoral study through online technologies.
International Learning and Teaching Conference, Singapore. Available online: https://irep.ntu.ac.uk/id/eprint/26333/ (accessed
on 11 January 2025).
Brougham, D., & Haar, J. (2020). Technological disruption and employment: The influence on job insecurity and turnover intentions: A
multi-country study. Technological Forecasting and Social Change,161, 120276. [CrossRef]
Bui, H. N., & Duong, C. D. (2024). ChatGPT adoption in entrepreneurship and digital entrepreneurial intention: A moderated
mediation model of technostress and digital entrepreneurial self-efficacy. Equilibrium. Quarterly Journal of Economics and Economic
Policy,19(2), 391–428. [CrossRef]
Campos, S., Aguilar, M., & Batlle, P. F. (2014). Uso académico de redes sociales: Análisis comparativo entre estudiantes de ciencias y de
letras. Historia y Comunicación Social,18(2), 667–678. [CrossRef]
Carlin, B. A., Gelb, B. D., Belinne, J. K., & Ramchand, L. (2018). Bridging the gender gap in confidence. Business Horizons,61(5), 765–774.
[CrossRef]
Cattaneo, M. D. (2010). Efficient semiparametric estimation of multi-valued treatment effects under ignorability. Journal of Econometrics,
155(2), 138–154. [CrossRef]
Chege, S. M., & Wang, D. (2020). Information technology innovation and its impact on job creation by SMEs in developing countries:
An analysis of the literature review. Technology Analysis & Strategic Management,32(3), 256–271. [CrossRef]
Chen, H., Liu, F., & Wen, Y. (2023). The influence of college students’ core self-evaluation on job search outcomes: Chain mediating
effect of career exploration and career adaptability. Current Psychology,42(18), 15696–15707. [CrossRef]
Choudhury, A., & Shamszare, H. (2023). Investigating the impact of user trust on the adoption and use of ChatGPT: Survey analysis.
Journal of Medical Internet Research,25, e47184. [CrossRef]
Cohen, A., Soffer, T., & Henderson, M. (2022). Students’ use of technology and their perceptions of its usefulness in higher education:
International comparison. Journal of Computer Assisted Learning,38(5), 1321–1331. [CrossRef]
Crawford, J., Cowling, M., & Allen, K.-A. (2023). Leadership is needed for ethical ChatGPT: Character, assessment, and learning using
artificial intelligence (AI). Journal of University Teaching and Learning Practice,20(3), 2. [CrossRef]
Creed, P., Muller, J., & Patton, W. (2003). Leaving high school: The influence and consequences for psychological well-being and
career-related confidence. Journal of Adolescence,26(3), 295–311. [CrossRef]
Behav. Sci. 2025,15, 362 18 of 20
Dahri, N. A., Yahaya, N., & Al-Rahmi, W. M. (2024). Exploring the influence of ChatGPT on student academic success and career
readiness. Education and Information Technologies, 1–45. [CrossRef]
Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2024). Does ChatGPT enhance student learning? A systematic review and meta-analysis of
experimental studies. Computers & Education,227, 105224. [CrossRef]
Deshmukh, S., & Bajaj, A. (2024, October 3–5). CareerBoost: A hybrid RAG-NLP job recommendation framework. 2024 8th International
Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (pp. 853–858), Kirtipur, Nepal. [CrossRef]
Emeka, J. (2024). Influence of gender stereotypes in hiring decisions. Journal of Advanced Sociology,5(1), 14–24. [CrossRef]
Essel, H. B., Vlachopoulos, D., Essuman, A. B., & Amankwa, J. O. (2024). ChatGPT effects on cognitive skills of undergraduate students:
Receiving instant responses from AI-based conversational large language models (LLMs). Computers and Education: Artificial
Intelligence,6, 100198. [CrossRef]
Federal Reserve Bank of New York. (2024). The labor market for recent college graduates. Available online: https://www.newyorkfed.org/
research/college-labor-market (accessed on 10 January 2025).
García-Alonso, E. M., León-Mejía, A. C., Sánchez-Cabrero, R., & Guzmán-Ordaz, R. (2024). Training and technology acceptance of
ChatGPT in university students of social sciences: A netcoincidental analysis. Behavioral Sciences,14, 612. [CrossRef]
García-López, I. M., González, C. S. G., Ramírez-Montoya, M. S., & Molina-Espinosa, J. M. (2025). Challenges of implementing ChatGPT
on education: Systematic literature review. International Journal of Educational Research Open,8, 100401. [CrossRef]
Guan, Y., Deng, H., Sun, J., Wang, Y., Cai, Z., Ye, L., Fu, R., Wang, Y., Zhang, S., & Li, Y. (2013). Career adaptability, job search
self-efficacy and outcomes: A three-wave investigation among Chinese university graduates. Journal of Vocational Behavior,83(3),
561–570. [CrossRef]
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis. Prentice Education, Inc.
Hakiki, M., Fadli, R., Samala, A. D., Fricticarani, A., Dayurni, P., Rahmadani, K., Astiti, A. D., & Sabir, A. (2023). Exploring the impact
of using Chat-GPT on student learning outcomes in technology learning: The comprehensive experiment. Advances in Mobile
Learning Educational Research,3(2), 859–872. [CrossRef]
Heung, Y. M. E., & Chiu, T. K. (2025). How ChatGPT impacts student engagement from a systematic review and meta-analysis study.
Computers and Education: Artificial Intelligence,8, 100361. [CrossRef]
Huseynov, S. (2023). ChatGPT and the labor market: Unraveling the effect of AI discussions on students’ earnings expectations. arXiv,
arXiv:2305.11900. [CrossRef]
Inavatin, D., Muarifah, A., & Hidayah, N. (2020). Internal and external predictors of vocational high school students’ employability:
The role and functions of school counselors. PSIKOPEDAGOGIA Jurnal Bimbingan dan Konseling,9(2), 62–72. [CrossRef]
Inside Higher Ed. (2018). Study: College students don’t have confidence they’ll land a job. Available online: https://www.insidehighered
.com/news/2018/01/17/study-college-students-dont-have-confidence-theyll-land-job (accessed on 10 January 2025).
Jain, P., Verma, S., & Yadav, R. (2024). Smart chatbot to assist users of employment websites in job search, skill development, and
networking opportunities. International Journal of Research and Scientific Engineering and Technology,6(2), 79–85. [CrossRef]
James, T. S. (2021). Labour market policies during an age of uncertainty. Policy Studies,43(1), 1–2. [CrossRef]
Jawhar, M., Bitar, Z., Miller, J. R., & Jawhar, S. (2024, May 13–14). AI-powered customized university and career guidance. 2024 Intermountain
Engineering, Technology and Computing (pp. 157–161), Logan, UT, USA. [CrossRef]
Kabudi, T., Pappas, I., & Olsen, D. H. (2021). AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers
and Education: Artificial Intelligence,2, 100017. [CrossRef]
Khairunnisa, D., Widiana, H. S., & Suyono, H. (2022). Self-confidence and psychological well-being on employability of vocational
high school students. Psikostudia: Jurnal Psikologi,11(1), 13–21. [CrossRef]
Kline, R. B. (2005). Principles and practice of structural equation modeling. Guilford Press.
Kocak, Z. (2024). Publication ethics in the era of artificial intelligence. Journal of Korean Medical Science,39(33), e249. [CrossRef]
Kwon, N., Lee, J., Chung, E., Chang, G., & Yoon, H. (2013). Comparative analyses of the uses of information and information
technologies in the everyday life of undergraduate and graduate students majoring in science and engineering. Journal of the
Korean Society for Information Management,30(2), 269–295. [CrossRef]
Li, Y. (2023). The potential application of ChatGPT in higher education management. Lecture Notes in Education Psychology and Public
Media,25, 200–208. [CrossRef]
Liu, S., Wang, M., Liao, H., & Shi, J. (2014). Self-regulation during job search: The opposing effects of employment self-efficacy and job
search behavior self-efficacy. Journal of Applied Psychology,99(6), 1159–1172. [CrossRef]
Lo, C. K., Hew, K. F., & Jong, M. S. Y. (2024). The influence of ChatGPT on student engagement: A systematic review and future
research agenda. Computers & Education,219, 105100. [CrossRef]
Markus, K. A. (2012). Principles and practice of structural equation modeling by Rex B. Kline. Structural Equation Modeling: A
Multidisciplinary Journal,19(3), 509–512. [CrossRef]
Meier, A., Eller, R., & Peters, M. (2025). Creating competitiveness in incumbent small-and medium-sized enterprises: A revised
perspective on digital transformation. Journal of Business Research,186, 115028. [CrossRef]
Behav. Sci. 2025,15, 362 19 of 20
Mittal, U., Sai, S., Chamola, V., & Sangwan, D. (2024). A comprehensive review on generative AI for education. IEEE Access,12,
142733–142751. [CrossRef]
Moses, P., Tey, T., Cheah, P. K., Teo, T., & Wong, S. (2014, November 30–Decmber 4). An online survey: Studying the antecedents of
technology use through the UTAUT model among arts and science undergraduate students. The 22nd International Conference on
Computers in Education, Nara, Japan.
Moynihan, L., Roehling, M., LePine, M., & Boswell, W. (2003). A longitudinal study of the relationships among job search self-efficacy,
job interviews, and employment outcomes. Journal of Business and Psychology,18, 207–233. [CrossRef]
Muñoz, S. A. S., Gayoso, G. G., Huambo, A. C., Tapia, R. D. C., Incaluque, J. L., Aguila, O. E. P., Cajamarca, J. C. R., Acevedo, J. E. R.,
Rivera, H. V. H., & Arias-Gonzáles, J. L. (2023). Examining the impacts of ChatGPT on student motivation and engagement. Social
Space,23(1), 1–27.
Murtaza, M. I., Ahmed, Y., Shamsi, J. A., Sherwani, F., & Usman, M. (2022). AI-based personalized e-learning systems: Issues,
challenges, and solutions. IEEE Access,10, 87087–87106. [CrossRef]
Nam, T. (2019). Technology usage, expected job sustainability, and perceived job insecurity. Technological Forecasting and Social Change,
138, 155–165. [CrossRef]
National Center for Education Statistics. (2022). College student employment. Available online: https://nces.ed.gov/programs/coe/
indicator/ssa/college-student-employment (accessed on 11 January 2025).
Ngo, T. C. T., Tran, T. T., An, G. K., & Nguyen, P. T. (2024). ChatGPT for educational purposes: Investigating the impact of knowledge
management factors on student satisfaction and continuous usage. IEEE Transactions on Learning Technologies,17, 1367–1378.
[CrossRef]
Nikolova, M., Cnossen, F., & Nikolaev, B. (2024). Robots, meaning, and self-determination. Research Policy,53(5), 104987. [CrossRef]
Pack, A., & Maloney, J. (2023). Using generative artificial intelligence for language education research: Insights from using OpenAI’s
ChatGPT. TESOL Quarterly,57(4), 1571–1582. [CrossRef]
Patil, K. P., & Pramod, D. (2024, May 2–3). Conversational artificial intelligence in the workplace: Analysing the impact of ChatGPT on users’
perceived self-efficacy. 2024 2nd International Conference on Advancement in Computation & Computer Technologies, Gharuan,
India. [CrossRef]
Pérez-Núñez, A. (2023). Exploring the potential of generative AI (ChatGPT) for foreign language instruction: Applications and
challenges. Hispania,106, 355–362. [CrossRef]
Qenani, E., Macdougall, N., & Sexton, C. (2014). An empirical study of self-perceived employability: Improving the prospects for
student employment success in an uncertain environment. Active Learning in Higher Education,15(3), 199–213. [CrossRef]
Rajaram, S., Bhat, H., & Shah, P. (2024). Pathfinder-Career guidance using artificial intelligence. International Journal of Advanced Research
in Science, Communication, and Technology,7(4), 102–109. [CrossRef]
Rashid, T., & Asghar, H. M. (2016). Technology use, self-directed learning, student engagement and academic performance: Examining
the interrelations. Computers in Human Behavior,63, 604–612. [CrossRef]
Richie, C. (2022). Environmentally sustainable development and use of artificial intelligence in health care. Bioethics,36, 547–555.
[CrossRef] [PubMed]
Ristiani, R., & Lusianingrum, F. P. W. (2022). The effect of self confidence on job readiness. Journal of Applied Business, Taxation and
Economics Research,1(5), 450–461. [CrossRef]
Royani, I., Sihombing, M. R., Uin Syekh, A., & Padangsidimpuan, A. A. (2024). ChatGPT in the academic writing classroom. Esteem
Journal of English Education Study Programme,7(2), 630–638. [CrossRef]
Samuelson, P. (2023). Generative AI meets copyright. Science,381(6654), 158–161. [CrossRef]
Sapci, O., Elhai, J. D., Amialchuk, A., & Montag, C. (2021). The relationship between smartphone use and studentsacademic
performance. Learning and Individual Differences,89, 102035. [CrossRef]
Schiff, D., Rakova, B., Ayesh, A., Fanti, A., & Lennon, M. (2021). Explaining the principles to practices gap in AI. IEEE Technology and
Society Magazine,40(2), 72–82. [CrossRef]
Shaw, F. S. S. F. S., & Giacquinta, J. B. G. J. B. (2000). A survey of graduate students as a survey of graduate students as end users of
computer technology: End users of computer technology: New roles for faculty new roles for faculty. Information Technology,
Learning, and Performance Journal,18(1), 21.
Sok, S., & Heng, K. (2024). Opportunities, challenges, and strategies for using ChatGPT in higher education: A literature review. Journal
of Digital Educational Technology,4(1), ep2401. [CrossRef]
Sökmen, A., Arici, H. E., & Çali¸skan, G. (2024). Determinants of the usage of ChatGPT in the tourism and hospitality industry: A model
proposal from the technology acceptance perspective. Journal of Tourism & Gastronomy Studies,12(1), 626–644. [CrossRef]
Sreen, A. S., & Majid, M. H. M. (2024). Leveraging ChatGPT for personalized learning: A systematic review in educational settings.
Amandemen: Journal of Learning, Teaching and Educational Studies,2(1), 63–78. [CrossRef]
Suriano, R., Plebe, A., Acciai, A., & Fabio, R. A. (2025). Student interaction with ChatGPT can promote complex critical thinking skills.
Learning and Instruction,95, 102011. [CrossRef]
Behav. Sci. 2025,15, 362 20 of 20
Sweet, R. (2012). Apprenticeship, pathways and career guidance: A cautionary tale. In The Architecture of Innovative Apprenticeship
(pp. 51–68). Springer.
Tang, B., Li, S., & Zhao, C. (2024). Predicting the performance of students using deep ensemble learning. Journal of Intelligence,12, 124.
[CrossRef] [PubMed]
Tanvir, K., Islam, M. S., Sezan, S. B. K., Sanad, Z. A., & Ataur, A.-J. I. (2023). Impact of ChatGPT on academic performance among
Bangladeshi undergraduate students. International Journal of Research in Science & Engineering,35(18), 28–36. [CrossRef]
Thomson, S. R., Pickard-Jones, B. A., Baines, S., & Otermans, P. C. (2024). The impact of AI on education and careers: What do students
think? Frontiers in Artificial Intelligence,7, 1457299. [CrossRef]
Toma, R. B., & Yánez-Pérez, I. (2024). Effects of ChatGPT use on undergraduate students’ creativity: A threat to creative thinking?
Discover Artificial Intelligence,4(1), 74. [CrossRef]
Trusz, S. (2020). Why do females choose to study humanities or social sciences, while males prefer technology or science? Some
intrapersonal and interpersonal predictors. Social Psychology of Education,23(3), 615–639. [CrossRef]
Urban, M., Dˇechtˇerenko, F., Lukavský, J., Hrabalová, V., Svacha, F., Brom, C., & Urban, K. (2024). ChatGPT improves creative
problem-solving performance in university students: An experimental study. Computers & Education,215, 105031. [CrossRef]
Vinchon, F., Gironnay, V., & Lubart, T. (2024). GenAI creativity in narrative tasks: Exploring new forms of creativity. Journal of
Intelligence,12(125), 125. [CrossRef]
Wang, D., Guo, D., Song, C., Hao, L., & Qiao, Z. (2022). General self-efficacy and employability among financially underprivileged
Chinese college students: The mediating role of achievement motivation and career aspirations. Frontiers in Psychology,12, 719771.
[CrossRef]
Wang, S., & Zhang, H. (2024). Enhancing SMEs sustainable innovation and performance through digital transformation: Insights
from strategic technology, organizational dynamics, and environmental adaptation. Socio-Economic Planning Sciences,98, 102124.
[CrossRef]
Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert
Systems with Applications,252, 124167. [CrossRef]
Xu, F., & Du, J. T. (2019). Examining differences and similarities between graduate and undergraduate students’ user satisfaction with
digital libraries. The Journal of Academic Librarianship,45(6), 102072. [CrossRef]
Yam, K. C., Tang, P. M., Jackson, J. C., Su, R., & Gray, K. (2023). The rise of robots increases job insecurity and maladaptive workplace
behaviors: Multimethod evidence. Journal of Applied Psychology,108(5), 850. [CrossRef] [PubMed]
Yu, S.-C., Huang, Y.-M., & Wu, T.-T. (2024). Tool, threat, tutor, talk, and trend: College students’ attitudes toward ChatGPT. Behavioral
Sciences,14, 755. [CrossRef]
Zhang, J., Li, W., & Li, S. (2024). ChatGPT: Analyzing intelligent chatbots based on big language models. Frontiers in Business, Economics
and Management,16(2), 206–209. [CrossRef]
Zhang, R., & Jen, T. H. (2024). Regression analysis of factors affecting college students’ employment motivation: College students’
willingness to receive career guidance. Applied Mathematics and Nonlinear Sciences,9(1). [CrossRef]
Zheng, S., Wu, G., Zhao, J., & Chen, W. (2022). Impact of the COVID-19 epidemic anxiety on college students’ employment confidence
and employment situation perception in China. Frontiers in Psychology,13, 980634. [CrossRef] [PubMed]
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