The impact of artificial intelligence literacy on learning engagement among university students: the mediating role of psychological capital and the moderating role of professional identity

Synthesis: Wang (2026) tests a moderated mediation model on 1,198 undergraduates from four universities in Zhengzhou, China, showing that AI literacy not only directly predicts learning engagement but also works indirectly by building psychological capital — a resource-transformation chain in which a technological cognitive resource becomes a psychological resource and then a behavioral one. Drawing on Conservation of Resources Theory with Self-Determination Theory and Broaden-and-Build as complements, the study finds that professional commitment does not itself drive engagement but acts as a contextual amplifier, roughly doubling the efficiency with which psychological capital converts into engagement. The finding reframes AI literacy training as necessary but insufficient: its educational payoff depends on simultaneous psychological and identity work.

Key Findings

• AI literacy directly predicts learning engagement. Using partial least squares structural equation modeling, the study finds that students who can understand, apply, and evaluate AI show substantially higher engagement, and the model explains a large share of the variance in engagement. This supports the baseline hypothesis.
• Psychological capital partially mediates the literacy–engagement link. AI literacy builds the psychological resources of self-efficacy, hope, optimism, and resilience, and those resources in turn predict engagement. Roughly half of the literacy effect runs through this psychological pathway, and the direct effect remains significant on top of it — so this is partial, not full, mediation.
• Professional commitment is a pure moderator, not a predictor. Commitment to one's major does not itself raise engagement (its direct effect is negligible and non-significant). Instead, it conditions the strength of the psychological capital → engagement relationship, so its role is an amplifier rather than an independent cause.
• The effect nearly doubles at high commitment. Simple slope analysis shows that psychological capital converts into engagement roughly twice as efficiently among students high in professional commitment as among those low in it — the study's core boundary-condition claim.
• The indirect effect strengthens with commitment. The path from AI literacy through psychological capital to engagement is stronger at high professional commitment, and the index of moderated mediation is significant, confirming the whole "conditional" chain rather than just the final link.
• Demographic differences were substantial. Males scored higher on AI literacy while females scored higher on learning engagement; student leaders reported higher engagement, psychological capital, and commitment; and freshmen and seniors showed relatively higher engagement and commitment than middle-year students.
• Professional commitment is empirically independent of the other constructs. Its correlations with both AI literacy and learning engagement are near zero, which the author reads as evidence that it works as a clean boundary condition rather than a confounded proxy predictor.

Study Design & Method

A cross-sectional questionnaire survey of undergraduate students at four universities in Zhengzhou, Henan Province, conducted February–April 2026. A stratified random sampling design was used, with strata formed from the full combination of gender, grade level, and student leadership status, with proportional allocation by stratum and mixed-mode administration (online via Wenjuanxing and offline paper). After excluding invalid responses — excessive missing data, patterned responding, or unrealistically fast completion — 1,198 valid questionnaires remained. The sample's gender balance (about half female) closely matched national higher-education enrollment figures.

Instruments: AI literacy via a six-dimension scale (perception, comprehension, knowledge, skills, evaluation, innovation; 22 items); professional commitment via a four-dimension scale (ideal, affective, continuance, normative; 27 items); psychological capital via the four-dimension Positive Psychological Capital Questionnaire covering self-efficacy, resilience, hope, and optimism (27 items); and learning engagement via the Utrecht Work Engagement Scale for Students measuring vigor, dedication, and absorption (17 items). Factor structure, reliability, and discriminant validity were all adequately supported, and procedural remedies for common method bias included temporal separation of predictors and outcomes, differential scale anchors across constructs, and anonymity assurances.

Analysis used SPSS for descriptives and correlations and SmartPLS for partial least squares structural equation modeling, chosen for its prediction orientation and its handling of a complex model combining mediation, moderation, a second-order construct, and an interaction term. Mediation and moderated mediation were tested by bootstrap resampling with bias-corrected confidence intervals, with simple slopes plotted at one standard deviation above and below the mean of the moderator.

Implications

• Skills training alone is insufficient. Because roughly half of the AI literacy effect travels through psychological capital, institutions should pair AI literacy curricula with activities that generate success experiences and positive feedback — scaffolded, progressively difficult AI-assisted projects — to build Self-Efficacy, hope, optimism, and resilience.
• Identity work should precede or accompany skill investment. For students with weak professional identity — freshmen, transfer students, or those assigned to a major involuntarily — professional orientation courses, alumni career storytelling, industry–university practice programs, and faculty mentoring that links academic content to career relevance should take priority, since without an identity carrier even high psychological capital fails to convert into sustained behavior.
• Differentiate by student profile. Students high in AI literacy but low in psychological capital benefit most from small-step, achievable group projects; those high in psychological capital but low in professional commitment need identity-focused interventions; those low on both need foundational support on both dimensions at once.
• Monitor and screen. Universities can embed psychological capital and professional commitment measures into academic monitoring systems to flag the two priority groups and target interventions cost-effectively, while producing data to evaluate intervention effects over time.
• Theorists gain a resource-transformation account. Positioning professional commitment as a contextual amplifier rather than a direct predictor reconciles previously inconsistent findings that AI use sometimes enhances and sometimes erodes active learning behaviors: benefits are contingent on the co-presence of psychological resources and professional identity.

Limitations

• The cross-sectional design precludes strict causal inference; reverse or reciprocal paths (e.g. engaged students seeking out AI literacy) cannot be ruled out. Longitudinal designs or pretest–posttest intervention studies are needed.
• The sample is confined to undergraduates at four universities in Zhengzhou, limiting generalizability across regions, disciplines, and educational levels.
• Only psychological capital as a composite construct was tested as a mediator; future work should disaggregate AI self-efficacy, technological resilience, and other specific mediators.
• Rapid iteration of generative AI is shifting use from shallow instrumental application toward deep collaborative co-creation, so the conceptualization and measurement of AI literacy — and assessment tools capable of capturing adaptability and reflection across usage patterns — must evolve accordingly.

Connected Concepts

• AI Literacy — the independent variable, treated as a technological cognitive resource that predicts engagement directly and via psychological capital
• Student Engagement — the outcome, operationalized as vigor, dedication, and absorption on the UWES-S
• Self-Efficacy — one of the four psychological capital dimensions and the theoretical hinge of the cognitive mechanism
• Self-Determination Theory — used to explain how autonomy and competence need satisfaction convert AI literacy into psychological resources
• Motivation — the intrinsic motivational state that need satisfaction activates along the pathway
• Learner Identity — professional commitment as identity-based boundary condition; self-verification and social identity theories account for its amplifying role
• Career Development and Readiness — the study frames professional commitment as a malleable, career-relevant psychological variable that institutions can cultivate
• Higher Education — institutional context for the intervention implications

Connected Articles

• Examining the Impact of Generative AI on Student Motivation and Engagement: The Mediating Role of Autonomy-Support and Autonomous Motivation in Education — SDT-based modeling of autonomy support and autonomous motivation as mediators of GenAI's effect on engagement, testing an adjacent motivational pathway
• Student Motivation and Need Satisfaction in GenAI-Supported Classrooms: A Self-Determination Theory Perspective — decomposes GenAI-supported learning by autonomy, competence, and relatedness satisfaction, the same needs mechanism invoked here
• From disengaged to self-determined: a latent transition analysis of students' AI learning motivation — latent transition analysis linking self-determined motivational profiles to gains in AI literacy, the reverse-direction counterpart to this pathway
• Explaining learning engagement in AI-assisted learning through teacher support and achievement goals: insights from goal-setting theory — SEM study of learning engagement in AI-assisted learning with a different motivational lens (achievement goal orientations)
• Understanding Student Dependency on AI: The Role of AI Literacy, Academic Self-Efficacy, and Resource Management Strategies — shows AI literacy alone does not protect against overreliance and that self-efficacy matters at least as much, echoing the "literacy is not enough" argument
• AI literacy alone is not enough: Student AI readiness and career adaptability in business and management education — finds AI readiness and AI self-efficacy, not literacy alone, link AI competence to career adapt-abilities, a parallel mediation account
• When the algorithm enters the classroom: A critical integrative review of large language models, nursing education — examines how AI integration reshapes professional identity formation, the construct this study treats as a moderator

Citation

Wang, N. (2026). The impact of artificial intelligence literacy on learning engagement among university students: the mediating role of psychological capital and the moderating role of professional identity. Frontiers in Psychology, 17, 1892204.