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AI Literacy and Undergraduate Self-Perceived Employability: Roles of AI Competency, Career Decision-Making Self-Efficacy, and Growth Mindset

Drawing on Social Cognitive Career Theory, this study of 381 Chinese undergraduates reveals that AI literacy enhances self-perceived employability by boosting AI competency and career decision-making self-efficacy, with these positive relationships further strengthened by a growth mindset.

Original authors: Hazem Ali, Min Li

Published 2026-07-27

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Hazem Ali, Min Li

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are standing at the edge of a vast, bustling ocean called the "Job Market." In the past, the boats people used to cross this ocean were made of wood and powered by oars. But today, the ocean is changing. Giant, high-tech ships powered by Artificial Intelligence (AI) are zooming past, and the rules of sailing are being rewritten. For students graduating from college, the big question is: "Am I ready to sail these new waters, or will I be left behind?"

To answer this, researchers look at a few key ingredients. First, there is AI Literacy, which is like having a map and a compass that explain how the new ships work. It's knowing what AI is and how to talk to it. Second, there is AI Competency, which is the actual skill of steering the ship, fixing the engine, and navigating the waves using that technology. Third, there is Career Decision-Making Self-Efficacy. Think of this as your inner captain's confidence. It's the belief that you can make the right choices about where to steer your career, even when the fog rolls in. Finally, there is Growth Mindset. This is the belief that your brain is like a muscle: if you work hard and practice, you can get stronger and smarter. You don't have to be a genius today to be a great captain tomorrow; you just have to be willing to learn.

Why does this matter? Because the job market is shifting so fast that knowing how to use a computer isn't enough anymore. Students need to know how to work with AI to feel confident about getting a job. This study dives into how these pieces fit together to help students feel ready for their future.

The Study: Mapping the Path to a Confident Future

In this research, two scholars, Hazem Ali and Min Li, decided to investigate how these different ingredients mix together to create a sense of Self-Perceived Employability (SPE). SPE is simply how confident a student feels about their ability to get and keep a job. They gathered data from 381 undergraduate students in China to see if knowing about AI, being good at using it, and having a "growth mindset" actually helps students feel more employable.

The researchers built a model based on a theory called Social Cognitive Career Theory, which suggests that what we learn and how we think about our abilities shape our career choices. They wanted to see if AI Literacy acts as a foundation that builds up a student's skills and confidence.

What They Found

The study suggests a clear chain reaction. First, they found that AI Literacy is positively associated with AI Competency. In other words, students who understand the basics of AI are better at actually using it to solve problems. It's like knowing the theory of how a bicycle works helps you learn to ride it faster.

Second, the study found that AI Literacy also boosts Career Decision-Making Self-Efficacy. Students who understand AI feel more confident in their ability to make career choices. They aren't as scared of the future because they feel they have the tools to navigate it.

The results also showed that both AI Competency and Career Decision-Making Self-Efficacy are significant predictors of Self-Perceived Employability. This means that students who are good at using AI tools and feel confident making career decisions are the ones who feel most ready to enter the workforce. Interestingly, the study also found that having high AI Competency actually helps boost a student's confidence in making career decisions, creating a helpful cycle where skills and confidence feed into each other.

The Secret Booster: Growth Mindset

Here is where the story gets really interesting. The researchers tested whether Growth Mindset acts like a "super-charger" for these relationships. They found that it does.

The study suggests that the positive effects of AI Literacy on both AI Competency and Career Decision-Making Self-Efficacy are stronger for students who have a high Growth Mindset. Imagine two students with the same level of AI knowledge. The student who believes they can grow and improve (high Growth Mindset) will likely turn that knowledge into real skills and confidence much faster than the student who thinks their abilities are fixed and unchangeable. The data showed that Growth Mindset significantly strengthens the link between knowing about AI and being able to use it, as well as the link between knowing about AI and feeling confident about career choices.

What This Means (and What It Doesn't)

The authors are careful to note that because they asked students questions at just one point in time (a cross-sectional survey), they can't say for sure that one thing causes the other. They suggest these relationships exist, but they don't claim to have proven a cause-and-effect chain like a lab experiment would.

However, the findings offer a hopeful picture. It suggests that to help students feel ready for the AI-driven future, universities shouldn't just teach technical skills. They also need to foster a "Growth Mindset"—the belief that effort leads to improvement. By combining technical AI training with a psychological environment that encourages learning and resilience, educators can help students feel more confident and employable.

The study concludes that in a world where AI is everywhere, feeling ready for a job isn't just about having a degree; it's about having the right tools (AI Literacy), the right skills (AI Competency), the right confidence (Self-Efficacy), and the right attitude (Growth Mindset) to navigate the changing tides.

Technical Summary: AI Literacy and Undergraduate Self-Perceived Employability

Problem Statement

The rapid integration of Artificial Intelligence (AI) into contemporary labor markets has intensified the necessity for graduates to possess AI-related capabilities. While existing literature acknowledges the link between AI literacy (AIL) and student outcomes, there is a notable gap in empirical research examining how students' perceptions of AI influence their educational decisions and career aspirations. Specifically, the mechanisms through which foundational AI literacy translates into practical competency, career decision-making confidence, and ultimately, self-perceived employability (SPE) remain under-explored. Furthermore, the role of psychological resources, such as growth mindset (GM), in moderating these relationships within the context of AI-driven career development has not been sufficiently investigated.

Methodology

This study employed a quantitative, cross-sectional research design utilizing a structured questionnaire administered to undergraduate students in China.

Sample: Data were collected from 381 full-time undergraduate students across seven universities in Zhejiang Province. The sample was diverse regarding gender (54.39% female), academic major (ranging from Business Administration to Arts and Law), and year of study (primarily Year 3 and 4).
Instrumentation: The study utilized established scales adapted from prior literature to measure five key constructs on a five-point Likert scale:
- AI Literacy (AIL): Foundational knowledge and procedural understanding of AI tools.
- AI Competency (AIC): The ability to effectively apply and manage AI in academic and work contexts.
- Career Decision-Making Self-Efficacy (CDMSE): Confidence in making and committing to career decisions.
- Growth Mindset (GM): Beliefs regarding the malleability of intelligence and skills through effort.
- Self-Perceived Employability (SPE): Students' evaluation of their capacity to obtain and retain employment.
Analytical Approach: Data analysis was conducted in three phases using SPSS 23.0 and AMOS 23.0:
1. Measurement Model Assessment: Confirmatory Factor Analysis (CFA) was used to evaluate reliability (Cronbach's alpha, Composite Reliability) and validity (Convergent and Discriminant validity via AVE and Fornell-Larcker criteria).
2. Bias Assessment: Common Method Bias (CMB) was mitigated through procedural controls (randomization, anonymity) and statistical tests (Harman's single-factor test, CFA marker technique).
3. Structural Modeling: Structural Equation Modeling (SEM) was applied to test the hypothesized direct relationships and the moderating effects of GM.

Key Results

The structural equation modeling analysis yielded the following significant findings:

Direct Relationships:
- AIL $\rightarrow$ AIC & CDMSE: AI literacy is positively and significantly associated with both AI competency ( $B=0.285, p<0.001$ ) and career decision-making self-efficacy ( $B=0.372, p<0.001$ ).
- AIC $\rightarrow$ SPE & CDMSE: AI competency significantly predicts self-perceived employability ( $B=0.108, p<0.05$ ) and is positively associated with CDMSE ( $B=0.261, p<0.001$ ).
- CDMSE $\rightarrow$ SPE: Career decision-making self-efficacy is a significant positive predictor of self-perceived employability ( $B=0.159, p<0.001$ ).
Moderating Effects of Growth Mindset (GM):
- GM significantly strengthens the positive relationship between AI literacy and AI competency (Interaction effect: $B=0.155, p<0.001$ ).
- GM significantly strengthens the positive relationship between AI literacy and career decision-making self-efficacy (Interaction effect: $B=0.171, p<0.001$ ).
- This indicates that students with a stronger growth mindset are better able to convert AI literacy into practical skills and career confidence.
Model Fit: The proposed five-factor model demonstrated a good fit to the data ($CFI=0.941, TLI=0.942, RMSEA=0.068$), outperforming alternative one-, two-, three-, and four-factor models.

Key Contributions

The study makes three primary contributions to the literature on career development and employability:

Conceptual Advancement: It empirically conceptualizes AI literacy not merely as technical knowledge but as a foundational resource that shapes both practical AI competency and career decision-making self-efficacy within AI-driven labor markets.
Theoretical Integration: Grounded in Social Cognitive Career Theory (SCCT), the study clarifies the interrelationships among AIL, AIC, CDMSE, and SPE, demonstrating how technological learning experiences interact with self-efficacy beliefs to influence career outcomes.
Identification of Boundary Conditions: The study extends prior research by identifying Growth Mindset as a critical moderating variable. It demonstrates that psychological resources (GM) amplify the impact of AI literacy on key career-related capabilities, highlighting the importance of adaptive learning beliefs in technology-intensive contexts.

Significance and Claims

The authors claim that this study offers a more integrated explanation of undergraduate self-perceived employability in the AI era. By moving beyond models that focus solely on skills or attitudes, the research demonstrates that effective SPE development requires the convergence of technological resources (AIL and AIC) and psychological resources (CDMSE and GM).

The findings suggest that in rapidly evolving labor markets, students' confidence in their employability is bolstered not just by knowing how to use AI tools, but by possessing the psychological adaptability to leverage that knowledge for career planning. The study concludes that while AI literacy is foundational, its full potential to enhance employability is maximized when students possess a growth mindset that encourages persistence and continuous skill development.

Limitations Noted by Authors:
The authors explicitly acknowledge that the cross-sectional design precludes causal inference. They also note the reliance on self-reported data, which may introduce common method bias, and the limited generalizability of the sample to Chinese undergraduate students. Consequently, the authors call for future longitudinal, experimental, and cross-cultural studies to further validate these relationships.