AI dependence is identified as a maladaptive coping response to AI anxiety, driven by diminished AI self-efficacy, and higher education institutions should move beyond technical training to adopt holistic strategies to mitigate maladaptive dependence.
Abstract
Background The rapid integration of Artificial Intelligence (AI) in higher education offers transformative learning potential but has concurrently triggered psychological challenges, specifically AI anxiety and AI dependence. While these phenomena are increasingly prevalent, the psychological mechanisms converting affective strain into behavioral reliance remain underexplored. Grounded in Cognitive Behavioral Theory and Conservation of Resources Theory, this study investigates the relationship between AI anxiety and AI dependence among undergraduates. Specifically, it examines whether AI self-efficacy mediates this relationship and whether AI literacy acts as a boundary condition moderating these pathways. Methods A cross-sectional survey was conducted with 400 undergraduates recruited via stratified sampling from a normal university in Southwest China. Participants completed validated scales measuring AI anxiety, AI self-efficacy, AI literacy, and AI dependence. Confirmatory factor analysis was performed to verify instrument validity. Hypotheses were tested using the PROCESS macro (Models 4 and 8) to analyze mediation and moderated mediation effects, utilizing bootstrapping techniques with 5,000 resamples to determine the statistical significance of direct and indirect effects. Results The analysis revealed a significant positive association between AI anxiety and AI dependence. AI self-efficacy was found to partially mediate this relationship, indicating that anxiety exacerbates dependence by eroding students’ confidence in their capabilities. Furthermore, AI literacy served as a significant moderator. Results indicated that the negative impact of AI anxiety on AI self-efficacy was stronger for students with higher AI literacy, suggesting that high literacy may sensitize students to competency gaps under emotional strain. AI literacy did not moderate the direct relationship between anxiety and dependence. Conclusion This study identifies AI dependence as a maladaptive coping response to AI anxiety, driven by diminished AI self-efficacy. The findings challenge the assumption that literacy acts solely as a protective buffer, revealing that without emotional regulation, high literacy may intensify the erosion of self- confidence during anxious states. To mitigate maladaptive dependence, higher education institutions should move beyond technical training to adopt holistic strategies. These should include psychological support to build resilience, mastery experiences to foster self-efficacy, and comprehensive curricula that address the emotional and cognitive dimensions of human-AI interaction.
The growing integration of generative artificial intelligence (AI) into university education has raised concerns about the psychological consequences of sustained student reliance on AI tools. While prior research has documented behavioral and cognitive effects of AI overreliance, the psychological mechanism through which AI dependency damages academic motivation remains poorly understood. Drawing on learned helplessness theory (LHT) and self-determination theory (SDT), this study proposes and tests a moderated mediation model in which learned helplessness mediates the relationship between AI dependency and academic intrinsic motivation, with AI literacy serving as a boundary condition that attenuates this pathway. Data were collected from 457 Chinese university students via a cross-sectional survey using validated scales for all four constructs, and the model was tested using covariance-based structural equation modeling (CB-SEM). Results show that AI dependency significantly and positively predicts learned helplessness, which in turn significantly and negatively predicts intrinsic motivation. The indirect effect of AI dependency on intrinsic motivation through learned helplessness was significant, confirming the proposed mediation. AI literacy significantly moderated the AI dependency-learned helplessness pathway, such that the indirect effect was weaker among students with higher AI literacy than among those with lower AI literacy. These findings extend LHT by introducing AI-mediated academic success as a novel, non-failure antecedent of learned helplessness, enrich SDT by specifying the psychological mechanism through which AI dependency becomes need-thwarting, and reposition AI literacy as a psychologically active buffer against motivational harm. The study carries direct implications for university curricula, faculty practice, and institutional policy.
Xiaohui Cui, Tahir Jalal Khan· Frontiers in Psychology· 1 citation
ABSTRACT Objective Generative artificial intelligence is now embedded in university learning, but the psychological pathways linking AI literacy with AI dependence remain unclear. This study examined whether AI self-efficacy and academic procrastination mediate the association between AI literacy and AI dependence among Chinese undergraduates. Method A total of 417 Chinese undergraduate students aged 18–24 years completed measures of AI literacy, AI self-efficacy, academic procrastination and AI dependence. This study tested common method bias, estimated descriptive statistics and Pearson correlations, examined multicollinearity, conducted robustness checks and estimated a serial mediation model with PROCESS Model 6. Results AI literacy was negatively associated with AI dependence (r = –.283). Bootstrapped indirect effects indicated three significant pathways: through AI self-efficacy, through academic procrastination and through the sequential pathway from AI self-efficacy to academic procrastination. The total standardised indirect effect was −.229, accounting for 55.84% of the total effect. Conclusions AI self-efficacy and academic procrastination partially and sequentially mediated the association between AI literacy and AI dependence. The findings suggest that AI literacy may reduce maladaptive AI dependence by strengthening efficacy beliefs and reducing procrastination-related reliance on AI tools. Given the cross-sectional design, longitudinal research is needed to clarify temporal ordering.
Houyu Wu, Haiyan Ni, Jiaqi He et al.· Australian journal of psycho...· 0 citations
Generative artificial intelligence (AI) is increasingly embedded in university students’ writing, information retrieval, knowledge organisation and task completion. Although AI tools may improve learning convenience and access to resources, they may also generate technological uncertainty, pressure to adapt to new competencies and concerns about future development. As an important negative emotional response in intelligent technology contexts, AI anxiety may be closely associated with university students’ academic motivation and psychological adaptation.
This cross-sectional questionnaire study recruited 1,484 university students in China using convenience sampling. Participants completed measures of AI anxiety, emotion regulation and academic motivation through the Wenjuanxing online survey platform. Descriptive statistics, Pearson correlation analysis and regression analyses were used to examine associations among variables. The PROCESS macro was used to test the mediation effect and the moderated mediation effect. All indirect effects were estimated using 5,000 bootstrap samples and 95% confidence intervals.
AI anxiety was negatively associated with both emotion regulation and academic motivation, whereas emotion regulation was positively associated with academic motivation. Bootstrap analyses showed a significant negative indirect association between AI anxiety and academic motivation through emotion regulation. Gender significantly moderated the association between emotion regulation and academic motivation, with a stronger positive association among male students.
AI anxiety may constitute a psychological barrier to students’ motivational adaptation to AI-supported learning. Emotion regulation represents one psychological pathway linking AI anxiety to academic motivation, and gender constitutes a boundary condition for this association. These findings suggest that AI literacy education should integrate emotion-regulation and metacognitive support with differentiated learning assistance. Given the cross-sectional and self-reported nature of the data, the findings require further validation using longitudinal or experimental designs.
Background As artificial intelligence (AI) becomes increasingly integrated into education, fostering student creativity is a critical priority. While prior research suggests a positive link between AI literacy and creative self-efficacy, the underlying psychological mechanisms and key contextual factors remain largely unexplored. This study aimed to address this gap by examining the mediating roles of learning adaptability (cognitive, behavioral, and emotional) and the moderating role of teacher support in this relationship. Methods A total of 509 university engineering students (68.6% male; M age = 19.36 years) participated in a survey. They completed validated measures of AI literacy, learning adaptability, teacher support, and creative self-efficacy. A moderated mediation model was tested using statistical analysis. Results The findings indicated a significant positive association between AI literacy and creative self-efficacy. This relationship was significantly mediated by behavioral and emotional adaptability, but not by cognitive adaptability. Furthermore, teacher support moderated the positive effect of AI literacy on emotional adaptability; this effect was stronger for students who perceived higher levels of teacher support. Conclusion The study advances a nuanced model demonstrating that AI literacy enhances creative confidence primarily by fostering a willingness to act (behavioral adaptability) and bolstering emotional resilience (emotional adaptability). Teacher support is identified as a crucial contextual resource that amplifies this positive process. These findings offer valuable insights for educators and policymakers aiming to design interventions that maximize the creative potential of AI in learning environments.
Lu Pan, Xue Shuang, Yiping Liu· Frontiers in Psychology· 0 citations
Higher AI literacy was associated with lower AI anxiety, and this association was partly accounted for by AI attitudes and AI self-efficacy in the proposed serial mediation model, which suggests that more favorable attitudes may be linked to stronger self-efficacy, which may be related to lower anxiety.
Qin Zeng, Shenghua Zhang, Jiachen Hu et al.· Frontiers in Public Health· 0 citations
The integration of artificial intelligence (AI) into higher education has accelerated, yet little is known about the psychological mechanisms underlying students’ reliance on AI. This study conceptualizes AI dependency as a complex cognitive–motivational construct that extends beyond mere usage, influencing anxiety, digital stress, and quality of life.
A total of 521 participants (predominantly undergraduate, 80% female) were recruited via snowball sampling at King Abdulaziz University. Self-administered standardized instruments assessed AI dependency, AI-related general anxiety, digital stress, and quality of life. A network analysis approach was employed to examine the interrelations among AI dependency, cognitive offloading, anxiety, availability pressure, FoMO, digital overload, digital vigilance, social acceptance anxiety, and quality of life among university students. This approach allowed identification of central variables and conditional interactions within a dynamic psychological system.
AI dependency emerged as a central node in the network, showing strong conditional associations with various digital stressors, including digital vigilance and fear of missing out. Anxiety appeared to occupy a bridging position in the network, linking cognitive reliance to environmental pressures, while these patterns of association were, in turn, related to reduced quality of life. Social acceptance anxiety translated cognitive pressures into relational–identity concerns, and cumulative effects manifested in reduced quality of life. The network revealed non-linear, conditional associations, highlighting that the psychological impact of AI dependency is mediated by cognitive, motivational, and contextual factors rather than by direct usage intensity alone.
AI dependency is not a neutral or purely functional behavior but a central psychological construct with both potential advantages, such as reduced cognitive load and increased efficiency, and risks, including diminished autonomy, heightened anxiety, and long-term digital strain. These findings offer a culturally contextualized model for understanding AI’s influence on student well-being and provide a framework for interventions that target central nodes in the network to promote healthier engagement with AI in academic settings.
Fatma Khalifa Elsayed, Jahz Fahd Al-Mutairi, M. A. Moussa· BMC Psychology· 0 citations
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