AI-Dependency and Its Relationships with Anxiety, Quality of Life, and Digital Stress

AI-Dependency and Its Relationships with Anxiety, Quality of Life, and Digital Stress | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article AI-Dependency and Its Relationships with Anxiety, Quality of Life, and Digital Stress Fatma Khalifa Elsayed, Jahz Fahd Al-Mutairi, Mahmoud Ali Moussa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8870746/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background 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. Methods A 521 participants (predominantly undergraduate, 80% female) recruited via snowball sampling at King Abdulaziz University. Self-administered standardized instruments assessed AI dependency, AI-related general anxiety, digital stress, quality of life, and AI dependency. 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. Results AI dependency emerged as a structurally foundational variable, reorganizing students’ cognitive and emotional experiences. It was closely linked to digital stressors, including digital vigilance and fear of missing out, while anxiety functioned as a mediator connecting cognitive reliance to environmental pressures. 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. Conclusions 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. Artificial intelligence AI dependency cognitive offloading anxiety digital stress quality of life university students network analysis Figures Figure 1 Figure 2 Figure 3 Introduction In recent years, the integration of artificial intelligence (AI) technologies into everyday life has accelerated at an unprecedented pace. AI applications have evolved from peripheral technical aids into core infrastructures shaping education, work practices, healthcare delivery, and decision-making processes. What was once confined to specialized research domains has now become a pervasive and influential force across personal and professional contexts [ 1 , 2 ]. This rapid diffusion signals not merely technological advancement, but a profound transformation in how cognitive labor, judgment, and problem-solving are distributed between humans and intelligent systems. Emerging literature suggests that reliance on AI systems increasingly extends beyond instrumental task support toward a form of psychological and behavioral dependency. In this mode of interaction, individuals tend to delegate core cognitive functions—such as reasoning, evaluation, and decision-making—to algorithmic outputs, often with minimal critical scrutiny [ 3 ]. Such reliance reflects a qualitative shift in human–technology interaction, wherein AI systems are no longer perceived solely as tools, but as epistemic authorities that shape users’ confidence, agency, and cognitive engagement. At the global level, AI adoption has expanded at a historically unparalleled rate. According to the AI Index Report [ 4 ], AI technologies represent one of the fastest-spreading innovations in modern history, surpassing even the early diffusion trajectories of electricity and the internet [ 4 ]. This rapid proliferation underscores the urgency of examining not only the functional benefits of AI but also its psychological, cognitive, and social implications for users embedded in AI-mediated environments. Within the Saudi Arabian context, recent official statistics indicate that approximately 21.5% of internet users employ AI-based tools, reflecting an early yet rapidly evolving stage of individual-level adoption [ 5 ]. Complementing these figures, a national survey revealed that nearly 49% of Saudi citizens report general use of AI applications, with ChatGPT emerging as the most frequently used tool (41%), signaling growing technological awareness and societal engagement with generative AI systems [ 6 ]. These trends highlight the relevance of investigating AI-related psychological phenomena within local and culturally specific contexts. The university environment has witnessed a marked expansion in the use of generative AI tools. Students increasingly rely on these systems for completing academic assignments, generating ideas, summarizing scientific texts, and supporting self-directed learning. When employed in a balanced and reflective manner, such usage has been associated with enhanced academic efficiency, reduced cognitive load, and accelerated access to information [ 7 , 8 ]. Motivations underlying this high adoption rate include the desire to facilitate learning, improve academic productivity, optimize educational efficiency, and accelerate knowledge acquisition through immediate informational support [ 9 ]. However, contemporary scholarship increasingly conceptualizes AI dependency as more than a function of usage intensity. Rather, it is understood as a psychologically embedded behavioral pattern encompassing cognitive, emotional, and motivational dimensions that shape decision-making processes. Recent studies characterize AI dependency as a distinct form of human–technology interaction in which trust, perception, and psychological needs converge, leading individuals to outsource analytical and evaluative thinking to intelligent systems rather than relying on their own cognitive capacities [ 10 ]. Empirical evidence further indicates that high levels of AI dependency are associated with diminished critical thinking abilities, driven by cognitive fatigue and a shift toward externalized problem-solving strategies [ 11 ]. These findings suggest that AI dependency possesses a clear cognitive dimension, alongside behavioral and emotional consequences linked to users’ self-perceptions and trust in technology. Despite its potential benefits, intensive and unregulated use of AI tools—particularly in academic and daily task contexts—introduces significant psychological and cognitive challenges [ 12 ]. Research in human–AI interaction distinguishes between adaptive use, which augments human cognition and supports informed decision-making, and maladaptive or unbalanced dependency, wherein AI systems supplant independent critical reasoning and erode users’ cognitive autonomy [ 13 ]. In the latter case, users may accept AI outputs uncritically, resulting in weakened analytical and evaluative skills. Some scholars further conceptualize excessive AI dependency as a form of behavioral addiction, characterized by the substitution of algorithmic processes for complex cognitive functions in a manner that undermines intellectual autonomy. Such dependency has been linked to impaired self-regulation among students, reduced academic life satisfaction [ 14 ], heightened fear of missing out [ 15 ], impulsive decision-making aimed at avoiding perceived informational loss [ 16 ], attentional fragmentation, and diminished concentration during academic tasks [ 17 , 18 ]. Additional consequences include elevated academic anxiety, increased cognitive load, and erosion of higher-order cognitive skills such as critical thinking and problem-solving [ 19 , 20 ]. Moreover, the personalized and interactive nature of generative AI systems increases the likelihood that users develop feelings of attachment or perceived intimacy, potentially overestimating the depth and reciprocity of their relationship with AI. Such dynamics may foster emotional or social dependency on generative AI systems [ 21 ], further complicating the psychological landscape of AI use. Growing reliance on AI systems has also been associated with heightened levels of anxiety and psychological uncertainty. This relationship may stem from diminished confidence in one’s own cognitive abilities when performing tasks without technological assistance, fear of failure in the absence of AI support, and the persistent cognitive strain associated with interacting with complex AI systems. Frenkenberg and Hochman [ 3 ] argue that AI-related anxiety can paradoxically reinforce excessive dependency by functioning as an emotional coping mechanism, indicating that anxiety is not merely a transient response but a critical psychological indicator shaping technology use patterns. Supporting this view, recent findings demonstrate that anxiety related to learning and using AI correlates with lower self-efficacy and increased cognitive-emotional stress, thereby justifying the inclusion of anxiety as a central psychological variable in studies of technological dependency [ 22 ]. Beyond anxiety, evidence suggests that AI-related stress mediates the relationship between technological dependency and diminished quality of life. Recent analyses indicate that technostressors associated with AI—such as perceived technological insecurity, system complexity, and expectations of constant availability—intensify negative emotional states, which in turn undermine key indicators of quality of life, including life satisfaction, psychological well-being, and work–life balance [ 23 ]. Quality of life is widely conceptualized as a multidimensional construct encompassing psychological, social, and functional dimensions of individual and collective well-being. While some studies highlight the potential of AI to enhance quality of life by improving efficiency, supporting healthcare, and facilitating access to digital services [ 24 ], others report adverse effects linked to increased insecurity, workload intensification, and reduced autonomy [ 25 ]. This inconsistency underscores the non-deterministic nature of AI’s impact on quality of life, suggesting that outcomes depend heavily on design features, usage patterns, and psychosocial contexts. Additionally, sustained interaction with AI-enhanced digital environments has given rise to novel forms of psychological strain, most notably digital stress. Digital stress is conceptualized as a state of psychological pressure resulting from continuous exposure to digital stimuli, information overload, perpetual connectivity, and fear of missing out [ 26 ]. Among university students, such stress has been linked to reduced academic performance, mental exhaustion, and diminished digital well-being, particularly in the absence of regulatory frameworks that promote balanced and health-oriented technology use [ 27 ]. University students are considered especially vulnerable to digital stress due to their intensive reliance on digital technologies for learning, communication, and academic achievement, particularly amid the widespread adoption of digital and hybrid education models [ 28 ]. Despite the growing body of research on AI applications and their psychological implications, the literature reveals a notable gap in conceptualizing AI dependency as an independent psychological construct distinct from mere usage frequency. Moreover, there is a lack of integrative explanatory models that simultaneously examine AI dependency, anxiety, quality of life, and digital stress within a unified framework. Compounding this limitation, most existing studies have been conducted in Western contexts, with limited empirical investigation in Arab societies or among university populations. This gap restricts the generalizability of current findings and underscores the necessity of the present study. Theoretical framework The present study is grounded in a tightly integrated theoretical model that conceptualizes AI dependency as a maladaptive cognitive–motivational state emerging from repeated cognitive offloading and functioning as a proximal determinant of anxiety, digital stress, and quality of life. Drawing on Cognitive Offloading Theory, AI dependency is understood as a progressive shift in the locus of cognitive control, wherein individuals increasingly substitute internally generated reasoning, judgment, and problem-solving processes with algorithmic outputs [ 29 , 30 ]. This substitution does not merely reduce cognitive effort; rather, it restructures metacognitive monitoring by diminishing users’ active engagement in evaluation and verification processes [ 31 ]. Over time, this pattern leads to a reduced perception of personal cognitive efficacy and an internalized belief that effective task performance is contingent upon AI availability, thereby establishing dependency as a stable psychological orientation rather than a situational behavior [ 32 , 11 ]. Building on this cognitive foundation, Self-Determination Theory (SDT) provides a precise motivational explanation for the emergence of anxiety within AI-dependent individuals. According to SDT, psychological well-being is contingent upon the satisfaction of the basic needs for autonomy and competence [ 33 , 34 ]. AI dependency systematically frustrates these needs by reallocating agency from the individual to the technological system. As autonomy is compromised, individuals experience reduced perceived control over task execution; as competence is undermined, they exhibit declining confidence in their unaided cognitive abilities. This dual need frustration generates anxiety through anticipatory threat appraisal—specifically, fear of underperformance, cognitive inadequacy, or failure in contexts where AI assistance is limited or unavailable [ 35 , 3 ]. Within this framework, anxiety is not a byproduct of AI complexity, but a direct motivational consequence of dependency-driven erosion of self-regulatory capacity and internal mastery [ 22 ]. The Transactional Model of Stress and Coping further specifies how AI dependency and anxiety interact to produce sustained digital stress. According to this model, stress arises when individuals appraise environmental demands as exceeding their perceived coping resources [ 36 ]. AI-dependent individuals, whose self-efficacy has been weakened through chronic cognitive delegation, are more likely to appraise digital and academic demands as threatening rather than manageable [ 37 ]. Anxiety amplifies this appraisal bias by heightening vigilance to potential failure and increasing sensitivity to performance-related cues. Consequently, dependent users experience digital environments as cognitively intrusive and psychologically taxing, characterized by information overload, pressure for constant responsiveness, and fear of technological inadequacy—core components of digital stress and technostress in AI-mediated contexts [ 38 , 39 , 23 ]. Within this integrated framework, quality of life is conceptualized as a distal outcome shaped by the prolonged activation of these cognitive, motivational, and stress-related processes. Persistent anxiety and digital stress compromise psychological well-being by depleting emotional resources, impairing concentration, and reducing satisfaction with academic and personal functioning [ 40 , 41 ]. Moreover, AI dependency indirectly undermines quality of life by eroding perceived autonomy and competence, which are core determinants of well-being across psychological and educational domains [ 42 , 43 ]. While AI systems may enhance efficiency in the short term, dependency-driven reliance transforms these gains into long-term psychological costs, manifesting as reduced life satisfaction, diminished sense of control, and impaired balance across life domains. Critically, this model positions AI dependency as the central organizing construct that initiates and sustains the relationships among anxiety, digital stress, and quality of life. Rather than treating these variables as parallel consequences of technology use, the framework specifies a sequential pathway in which cognitive offloading fosters dependency, dependency frustrates motivational needs, anxiety intensifies maladaptive stress appraisals, and sustained digital stress ultimately degrades quality of life. By integrating cognitive, motivational, and stress theories at a mechanistic level, this study advances a psychologically precise explanation of how reliance on intelligent systems reshapes human functioning in AI-saturated environments. Problem statement Although artificial intelligence has become deeply embedded in students’ academic and daily functioning, its expanding integration has introduced a qualitatively new psychological phenomenon, AI dependency , whose implications for anxiety, quality of life, and digital stress remain insufficiently defined and empirically tested [ 3 , 21 ]. Existing research largely conceptualizes AI engagement as a neutral, instrumental, or performance-enhancing form of tool use, emphasizing efficiency, academic productivity, and learning facilitation [ 8 , 9 ], while overlooking how sustained reliance on AI for cognitive, evaluative, and decision-making tasks may foster dependency that undermines psychological autonomy, perceived competence, and cognitive self-efficacy [ 10 , 13 , 19 ]. Consequently, the psychological mechanisms through which AI dependency contributes to heightened anxiety, increased digital stress, and diminished quality of life remain theoretically fragmented and insufficiently articulated within a unified explanatory framework [ 23 , 39 ]. This limitation is particularly salient among university students, who constitute a high-risk population due to their intensive exposure to AI-mediated learning environments, persistent academic performance pressures, and continuous digital connectivity [ 27 ]. Although prior studies have examined anxiety related to AI use [ 22 ], technostress in digital and AI-supported contexts [ 38 , 23 ], and quality of life outcomes associated with technology use [ 24 , 25 ], these constructs are typically investigated in isolation rather than as interdependent outcomes arising from a shared dependency-driven process. As a result, the potential mediating and sequential relationships among AI dependency, anxiety, digital stress, and quality of life remain largely unexplored [ 20 , 3 ]. Moreover, the absence of integrative psychological models is compounded by a pronounced lack of empirical evidence from non-Western and Arab cultural contexts, despite the rapid expansion of AI adoption in Saudi Arabia and similar societies [ 5 , 6 ]. This geographic and cultural imbalance limits the generalizability of existing findings and obscures culturally specific dynamics shaping AI dependency and its psychological consequences. Accordingly, there is a critical need to systematically conceptualize AI dependency as a central psychological construct and to empirically examine its relationships with anxiety, digital stress, and quality of life within a unified theoretical framework. Addressing this problem is essential for advancing theory in human–AI interaction and for informing culturally responsive educational and mental health interventions in AI-intensive academic environments. Methodology Design This study employed a quantitative, non-experimental design to explore the relationships between AI dependency, anxiety, digital stress, and quality of life among university students. The design was suited to analyzing naturally occurring psychological variations related to AI use in academic settings. Data were collected via self-administered standardized measures during a single assessment period, allowing for the evaluation of participants' perceived AI dependency and related psychological outcomes. This method minimized participant burden while maintaining ecological validity and enabled the examination of relational patterns among variables. The analysis followed a theoretically driven model, with AI dependency as the primary predictor, anxiety and digital stress as intervening factors, and quality of life as the outcome. Advanced statistical techniques were used to assess both direct and indirect associations among variables. Although the non-experimental design limits causal inferences, it offers a robust empirical framework for testing theoretical relationships and contributes to the literature on the psychological impacts of AI reliance in higher education. Participants The study included 521 participants recruited through snowball sampling, ensuring no participant was enrolled in classes taught by the researchers at King Abdulaziz University. The majority were undergraduate students (n = 466, 89.4%), with smaller proportions of postgraduate students (n = 10, 1.9%), faculty or lecturers (n = 18, 3.5%), and employees or technicians (n = 27, 5.2%). Participants’ ages ranged from 18 to 58 years (M = 21.92, SD = 3.75), and the sample was predominantly female (n = 418, 80.2%), with males comprising 19.8% (n = 103). Academic specializations were diverse and are not reported due to the high number of categories. This sampling approach ensured demographic variability while minimizing potential bias related to the researchers’ instructional influence. Instruments AI-Dependency Scale To assess the extent to which individuals rely on digital tools and AI-driven resources in their daily tasks and decision-making processes. The scale captures the degree of comfort and habitual usage of digital aids, ranging from the initial stages of task planning to active participation in collaborative and social interactions. It evaluates tendencies to consult digital resources before initiating work, to enhance efficiency, or to organize personal ideas and tasks. Furthermore, the instrument examines the degree to which individuals depend on AI-generated or digital content in both independent and group contexts, including reliance for decision-making, discussion contributions, and content modification or adaptation. The scale also addresses the subtle influence of digital tools on judgment, exploring how users may unconsciously allow digital sources to guide decisions or shape their personal contributions. It considers perceptions of social acceptance, reflecting how individuals perceive others’ recognition of their work based on AI-supported information. By encompassing these behaviors, the AI-Dependency Scale provides a comprehensive evaluation of the psychological and functional reliance on digital technologies, highlighting patterns of habitual integration, dependency, and potential automatization of decisions in the digital era. Participants respond using a five-point Likert scale, ranging from “Strongly Disagree” to “Strongly Agree,” enabling nuanced measurement of the intensity and frequency of reliance on digital and AI-assisted tools. This format allows researchers to quantify the degree of dependency, supporting analyses that link AI usage patterns with cognitive, social, and behavioral outcomes. Exploratory factor analysis (EFA) of the 15-item AI dependency scale indicated excellent data adequacy for factor analysis, with a Kaiser-Meyer-Olkin index of 0.94 and a significant Bartlett’s test of sphericity (χ² = 4784.68, df = 105, p < 0.001). Parallel analysis and scree plot examination supported a three-factor solution, accounting for 51.9% of the total variance. Factor loadings were predominantly above 0.6, delineating three dimensions: Factor 1 reflecting direct interaction reliance (Q2, Q3, Q4, Q5, Q12), Factor 2 representing practical or applied dependency (Q6–Q10), and Factor 3 capturing cognitive and affective reliance (Q1, Q11, Q13–Q15). The second-order confirmatory factor analysis (CFA) showed that these first-order factors were adequately represented under a higher-order general factor (g), with strong standardized loadings (F1 = 0.819, F2 = 0.993, F3 = 0.793) and acceptable fit indices (CFI = 0.922, TLI = 0.906, RMSEA = 0.090, SRMR = 0.080), substantially outperforming a single-factor model (RMSEA = 0.145). R² values indicated that most items were well explained by their respective factors, though some items in Factor 3 exhibited lower explained variance, suggesting potential refinement. Reliability analysis yielded high internal consistency, with Omega Total = 0.95, Omega Hierarchical = 0.80, and subscale Omegas ranging from 0.75 to 0.92, with the general factor accounting for 70% of the common variance. AI-Related General Anxiety Scale The AI-Related General Anxiety Scale is a psychometric tool developed to assess the general level of anxiety individuals experience in relation to the use and interaction with artificial intelligence (AI) technologies. This instrument evaluates multiple dimensions of psychological response, including concerns about the potential impact of AI on professional and social life, difficulties in concentration, mental fatigue associated with AI use, as well as emotional reactions such as tension, irritability, and persistent worry about possible outcomes. Additionally, the scale examines the influence of these concerns on sleep patterns and overall psychological well-being, providing a comprehensive understanding of the interplay between AI engagement and general anxiety. Responses are measured using a five-point Likert scale, ranging from Strongly Disagree to Strongly Agree,” allowing for nuanced assessment of the intensity and frequency of anxiety-related experiences. This scaling method enables researchers to quantify subjective perceptions and to identify individuals who may be more susceptible to anxiety in the context of emerging AI technologies. The tool is particularly suitable for research exploring the psychological impacts of rapid digital transformation and technological integration, offering insights into both individual and societal responses to AI adoption. A first-order general factor model was treated using the robust weighted least squares estimator (WLSMV) appropriate for Likert-type data. Conceptually justified correlated residuals were freely estimated between closely related items (Q1–Q2, Q2–Q3, Q4–Q5, Q6–Q7) to account for shared item-specific variance without altering the unidimensional structure. The resulting model demonstrated excellent incremental fit, with a Comparative Fit Index (CFI = 0.995) and Tucker-Lewis Index (TLI = 0.989), while the Standardized Root Mean Square Residual (SRMR = 0.049) indicated minimal residual discrepancy; the RMSEA was elevated (0.108), but this was interpreted cautiously given its sensitivity to model complexity and ordinal indicators. All factor loadings on the general anxiety factor were statistically significant and of moderate to high magnitude, ranging from 0.659 (Q1) to 0.854 (Q3), indicating that each item contributed substantially to the latent construct. Examination of the R-squared values revealed that the general factor explained 43–73% of the variance across items, with the highest variance accounted for in items reflecting concentration difficulties and persistent worry. Reliability analyses using McDonald’s omega indicated strong internal consistency for the total scale (ω = 0.89), supporting the unidimensionality and stability of the measure. Quality of Life scale A self-developed measure of quality of life was constructed. At the same time, the instrument is original to the researcher, the formulation of several items was guided by established theoretical models and widely used instruments, including the World Health Organization Quality of Life Brief version (WHOQOL-BREF), the General Health Questionnaire (GHQ), and prior research on psychological well-being, life satisfaction, perceived stress, and self-efficacy [ 44 , 45 ]. The scale comprises 12 items designed to capture a spectrum of psychological and emotional dimensions, integrating both negative indicators reflecting lower quality of life, such as stress, low self-esteem, and difficulty coping, and positive indicators representing adaptive functioning, self-efficacy, and enjoyment of daily activities, with phrasing adapted to the cultural context of the Saudi population. Respondents rate each item on a five-point Likert scale (Always, Often, Sometimes, Rarely, and Never), with negatively phrased items reverse-scored to allow calculation of a total score. Higher scores indicate a higher perceived quality of life, while lower scores reflect diminished well-being and elevated psychological distress or impaired coping. This measure therefore provides a culturally sensitive and psychometrically informed tool for assessing quality of life in relation to psychological stress, coping, and overall well-being. A first-order general factor model initially exhibited inadequate fit in the confirmatory factor analysis. In accordance with best-practice recommendations for Likert-type data, all items were treated as ordered categorical variables and the model was estimated using the robust weighted least squares estimator (WLSMV) to obtain appropriate parameter estimates and fit statistics for ordinal indicators. The hypothesized measurement structure retained a single latent factor representing overall quality of life, without specifying subordinate dimensions, thereby preserving the theoretical assumption of unidimensionality. To enhance model fit while maintaining the substantive integrity of the construct, a limited number of residual covariances were freely estimated between conceptually proximate items sharing similar wording or content (e.g., Q1–Q2, Q7–Q8, Q10–Q11). These correlated residuals were theoretically justified as capturing shared item-specific variance not explained by the general factor, rather than indicating additional latent constructs. Following these theoretically guided modifications, the model demonstrated good overall fit, as evidenced by strong incremental fit indices (CFI = 0.944; TLI = 0.923) and absolute fit indices within recommended thresholds (RMSEA = 0.046; SRMR = 0.010), indicating that the unidimensional model adequately reproduced the observed covariance structure. Examination of the standardized factor loadings revealed that all items loaded significantly on the general quality-of-life factor, with coefficients ranging from moderate to high in magnitude (approximately |0.42| to |0.79|), suggesting that each item made a meaningful contribution to the latent construct. The strongest loadings were observed for items reflecting core aspects of perceived well-being, whereas relatively lower—yet still substantial—loadings were found for items capturing more context-specific evaluations. Digital stress test The situational measure of digital stress was developed based on previous instruments assessing stress and digital overload, as denoted by Xie et al. [ 46 ], with adaptations to ensure cultural and cognitive relevance for the Saudi context. Item formulations were designed to capture both psychological and emotional dimensions, reflecting individuals’ internal experiences and affective responses to daily digital stimuli. The instrument aims to quantify the degree of psychological engagement and emotional reactivity resulting from continuous exposure to digital environments. It comprises five primary dimensions, each represented by five situational items, with responses recorded on a 5-point Likert-type scale ranging from 0 (no engagement) to 4 (high engagement), thereby capturing gradations of cognitive and emotional stress. The first dimension, availability pressure, assesses the perceived psychological strain associated with expectations to respond immediately to digital messages and notifications. Items simulate scenarios such as receiving an urgent work-related notification after a long day and feeling guilt if one does not respond promptly; receiving a request from a colleague during family time; encountering a time-sensitive notification while reflecting at the end of the day; receiving an urgent work message during personal tasks; and receiving a message from a supervisor while reading, generating internal conflict between leisure and social obligations. The second dimension, social acceptance anxiety, evaluates concern over how one’s online posts and comments are perceived by others. Items include considering posting a personal photo while fearing judgment; hesitating to contribute to an online discussion due to potential criticism; monitoring reactions to a personal achievement post and experiencing stress if expected engagement is lacking; posting an idea and noticing comments that trigger concern about acceptance or rejection; and intending to share sensitive content while anticipating possible misunderstanding or disagreement. The third dimension, fear of missing out, captures feelings of social exclusion and digital deprivation. Situations include observing friends’ photos from trips not attended, following posts about events missed, discovering that friends participated in activities without invitation, comparing oneself to peers’ educational or collaborative achievements, and seeing friends enjoying evening experiences in preferred locations, generating feelings of inadequacy and disconnection. The fourth dimension, digital overconnectivity, assesses stress resulting from exposure to multiple simultaneous digital stimuli and its impact on attention and cognitive functioning. Items involve receiving a flood of notifications after a busy day, experiencing frequent phone vibrations during meals, handling concurrent emails and chats, attempting to complete a report amid constant digital interruptions, and managing multiple messages during an online meeting, all eliciting progressively greater cognitive strain and stress. The fifth dimension, digital vigilance, evaluates compulsive engagement with messages and notifications and their interference with daily activities. Items simulate scenarios such as receiving notifications during a family gathering, placing the phone aside before sleep while remaining mentally alert, reaching for the phone during a lecture in anticipation of urgent messages, feeling compelled to check posts during casual outings, and searching for a misplaced phone, producing heightened anxiety and feelings of disconnection. All items were constructed to reflect a gradient of psychological and emotional engagement, ranging from minimal concern or low arousal to high immersion and distress. This design allows for a comprehensive assessment of digital stress as a multidimensional construct, capturing the complex interplay of cognitive, emotional, and behavioral responses in digital contexts. confirmatory factor analysis supporting a five-factor first-order structure encompassing Availability Pressure, Social Acceptance Anxiety, Fear of Missing Out, Digital Overconnectivity, and Digital Vigilance, all subsumed under a higher-order general digital stress factor. The second-order CFA indicated excellent model fit, with CFI = 0.984, TLI = 0.983, RMSEA = 0.027, and SRMR = 0.032, reflecting strong concordance between the observed data and the hypothesized hierarchical structure. Standardized factor loadings for first-order factors ranged from 0.580 to 0.788, and loadings on the second-order factor ranged from 0.871 to 0.918, confirming that each dimension meaningfully contributed to the overarching construct. Item-level R² values ranged from 0.336 to 0.649, while first-order factors accounted for 0.758 to 0.843 of variance, indicating coherent subscale representation. Omega reliability analysis demonstrated excellent internal consistency, with Omega Total = 0.96 and Omega Hierarchical = 0.85, indicating that the general factor explained 71% of the common variance (ECV = 0.71), while subscale Omegas ranged from 0.57 to 0.66, supporting reliable assessment of the individual dimensions. Table 1 Standardized Factor Loadings for the five-Factor of Digital stress Model Standardized Loading (First-Order) Loading on General Factor (Bifactor model) R² (Item) Omega Subscale Availability Pressure 0.60 Q1 0.580 0.918 0.336 Q2 0.597 0.918 0.357 Q3 0.631 0.918 0.398 Q4 0.649 0.918 0.421 Q5 0.675 0.918 0.455 Social Acceptance Anxiety 0.66 Q6 0.696 0.873 0.485 Q7 0.746 0.873 0.556 Q8 0.762 0.873 0.581 Q9 0.760 0.873 0.577 Q10 0.764 0.873 0.583 Fear of Missing Out 0.66 Q11 0.713 0.876 0.508 Q12 0.774 0.876 0.599 Q13 0.804 0.876 0.647 Q14 0.785 0.876 0.617 Q15 0.788 0.876 0.621 Digital Overconnectivity 0.59 Q16 0.748 0.879 0.559 Q17 0.765 0.879 0.585 Q18 0.805 0.879 0.649 Q19 0.793 0.879 0.629 Q20 0.724 0.879 0.525 Digital Vigilance 0.57 Q21 0.750 0.871 0.562 Q22 0.692 0.871 0.479 Q23 0.760 0.871 0.578 Q24 0.715 0.871 0.511 Q25 0.643 0.871 0.413 Overall / Second-Order Factor 0.85 Ethical consideration This study was conducted in full compliance with established ethical standards for research involving human participants. Prior to data collection, the research protocol was reviewed and approved by the Research Ethics Committee of the Faculty of Arts and Humanities, King Abdulaziz University (Serial Number: REC-FAH-KAU-2026-003, dated 31 December 2025). Participation in the study was entirely voluntary. All participants were provided with clear and comprehensive information regarding the purpose of the study, the nature of their involvement, and their right to decline participation or withdraw from the study at any time without penalty or adverse consequences. Informed consent was obtained from all participants before their inclusion in the study. To ensure confidentiality and privacy, no personally identifiable information was collected. Data were anonymized at the point of collection and used exclusively for scientific research purposes. Access to the data was restricted to the principal investigator, and all data were securely stored in password-protected electronic files in accordance with institutional data protection guidelines. The study posed minimal risk to participants. No deceptive procedures were employed, and no physical, psychological, or social harm was anticipated as a result of participation. Participants were not exposed to distressing content, and the survey instruments were designed to assess attitudes and experiences related to AI use without inducing discomfort or coercion. All procedures adhered to the ethical principles of respect for persons, beneficence, and justice, as well as to internationally recognized guidelines for human-subject research. The findings of the study will be reported in an aggregate form to ensure anonymity and will be used solely for scholarly purposes. Results Descriptive statistics Descriptive analyses of the study variables revealed meaningful variation in both AI Dependency and Digital Stress among participants. The AI Dependency Scale, encompassing the overall score Ai-Dep and its subscales, Direct Interaction Reliance (DIR), Practical/Applied Dependency (PAD), and Cognitive/Affective Reliance (CAR, showed that participants exhibited moderate to high levels of reliance on digital tools and AI-assisted resources in daily life. The total AI Dependency score averaged 43.8 (SD = 11.1), with subscale means of 17.1 for DIR, 14.9 for PAD, and 11.7 for CAR, indicating that participants were particularly engaged in direct interactions with digital tools and moderately dependent on practical or applied functionalities, while cognitive and affective reliance was slightly lower. Examination of variability measures indicated that the median absolute deviation (MAD) ranged from 2 for DIR to 7 for the total Ai-Dep score, reflecting individual differences in dependency patterns. Median scores closely aligned with the means, suggesting relatively symmetric distributions, while the 75th percentile (P75) values—20 for DIR, 18 for PAD, 14 for CAR, and 51 for Ai-Dep total—highlighted the upper range of dependency levels. Notably, the prevalence of participants scoring above the P75 ranged from 16.9% for DIR to 24.8% for Ai-Dep, demonstrating that approximately one-fifth to one-quarter of the sample exhibited high AI Dependency. The Digital Stress Scale, which comprised subscales of Availability Pressure (AP), Social Acceptance Anxiety (SAA), Fear of Missing Out (FoMO), Digital Overconnectivity (DO), and Digital Vigilance (DV), similarly revealed moderate levels of digital stress across participants. Subscale means ranged from 17.9 (AP) to 18.9 (SAA and DO), with the total Digital Stress score averaging 90.0 (SD = 12.5), indicating that participants experienced noticeable psychological and emotional engagement related to digital environments. Median scores were comparable to the means, with MAD values ranging from 3 to 4, reflecting consistency across participants, while P75 values spanned 20 to 102 across subscales and total scores. The prevalence of high scorers above P75 ranged from 21.0% for DV to 24.0% for PAD, AP, and DO, indicating that a substantial portion of the sample experienced elevated stress levels associated with digital overconnectivity, social pressures, and vigilance. Additionally, general well-being measures revealed that Anxiety scores averaged 16.2 (SD = 6.48), with a P75 of 21 and 22.5% of participants scoring above this threshold, while Quality of Life (QoL) scores averaged 39.4 (SD = 4.41), with a P75 of 42 and 19.4% of participants above this value. Overall, these descriptive findings illustrate both the central tendencies and the upper-bound prevalence of AI Dependency and Digital Stress in the sample, highlighting meaningful variability and suggesting that while most participants reported moderate levels of digital reliance and stress, a notable subset exhibited high engagement and susceptibility to digital-induced psychological strain. Table 2 Descriptive Statistics for Network Variables Variable Mean SD MAD Median P75 Prevalence > P75 (%) Anxiety 16.2 6.48 5 15 21 22.5 QoL 39.4 4.41 3 39 42 19.4 DIR 17.1 3.98 2 17 20 16.9 PAD 14.9 4.74 3 15 18 24.0 CAR 11.7 3.66 3 12 14 24.0 Ai-Dep 43.8 11.1 7 43 51 24.8 AP 17.9 3.88 3 18 20 24.0 SAA 18.9 4.59 4 19 23 21.5 FoMO 18.0 5.07 4 18 22 23.0 DO 18.9 4.54 4 20 22 24.0 DV 17.3 4.12 3 17 21 21.0 Digital stress 90.0 12.5 9 89 102 22.5 Shapiro-Wilk tests were conducted to evaluate the assumption of normality across all numeric study variables, including AI Dependency and Digital Stress subscales, as well as Anxiety and Quality of Life. Results indicated significant deviations from normality for most variables, with W statistics ranging from 0.943 for QoL to 0.992 for the overall AI Dependency score (Ai-Dep), and corresponding p-values well below 0.001 for most measures. Specifically, Anxiety (W = 0.956, p < .001) and Quality of Life (QoL; W = 0.943, p < .001) exhibited significant deviations from normality. Similarly, the AI Dependency subscales, Direct Interaction Reliance (DIR; W = 0.974, p < .001), Practical/Applied Dependency (PAD; W = 0.980, p < .001), and Cognitive/Affective Reliance (CAR; W = 0.982, p < .001)—demonstrated notable non-normal distributions, whereas the overall AI Dependency score showed only a modest departure from normality (W = 0.992, p < .001). Digital Stress subscales, encompassing Availability Pressure (AP), Social Acceptance Anxiety (SAA), Fear of Missing Out (FoMO), Digital Overconnectivity (DO), and Digital Vigilance (DV), also deviated significantly from normality, with W values ranging from 0.946 to 0.983 (all p < .001), while the total Digital Stress score indicated a slightly smaller departure (W = 0.975, p < .001. Despite the statistical significance of these departures, the W statistics were generally high, indicating modest skewness and kurtosis. Consequently, parametric analyses, including correlation and regression procedures, remain interpretable and appropriate for these data, while acknowledging the presence of slight non-normality in the distributions of several subscales. Pearson correlations among AI Dependency subscales, Digital Stress subscales, Anxiety, and Quality of Life (QoL) are summarized in Table 3 . As anticipated, subscales within each construct demonstrated moderate to strong positive correlations, indicating good internal coherence. Within the AI Dependency Scale, Direct Interaction Reliance (DIR) correlated strongly with Practical/Applied Dependency (PAD; r = 0.712) and Cognitive/Affective Reliance (CAR; r = 0.649), while the total AI Dependency score (Ai-Dep) was highly correlated with each subscale, DIR (r = 0.876), PAD (r = 0.928), and CAR (r = 0.880), highlighting the hierarchical structure of the construct. Similarly, the Digital Stress subscales, including Availability Pressure (AP), Social Acceptance Anxiety (SAA), Fear of Missing Out (FoMO), Digital Overconnectivity (DO), and Digital Vigilance (DV), showed moderate to strong correlations with the total Digital Stress score (r = 0.63–0.87), supporting the coherence of the higher-order factor. Anxiety was modestly associated with Ai-Dep (r = 0.228) and with the Digital Stress subscales, reflecting the psychological link between digital dependency and stress-related affective states. In contrast, QoL demonstrated weaker correlations with both AI Dependency and Digital Stress measures, suggesting that while higher reliance on digital tools and increased digital stress may relate to psychological distress, their immediate association with overall quality of life is more nuanced. Table 3 Correlation matrix between study variables underlining the network analysis. Anxiety QoL DIR PAD CAR Ai-Dep AP SAA FoMO DO DV Digital stress Anxiety 1 0.343 0.064 0.246 0.302 0.228 -0.170 -0.107 -0.126 -0.159 -0.177 -0.172 QoL 1 0.237 0.249 0.279 0.283 -0.079 0.001 -0.038 -0.044 -0.086 -0.056 DIR 1 0.712 0.649 0.876 -0.005 -0.011 0.032 0.080 0.035 0.031 PAD 1 0.745 0.928 0.002 0.002 0.045 0.008 -0.013 0.012 CAR 1 0.880 0.005 -0.038 -0.003 0.010 -0.073 -0.023 Ai-Dep 1 0.000 -0.016 0.030 0.035 -0.017 0.009 The observed low correlations between overall digital stress and key study variables highlighted the limitations of conventional bivariate analyses in capturing the complex interplay of students’ psychological responses. This prompted the inclusion of digital stress dimensions in a network analysis, revealing their central role in structuring cognitive, emotional, and behavioral dynamics, and providing a more precise understanding of how digital stress shapes AI dependency, anxiety, and quality of life in the academic environment. Network Estimation An EBICglasso Gaussian Graphical Model was estimated using qgraph, with partial correlations computed via cor_auto. The resulting network depicted AI Dependency (Ai-Dep), Anxiety, Quality of Life (QoL), and the five digital stress dimensions (Availability Pressure [AP], Social Acceptance Anxiety [SAA], Fear of Missing Out [FoMO], Digital Overconnectivity [DO], and Digital Vigilance [DV]) as nodes connected by weighted edges representing partial correlations. The network visualization (see Fig. 1 ) indicated that digital stress components formed a dense subnetwork, with AP, SAA, and FoMO emerging as highly interconnected nodes. AI Dependency was centrally located and connected to both cognitive–affective and digital stress variables, while QoL was positioned peripherally, indicating its outcome-like role. Centrality Analyses Centrality indices revealed that Digital Overconnectivity (DO, Strength = 0.911), Fear of Missing Out (FoMO, Strength = 0.879), and Availability Pressure (AP, Strength = 0.886) were the most central nodes in terms of connectivity. AI Dependency exhibited moderate strength (0.304) but meaningful expected influence (0.304–0.304) as a structurally foundational node connecting cognitive reliance with stress responses. QoL and Anxiety had lower centrality values, consistent with their roles as outcome or mediating variables rather than initiators of network activation. Table 2 summarizes key centrality metrics. Table 4 Centrality Measures for Network Variables Node Strength Closeness Betweenness InExpectedInfluence OutExpectedInfluence Anxiety 0.477 0.00744 20 0.268 0.268 QoL 0.448 0.00665 0 0.448 0.448 Ai-Dep 0.304 0.00557 0 0.304 0.304 AP 0.886 0.00921 0 0.807 0.807 SAA 0.855 0.00864 0 0.855 0.855 FoMO 0.879 0.00924 0 0.879 0.879 DO 0.911 0.00983 0 0.881 0.881 DV 0.867 0.01058 18 0.767 0.767 These results indicate that digital stress dimensions, particularly DO, FoMO, and AP, act as hubs driving network dynamics, whereas AI Dependency serves as a structural bridge linking cognitive reliance to these hubs (see, Figs. 2 and 3 ). The psychological network analysis revealed that Betweenness indices—representing the “bridging” role of nodes, were near zero for most central variables, including AI Dependency (Ai-Dep), Availability Pressure (AP), Social Acceptance Anxiety (SAA), Fear of Missing Out (FoMO), and Digital Overconnectivity (DO). From a network psychology perspective, this does not imply that these variables are unimportant; rather, they function as structural hubs that directly influence the dissemination of effects throughout the network without serving as bridges between distinct clusters of variables. In other words, these variables exert their influence broadly across the network, either directly or indirectly, reflecting their structural centrality in organizing students’ digital psychological experiences. Conversely, the low Closeness indices for variables such as AI Dependency, Quality of Life (QoL), and Anxiety suggest that these nodes occupy peripheral positions within the network. Psychologically, this can be interpreted as these variables being more reactive than generative—they tend to reflect the influence of central digital stressors rather than initiating network activity. For instance, AI Dependency does not directly induce anxiety; instead, it establishes a cognitive environment that shapes how students respond to digital pressures, which in turn affects their experienced anxiety and perceived quality of life. The near equivalence of In- and Out-Expected Influence indices indicates that the impact of each node on the network and the influence it receives from others are roughly balanced. This reflects a reciprocal and dynamic nature within the digital stress network, wherein each variable can both affect and be affected by other nodes to a similar degree, endowing the system with integrated dynamism. From this analysis, it can be concluded that the most influential variables in structuring students’ digital psychological experience are DO, FoMO, and AP, which serve as pressure hubs determining network activation and the distribution of psychological impact. In contrast, variables such as Anxiety and QoL occupy peripheral positions and largely reflect the downstream consequences of these dynamics rather than driving them. This interpretation provides a nuanced understanding of how AI Dependency and digital stress shape students’ psychological experiences and highlights potential targets for interventions aimed at enhancing mental health in technology-saturated educational environments. Overall, the findings indicate that students’ psychological experiences in digital learning contexts emerge from a dynamic interplay of cognitive and emotional interactions: central digital stressors drive attention and cognitive load, while AI Dependency functions as an internal regulatory factor, redistributing cognitive effort and shifting the locus of control toward technological resources. Consequently, accumulated cognitive–emotional tensions manifest in elevated anxiety and altered perceptions of quality of life. In essence, negative psychological states do not arise as immediate reactions to technology or digital pressures but are the product of continuous psychological reorganization, wherein students increasingly rely on digital tools to offload evaluative and decision-making demands, reflecting a fundamental shift in perceived competence and self-regulatory control (see Figs. 2 and 3 ). The psychological network analysis shows a complex structure implied the experiences of university students in AI-saturated learning environments. The findings suggest that psychological effects cannot be reduced to simple linear relationships between usage intensity and stress or anxiety. Rather, the network reflects a dynamic system in which cognitive, emotional, and relational variables interact through conditional partial connections, indicating deeper patterns of psychological organization beyond mere frequency or intensity of AI use. Within this framework, AI dependency (Ai-Dep) emerges as a structurally foundational variable. Its significance is not necessarily indicated by high observable centrality metrics but rather by its explanatory role within the overall system. From a network perspective, Ai-Dep functions as a predisposing cognitive condition or a relatively stable psychological tendency that reorganizes the individual’s engagement with other digital stressors, rather than acting as a highly active daily interaction node. Psychologically, this dependency reflects a gradual shift in the locus of cognitive control, wherein individuals increasingly delegate evaluation and self-judgment to external outputs implicitly assumed to be more efficient. This pattern does not signify a direct loss of cognitive capacity but rather a redistribution of cognitive effort, potentially creating a perceptual gap between technologically supported performance and unaided self-efficacy. Within this network, anxiety occupies a pivotal position as a mediating variable linking cognitive dependency to digital stress. Network properties indicate that anxiety does not function as a continuous affective state but as a conditional variable that is activated when cognitive dependency intersects with high digital performance demands. This form of anxiety extends beyond traditional academic anxiety to reflect a cognitive–motivational tension associated with diminished perceptions of competence and autonomy when performing tasks without technological support, consistent with Self-Determination Theory’s propositions regarding the frustration of autonomy and competence needs. Availability pressure (AP) serves a critical contextual role, representing a psychological climate characterized by continuous digital presence and immediate responsiveness. Network analysis indicates that AP acts as an enabling factor for activating other variables, increasing the likelihood of AI tool use to reduce temporal and cognitive burdens, thereby reinforcing the dependency cycle. AP thus represents not merely temporal pressure but a digital–cultural framework that redefines performance and achievement standards within the academic environment. Social Acceptance Anxiety (SAA) functions to translate individual cognitive pressure into relational and identity-level stress. It reflects concerns about losing social standing or relative position in competitive digital academic spaces, where technical proficiency and rapid task completion become implicit criteria for acceptance. Within the network, SAA links self-worth to digital presence and engagement, thereby heightening students’ propensity to adopt AI-dependent strategies not only to achieve academic outcomes but also to maintain a sense of belonging and status. Fear of Missing Out (FoMO) acts as an accelerative emotional mechanism within the network, amplifying availability pressure and promoting hyper-connectivity. FoMO extends beyond concern over lost information or opportunities to represent ongoing tension associated with desynchronization from academic and social peers. Network analysis shows that FoMO contributes to increased digital interaction density, raising the likelihood of experiencing elevated digital load. Digital overload (DO) and digital vigilance (DV) represent central convergence points in the network, reflecting cumulative effects of multiple cognitive and emotional stressors. DO embodies cognitive fatigue arising from input exceeding processing capacity, while DV reflects a perceptual pattern characterized by sustained readiness for digital stimuli even in their absence. Importantly, this description is structural and psychological, not clinical, capturing patterns of attention and vigilance in high-density digital environments. Finally, quality of life (QoL) appears as a distal outcome reflecting the long-term cumulative effects of network dynamics. Its network position suggests that QoL is influenced by the interplay of stress, dependency, and cognitive load rather than acting as an active driver within the current model. While QoL may play a dynamic role in longitudinal contexts, here it functions as a holistic indicator of system-level psychological health. Overall, these findings indicate that the relationship between AI use and mental health is neither directly causal nor unidirectional. Rather, it reflects a complex psycho-digital structure in which concepts of competence, autonomy, belonging, and performance are reorganized. Network analysis positions AI dependency as a structural condition, with availability pressures and social acceptance anxiety forming the contextual stressors, FoMO acting as an accelerant, and the heaviest psychological outcomes manifesting through digital overload and continuous vigilance. These processes cumulatively contribute to gradual declines in quality of life, positioning AI not as a neutral tool but as a factor reshaping the psychological architecture of human experience in digital academic environments. Discussion The findings of the present study indicate that reliance on artificial intelligence (AI) cannot be reduced to a neutral technical behavior or merely a supportive educational tool; rather, it constitutes a complex psychological structure in which cognitive, motivational, and emotional processes intersect. This perspective aligns with prior observations that repeated interaction with intelligent systems reshapes self-regulatory patterns and decision-making processes, extending beyond simple functional usage [ 1 , 2 , 3 ]. By moving from a logic of “use” to a logic of “dependency,” this study positions AI reliance as a relatively stable psychological state with multiple manifestations across networks of anxiety, digital stress, and quality of life. Unlike most previous studies, which considered these variables as independent or adjacent factors [ 7 , 9 ], network analysis in this study reveals a connected structure in which AI dependency serves as a central node reorganizing students’ psychological experiences within digital academic environments. The psychological network demonstrates that AI dependency is more strongly associated with dimensions of digital stress, such as digital vigilance, fear of missing out, and pressure from constant availability, than with general anxiety or quality of life. This pattern is consistent with prior findings indicating that psychological effects of smart technologies often first emerge as digital pressures and compulsive monitoring behaviors before manifesting as overt anxiety or reductions in well-being [ 47 , 48 ] Consequently, the psychological impact of AI dependency may operate initially through restructuring the individual’s relationship with digital environments rather than through immediate affective symptoms, partially contradicting earlier assumptions of direct links between technology use and anxiety [ 9 ]. This provides a new explanatory dimension that extends beyond previous studies focusing on anxiety or life satisfaction as end-point outcomes, without examining the intermediate mechanisms that translate dependency from cognitive to affective levels. From a cognitive offloading perspective, high AI reliance facilitates the transfer of core cognitive functions, reasoning, evaluation, and decision-making from the individual to the AI system, gradually weakening self-monitoring processes. This interpretation is supported by prior empirical evidence showing that excessive reliance on digital media correlates with reduced internal cognitive effort and memory retention [ 29 , 31 ]. Cognitive offloading does not merely reduce effort but also reshapes perceived sources of competence, rendering successful performance implicitly dependent on system availability. Network analysis indicates that this form of offloading does not initially elicit overt anxiety but generates sustained digital vigilance and fear of detachment from intelligent sources, consistent with evidence on the cognitive and emotional costs of digital interruptions. Unlike studies attributing AI-related anxiety to technological complexity or opacity [ 2 ], the present study draws on Self-Determination Theory to propose that anxiety emerges indirectly from the frustration of basic psychological needs for autonomy and competence, rather than from AI use per se [ 33 , 34 ] Increased reliance on intelligent systems undermines perceived control and diminishes confidence in independent task performance, creating anticipatory anxiety concerning potential failure when AI support is unavailable. This framework advances beyond explanations centered on technology exposure and aligns with research showing that motivational need frustration drives sustained emotional responses in digital contexts [ 3 , 10 ]. Examining quality of life within the network revealed that it is not directly affected by AI dependency but emerges as a distal outcome of accumulated anxiety and digital stress. This aligns with cumulative well-being models, suggesting that declines in quality of life typically result from sustained stressors rather than immediate triggers [ 40 ]. Such a framework reconciles previous mixed findings regarding AI’s impact on well-being, where some studies reported positive effects [ 8 , 7 ] and others reported adverse outcomes [ 25 , 23 ]. The present study suggests that these discrepancies may reflect the omission of mediating pathways through which short-term functional benefits transform into long-term psychological costs when use evolves into dependency. This study is among the first to provide empirical evidence from an Arab context that AI dependency interacts with cultural characteristics, academic expectations, and self-monitoring norms, potentially explaining relatively elevated dimensions such as digital social acceptance and fear of evaluation. These findings correspond with cross-cultural literature emphasizing the role of societal context in shaping technology-mediated experiences [ 11 , 21 ]. Methodologically, the study advances prior work by employing psychological network analysis to uncover non-linear relationships, identify central variables, and characterize internal dynamics of the psychological system, as recommended in complex psychological research [ 49 , 50 ]. This approach enhances interpretive precision and informs interventions targeting core nodes such as cognitive dependency and digital vigilance rather than focusing exclusively on final outcomes like anxiety or reduced quality of life. In sum, the current findings offer a comprehensive explanatory model in which AI dependency, through cognitive offloading and motivational need frustration, reshapes the student’s psychological experience in digital academic environments. This restructuring has cumulative effects on anxiety, digital stress, and quality of life. The study not only extends existing knowledge but organizes it within a theoretically and methodologically rigorous framework, highlighting new avenues for research and interventions in the era of AI. Conclusion This study offers a nuanced understanding of AI dependency as a central psychological construct that reorganizes cognitive, motivational, and emotional processes within the digital learning experiences of university students. By conceptualizing reliance on AI as more than mere usage, the research highlights how cognitive offloading and frustration of autonomy and competence contribute to heightened anxiety, digital stress, and ultimately, declines in quality of life. The study’s strengths lie in its use of psychological network analysis, which uncovers non-linear, system-level interactions and identifies key structural nodes, such as AI dependency, availability pressure, and social acceptance anxiety, thereby offering actionable targets for interventions aimed at mitigating maladaptive reliance and fostering digital well-being. Furthermore, the inclusion of an Arab university context expands the cross-cultural generalizability of theoretical models and provides empirical evidence of culturally specific dynamics in AI-mediated academic environments. At the same time, the findings underscore potential risks: unregulated or excessive dependency may erode cognitive autonomy, amplify vigilance and hyper-connectivity, and produce cumulative psychological strain that is not immediately visible. Overall, the study advances both theoretical and practical understanding of AI’s psychological impact, offering a framework for designing balanced, culturally informed strategies that maximize the benefits of AI while minimizing its cognitive and emotional costs. Abbreviations AI Dep–Artificial Intelligence Dependency AP Availability Pressure QoL Quality of Life SAA Social Acceptance Anxiety FoMO Fear of Missing Out DO Digital Overconnectivity DV Digital Vigilance DIR Digital Interaction Reliance PAD Practical Applied Dependency CAR Cognitive and Affective Reliance Declarations This section contains all the declarations required for submission, organized according to the journal's guidelines. Ethics approval and consent to participate This study was conducted in full compliance with the Declaration of Helsinki (https://www.wma.net/policies-post/wma-declaration-of-helsinki/). The research protocol was reviewed and granted prior ethical approval by the Research Ethics Committee of the Faculty of Arts and Humanities, King Abdulaziz University (Serial Number: REC-FAH-KAU-2026-003, dated 31 December 2025). Participation in the study was entirely voluntary. All participants were provided with clear and comprehensive information regarding the purpose of the study, and informed consent to participate was obtained online from all participants before their inclusion in the study. Consent for publication Not applicable. This manuscript does not contain any person’s data in any form (including individual details, images, or videos). Availability of data and materials The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. Access to the data may be subject to ethical considerations to ensure participant confidentiality and privacy. Competing Interests The authors declare that they have no competing interests. Funding This project was funded by KAU Endowment (WAQF) at King Abdulaziz University, Jeddah, Saudi Arabia. The authors, therefore, acknowledge with thanks WAQF and the Deanship of Scientific Research (DSR) for financial support. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Authors' contributions All authors have read and agreed to the published version of the manuscript. Conceptualization, F.K.E. and M.A.M.; Methodology, F.K.E., J.F.A., and M.A.M.; Formal Analysis, M.A.M.; Investigation, F.K.E. and J.F.A.; Data Curation, J.F.A.; Writing—Original Draft Preparation, M.A.M.; Writing—Review and Editing, F.K.E. and J.F.A.; Supervision, F.K.E.; Project Administration, F.K.E.; Funding Acquisition, F.K.E. All authors have read and agreed to the published version of the manuscript. Acknowledgements This project was funded by KAU Endowment (WAQF) at King Abdulaziz University, Jeddah, Saudi Arabia. The authors, therefore, acknowledge with thanks WAQF and the Deanship of Scientific Research (DSR) for financial support. References Stanford Human-Centered AI. AI Index Report 2025. Stanford University; 2025. Prasanth A, Densy JV, Surendran P, Bindhya T. Role of artificial intelligence and business decision making. Int J Adv Comput Sci Appl . 2023;14(6):22–31. Available from: https://www.academia.edu/download/106780771/Paper_103-f Frenkenberg A, Hochman G. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8870746","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":608391275,"identity":"6d3f05bc-c2c6-45d2-a6b9-320aa1ca2a24","order_by":0,"name":"Fatma Khalifa Elsayed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIie3PsYrCMBzH8b8E0iW16z+06CukFMRB8FUqBWen4yYtCJnqA/gWgi+gBHQRXTt6uHY4cfHAg/sfbiJRN4d8IUvgwy8BcLneMK/gHGCAATBmrleLB0Rs/olCmXs8e4mAzAORPEnEqr8/qzYGY3FSPxoa9TJl32cb8fU6LhQiGn/em2hIZJlyWVhIN/A0CoVDILLwNfRmREDYVojIC600jTgsfzWMiLDjxf6wVUgrqIxgGa2kqkwhtK7Q98OISGx4kkRbjKebLx1GVtJvyepziI2dOcjqo9OsrzNzrCzkNqRTy18ALpfL5brXH4/mRMBd6RZLAAAAAElFTkSuQmCC","orcid":"","institution":"King Abdulaziz University","correspondingAuthor":true,"prefix":"","firstName":"Fatma","middleName":"Khalifa","lastName":"Elsayed","suffix":""},{"id":608391276,"identity":"6ce93ee3-8955-4c55-913c-3fd187549b8d","order_by":1,"name":"Jahz Fahd Al-Mutairi","email":"","orcid":"","institution":"King Abdulaziz University","correspondingAuthor":false,"prefix":"","firstName":"Jahz","middleName":"Fahd","lastName":"Al-Mutairi","suffix":""},{"id":608391277,"identity":"65eb718e-2052-4c37-9812-c1cf46171772","order_by":2,"name":"Mahmoud Ali Moussa","email":"","orcid":"","institution":"Suez Canal University","correspondingAuthor":false,"prefix":"","firstName":"Mahmoud","middleName":"Ali","lastName":"Moussa","suffix":""}],"badges":[],"createdAt":"2026-02-13 10:38:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8870746/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8870746/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105150207,"identity":"72c062f5-5e84-4b35-bf0f-c89d0fe18f84","added_by":"auto","created_at":"2026-03-22 15:00:50","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":26302,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork Structure of AI Dependency, Anxiety, QoL, and Digital Stress Dimensions\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8870746/v1/4ba73422085526629689c534.jpeg"},{"id":105150205,"identity":"b1f6148e-71cd-4e39-8ae5-de5a1bd9155b","added_by":"auto","created_at":"2026-03-22 15:00:50","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64931,"visible":true,"origin":"","legend":"\u003cp\u003eNode Centrality Indices\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8870746/v1/638e009b50838a78a83f871f.jpeg"},{"id":105563808,"identity":"96364638-6f15-479e-9642-0676cdfc90d8","added_by":"auto","created_at":"2026-03-27 12:47:53","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47369,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork Centrality Indices for AI Dependency and Digital Stress Variables\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8870746/v1/053c69d7aea508dce0276feb.jpeg"},{"id":105569179,"identity":"05d327a9-24b3-40ea-825b-7487501a5ee2","added_by":"auto","created_at":"2026-03-27 13:11:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1161643,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8870746/v1/53ed4b96-d3da-469f-8319-5c6fa7a207dd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"AI-Dependency and Its Relationships with Anxiety, Quality of Life, and Digital Stress","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, the integration of artificial intelligence (AI) technologies into everyday life has accelerated at an unprecedented pace. AI applications have evolved from peripheral technical aids into core infrastructures shaping education, work practices, healthcare delivery, and decision-making processes. What was once confined to specialized research domains has now become a pervasive and influential force across personal and professional contexts [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This rapid diffusion signals not merely technological advancement, but a profound transformation in how cognitive labor, judgment, and problem-solving are distributed between humans and intelligent systems.\u003c/p\u003e \u003cp\u003eEmerging literature suggests that reliance on AI systems increasingly extends beyond instrumental task support toward a form of psychological and behavioral dependency. In this mode of interaction, individuals tend to delegate core cognitive functions\u0026mdash;such as reasoning, evaluation, and decision-making\u0026mdash;to algorithmic outputs, often with minimal critical scrutiny [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Such reliance reflects a qualitative shift in human\u0026ndash;technology interaction, wherein AI systems are no longer perceived solely as tools, but as epistemic authorities that shape users\u0026rsquo; confidence, agency, and cognitive engagement.\u003c/p\u003e \u003cp\u003eAt the global level, AI adoption has expanded at a historically unparalleled rate. According to the AI Index Report [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], AI technologies represent one of the fastest-spreading innovations in modern history, surpassing even the early diffusion trajectories of electricity and the internet [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This rapid proliferation underscores the urgency of examining not only the functional benefits of AI but also its psychological, cognitive, and social implications for users embedded in AI-mediated environments.\u003c/p\u003e \u003cp\u003eWithin the Saudi Arabian context, recent official statistics indicate that approximately 21.5% of internet users employ AI-based tools, reflecting an early yet rapidly evolving stage of individual-level adoption [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Complementing these figures, a national survey revealed that nearly 49% of Saudi citizens report general use of AI applications, with ChatGPT emerging as the most frequently used tool (41%), signaling growing technological awareness and societal engagement with generative AI systems [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These trends highlight the relevance of investigating AI-related psychological phenomena within local and culturally specific contexts.\u003c/p\u003e \u003cp\u003eThe university environment has witnessed a marked expansion in the use of generative AI tools. Students increasingly rely on these systems for completing academic assignments, generating ideas, summarizing scientific texts, and supporting self-directed learning. When employed in a balanced and reflective manner, such usage has been associated with enhanced academic efficiency, reduced cognitive load, and accelerated access to information [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Motivations underlying this high adoption rate include the desire to facilitate learning, improve academic productivity, optimize educational efficiency, and accelerate knowledge acquisition through immediate informational support [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, contemporary scholarship increasingly conceptualizes AI dependency as more than a function of usage intensity. Rather, it is understood as a psychologically embedded behavioral pattern encompassing cognitive, emotional, and motivational dimensions that shape decision-making processes. Recent studies characterize AI dependency as a distinct form of human\u0026ndash;technology interaction in which trust, perception, and psychological needs converge, leading individuals to outsource analytical and evaluative thinking to intelligent systems rather than relying on their own cognitive capacities [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Empirical evidence further indicates that high levels of AI dependency are associated with diminished critical thinking abilities, driven by cognitive fatigue and a shift toward externalized problem-solving strategies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These findings suggest that AI dependency possesses a clear cognitive dimension, alongside behavioral and emotional consequences linked to users\u0026rsquo; self-perceptions and trust in technology.\u003c/p\u003e \u003cp\u003eDespite its potential benefits, intensive and unregulated use of AI tools\u0026mdash;particularly in academic and daily task contexts\u0026mdash;introduces significant psychological and cognitive challenges [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Research in human\u0026ndash;AI interaction distinguishes between adaptive use, which augments human cognition and supports informed decision-making, and maladaptive or unbalanced dependency, wherein AI systems supplant independent critical reasoning and erode users\u0026rsquo; cognitive autonomy [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In the latter case, users may accept AI outputs uncritically, resulting in weakened analytical and evaluative skills.\u003c/p\u003e \u003cp\u003eSome scholars further conceptualize excessive AI dependency as a form of behavioral addiction, characterized by the substitution of algorithmic processes for complex cognitive functions in a manner that undermines intellectual autonomy. Such dependency has been linked to impaired self-regulation among students, reduced academic life satisfaction [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], heightened fear of missing out [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], impulsive decision-making aimed at avoiding perceived informational loss [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], attentional fragmentation, and diminished concentration during academic tasks [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Additional consequences include elevated academic anxiety, increased cognitive load, and erosion of higher-order cognitive skills such as critical thinking and problem-solving [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, the personalized and interactive nature of generative AI systems increases the likelihood that users develop feelings of attachment or perceived intimacy, potentially overestimating the depth and reciprocity of their relationship with AI. Such dynamics may foster emotional or social dependency on generative AI systems [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], further complicating the psychological landscape of AI use.\u003c/p\u003e \u003cp\u003eGrowing reliance on AI systems has also been associated with heightened levels of anxiety and psychological uncertainty. This relationship may stem from diminished confidence in one\u0026rsquo;s own cognitive abilities when performing tasks without technological assistance, fear of failure in the absence of AI support, and the persistent cognitive strain associated with interacting with complex AI systems. Frenkenberg and Hochman [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] argue that AI-related anxiety can paradoxically reinforce excessive dependency by functioning as an emotional coping mechanism, indicating that anxiety is not merely a transient response but a critical psychological indicator shaping technology use patterns. Supporting this view, recent findings demonstrate that anxiety related to learning and using AI correlates with lower self-efficacy and increased cognitive-emotional stress, thereby justifying the inclusion of anxiety as a central psychological variable in studies of technological dependency [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond anxiety, evidence suggests that AI-related stress mediates the relationship between technological dependency and diminished quality of life. Recent analyses indicate that technostressors associated with AI\u0026mdash;such as perceived technological insecurity, system complexity, and expectations of constant availability\u0026mdash;intensify negative emotional states, which in turn undermine key indicators of quality of life, including life satisfaction, psychological well-being, and work\u0026ndash;life balance [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eQuality of life is widely conceptualized as a multidimensional construct encompassing psychological, social, and functional dimensions of individual and collective well-being. While some studies highlight the potential of AI to enhance quality of life by improving efficiency, supporting healthcare, and facilitating access to digital services [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], others report adverse effects linked to increased insecurity, workload intensification, and reduced autonomy [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This inconsistency underscores the non-deterministic nature of AI\u0026rsquo;s impact on quality of life, suggesting that outcomes depend heavily on design features, usage patterns, and psychosocial contexts.\u003c/p\u003e \u003cp\u003eAdditionally, sustained interaction with AI-enhanced digital environments has given rise to novel forms of psychological strain, most notably digital stress. Digital stress is conceptualized as a state of psychological pressure resulting from continuous exposure to digital stimuli, information overload, perpetual connectivity, and fear of missing out [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Among university students, such stress has been linked to reduced academic performance, mental exhaustion, and diminished digital well-being, particularly in the absence of regulatory frameworks that promote balanced and health-oriented technology use [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUniversity students are considered especially vulnerable to digital stress due to their intensive reliance on digital technologies for learning, communication, and academic achievement, particularly amid the widespread adoption of digital and hybrid education models [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the growing body of research on AI applications and their psychological implications, the literature reveals a notable gap in conceptualizing AI dependency as an independent psychological construct distinct from mere usage frequency. Moreover, there is a lack of integrative explanatory models that simultaneously examine AI dependency, anxiety, quality of life, and digital stress within a unified framework. Compounding this limitation, most existing studies have been conducted in Western contexts, with limited empirical investigation in Arab societies or among university populations. This gap restricts the generalizability of current findings and underscores the necessity of the present study.\u003c/p\u003e\n\u003ch3\u003eTheoretical framework\u003c/h3\u003e\n\u003cp\u003eThe present study is grounded in a tightly integrated theoretical model that conceptualizes AI dependency as a maladaptive cognitive\u0026ndash;motivational state emerging from repeated cognitive offloading and functioning as a proximal determinant of anxiety, digital stress, and quality of life. Drawing on Cognitive Offloading Theory, AI dependency is understood as a progressive shift in the locus of cognitive control, wherein individuals increasingly substitute internally generated reasoning, judgment, and problem-solving processes with algorithmic outputs [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This substitution does not merely reduce cognitive effort; rather, it restructures metacognitive monitoring by diminishing users\u0026rsquo; active engagement in evaluation and verification processes [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Over time, this pattern leads to a reduced perception of personal cognitive efficacy and an internalized belief that effective task performance is contingent upon AI availability, thereby establishing dependency as a stable psychological orientation rather than a situational behavior [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBuilding on this cognitive foundation, Self-Determination Theory (SDT) provides a precise motivational explanation for the emergence of anxiety within AI-dependent individuals. According to SDT, psychological well-being is contingent upon the satisfaction of the basic needs for autonomy and competence [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. AI dependency systematically frustrates these needs by reallocating agency from the individual to the technological system. As autonomy is compromised, individuals experience reduced perceived control over task execution; as competence is undermined, they exhibit declining confidence in their unaided cognitive abilities. This dual need frustration generates anxiety through anticipatory threat appraisal\u0026mdash;specifically, fear of underperformance, cognitive inadequacy, or failure in contexts where AI assistance is limited or unavailable [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Within this framework, anxiety is not a byproduct of AI complexity, but a direct motivational consequence of dependency-driven erosion of self-regulatory capacity and internal mastery [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Transactional Model of Stress and Coping further specifies how AI dependency and anxiety interact to produce sustained digital stress. According to this model, stress arises when individuals appraise environmental demands as exceeding their perceived coping resources [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. AI-dependent individuals, whose self-efficacy has been weakened through chronic cognitive delegation, are more likely to appraise digital and academic demands as threatening rather than manageable [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Anxiety amplifies this appraisal bias by heightening vigilance to potential failure and increasing sensitivity to performance-related cues. Consequently, dependent users experience digital environments as cognitively intrusive and psychologically taxing, characterized by information overload, pressure for constant responsiveness, and fear of technological inadequacy\u0026mdash;core components of digital stress and technostress in AI-mediated contexts [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWithin this integrated framework, quality of life is conceptualized as a distal outcome shaped by the prolonged activation of these cognitive, motivational, and stress-related processes. Persistent anxiety and digital stress compromise psychological well-being by depleting emotional resources, impairing concentration, and reducing satisfaction with academic and personal functioning [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Moreover, AI dependency indirectly undermines quality of life by eroding perceived autonomy and competence, which are core determinants of well-being across psychological and educational domains [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. While AI systems may enhance efficiency in the short term, dependency-driven reliance transforms these gains into long-term psychological costs, manifesting as reduced life satisfaction, diminished sense of control, and impaired balance across life domains.\u003c/p\u003e \u003cp\u003eCritically, this model positions AI dependency as the central organizing construct that initiates and sustains the relationships among anxiety, digital stress, and quality of life. Rather than treating these variables as parallel consequences of technology use, the framework specifies a sequential pathway in which cognitive offloading fosters dependency, dependency frustrates motivational needs, anxiety intensifies maladaptive stress appraisals, and sustained digital stress ultimately degrades quality of life. By integrating cognitive, motivational, and stress theories at a mechanistic level, this study advances a psychologically precise explanation of how reliance on intelligent systems reshapes human functioning in AI-saturated environments.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eProblem statement\u003c/h2\u003e \u003cp\u003eAlthough artificial intelligence has become deeply embedded in students\u0026rsquo; academic and daily functioning, its expanding integration has introduced a qualitatively new psychological phenomenon, \u003cem\u003eAI dependency\u003c/em\u003e, whose implications for anxiety, quality of life, and digital stress remain insufficiently defined and empirically tested [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Existing research largely conceptualizes AI engagement as a neutral, instrumental, or performance-enhancing form of tool use, emphasizing efficiency, academic productivity, and learning facilitation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], while overlooking how sustained reliance on AI for cognitive, evaluative, and decision-making tasks may foster dependency that undermines psychological autonomy, perceived competence, and cognitive self-efficacy [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Consequently, the psychological mechanisms through which AI dependency contributes to heightened anxiety, increased digital stress, and diminished quality of life remain theoretically fragmented and insufficiently articulated within a unified explanatory framework [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis limitation is particularly salient among university students, who constitute a high-risk population due to their intensive exposure to AI-mediated learning environments, persistent academic performance pressures, and continuous digital connectivity [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Although prior studies have examined anxiety related to AI use [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], technostress in digital and AI-supported contexts [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and quality of life outcomes associated with technology use [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], these constructs are typically investigated in isolation rather than as interdependent outcomes arising from a shared dependency-driven process. As a result, the potential mediating and sequential relationships among AI dependency, anxiety, digital stress, and quality of life remain largely unexplored [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, the absence of integrative psychological models is compounded by a pronounced lack of empirical evidence from non-Western and Arab cultural contexts, despite the rapid expansion of AI adoption in Saudi Arabia and similar societies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This geographic and cultural imbalance limits the generalizability of existing findings and obscures culturally specific dynamics shaping AI dependency and its psychological consequences. Accordingly, there is a critical need to systematically conceptualize AI dependency as a central psychological construct and to empirically examine its relationships with anxiety, digital stress, and quality of life within a unified theoretical framework. Addressing this problem is essential for advancing theory in human\u0026ndash;AI interaction and for informing culturally responsive educational and mental health interventions in AI-intensive academic environments.\u003c/p\u003e \u003c/div\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDesign\u003c/h2\u003e \u003cp\u003eThis study employed a quantitative, non-experimental design to explore the relationships between AI dependency, anxiety, digital stress, and quality of life among university students. The design was suited to analyzing naturally occurring psychological variations related to AI use in academic settings.\u003c/p\u003e \u003cp\u003eData were collected via self-administered standardized measures during a single assessment period, allowing for the evaluation of participants' perceived AI dependency and related psychological outcomes. This method minimized participant burden while maintaining ecological validity and enabled the examination of relational patterns among variables.\u003c/p\u003e \u003cp\u003eThe analysis followed a theoretically driven model, with AI dependency as the primary predictor, anxiety and digital stress as intervening factors, and quality of life as the outcome. Advanced statistical techniques were used to assess both direct and indirect associations among variables.\u003c/p\u003e \u003cp\u003eAlthough the non-experimental design limits causal inferences, it offers a robust empirical framework for testing theoretical relationships and contributes to the literature on the psychological impacts of AI reliance in higher education.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eThe study included 521 participants recruited through snowball sampling, ensuring no participant was enrolled in classes taught by the researchers at King Abdulaziz University. The majority were undergraduate students (n\u0026thinsp;=\u0026thinsp;466, 89.4%), with smaller proportions of postgraduate students (n\u0026thinsp;=\u0026thinsp;10, 1.9%), faculty or lecturers (n\u0026thinsp;=\u0026thinsp;18, 3.5%), and employees or technicians (n\u0026thinsp;=\u0026thinsp;27, 5.2%). Participants\u0026rsquo; ages ranged from 18 to 58 years (M\u0026thinsp;=\u0026thinsp;21.92, SD\u0026thinsp;=\u0026thinsp;3.75), and the sample was predominantly female (n\u0026thinsp;=\u0026thinsp;418, 80.2%), with males comprising 19.8% (n\u0026thinsp;=\u0026thinsp;103). Academic specializations were diverse and are not reported due to the high number of categories. This sampling approach ensured demographic variability while minimizing potential bias related to the researchers\u0026rsquo; instructional influence.\u003c/p\u003e\n\u003ch3\u003eInstruments\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAI-Dependency Scale\u003c/h2\u003e \u003cp\u003eTo assess the extent to which individuals rely on digital tools and AI-driven resources in their daily tasks and decision-making processes. The scale captures the degree of comfort and habitual usage of digital aids, ranging from the initial stages of task planning to active participation in collaborative and social interactions. It evaluates tendencies to consult digital resources before initiating work, to enhance efficiency, or to organize personal ideas and tasks. Furthermore, the instrument examines the degree to which individuals depend on AI-generated or digital content in both independent and group contexts, including reliance for decision-making, discussion contributions, and content modification or adaptation. The scale also addresses the subtle influence of digital tools on judgment, exploring how users may unconsciously allow digital sources to guide decisions or shape their personal contributions. It considers perceptions of social acceptance, reflecting how individuals perceive others\u0026rsquo; recognition of their work based on AI-supported information. By encompassing these behaviors, the AI-Dependency Scale provides a comprehensive evaluation of the psychological and functional reliance on digital technologies, highlighting patterns of habitual integration, dependency, and potential automatization of decisions in the digital era. Participants respond using a five-point Likert scale, ranging from \u0026ldquo;Strongly Disagree\u0026rdquo; to \u0026ldquo;Strongly Agree,\u0026rdquo; enabling nuanced measurement of the intensity and frequency of reliance on digital and AI-assisted tools. This format allows researchers to quantify the degree of dependency, supporting analyses that link AI usage patterns with cognitive, social, and behavioral outcomes.\u003c/p\u003e \u003cp\u003eExploratory factor analysis (EFA) of the 15-item AI dependency scale indicated excellent data adequacy for factor analysis, with a Kaiser-Meyer-Olkin index of 0.94 and a significant Bartlett\u0026rsquo;s test of sphericity (χ\u0026sup2; = 4784.68, df\u0026thinsp;=\u0026thinsp;105, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Parallel analysis and scree plot examination supported a three-factor solution, accounting for 51.9% of the total variance. Factor loadings were predominantly above 0.6, delineating three dimensions: Factor 1 reflecting direct interaction reliance (Q2, Q3, Q4, Q5, Q12), Factor 2 representing practical or applied dependency (Q6\u0026ndash;Q10), and Factor 3 capturing cognitive and affective reliance (Q1, Q11, Q13\u0026ndash;Q15). The second-order confirmatory factor analysis (CFA) showed that these first-order factors were adequately represented under a higher-order general factor (g), with strong standardized loadings (F1\u0026thinsp;=\u0026thinsp;0.819, F2\u0026thinsp;=\u0026thinsp;0.993, F3\u0026thinsp;=\u0026thinsp;0.793) and acceptable fit indices (CFI\u0026thinsp;=\u0026thinsp;0.922, TLI\u0026thinsp;=\u0026thinsp;0.906, RMSEA\u0026thinsp;=\u0026thinsp;0.090, SRMR\u0026thinsp;=\u0026thinsp;0.080), substantially outperforming a single-factor model (RMSEA\u0026thinsp;=\u0026thinsp;0.145). R\u0026sup2; values indicated that most items were well explained by their respective factors, though some items in Factor 3 exhibited lower explained variance, suggesting potential refinement. Reliability analysis yielded high internal consistency, with Omega Total\u0026thinsp;=\u0026thinsp;0.95, Omega Hierarchical\u0026thinsp;=\u0026thinsp;0.80, and subscale Omegas ranging from 0.75 to 0.92, with the general factor accounting for 70% of the common variance.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAI-Related General Anxiety Scale\u003c/h3\u003e\n\u003cp\u003eThe AI-Related General Anxiety Scale is a psychometric tool developed to assess the general level of anxiety individuals experience in relation to the use and interaction with artificial intelligence (AI) technologies. This instrument evaluates multiple dimensions of psychological response, including concerns about the potential impact of AI on professional and social life, difficulties in concentration, mental fatigue associated with AI use, as well as emotional reactions such as tension, irritability, and persistent worry about possible outcomes. Additionally, the scale examines the influence of these concerns on sleep patterns and overall psychological well-being, providing a comprehensive understanding of the interplay between AI engagement and general anxiety. Responses are measured using a five-point Likert scale, ranging from Strongly Disagree to Strongly Agree,\u0026rdquo; allowing for nuanced assessment of the intensity and frequency of anxiety-related experiences. This scaling method enables researchers to quantify subjective perceptions and to identify individuals who may be more susceptible to anxiety in the context of emerging AI technologies. The tool is particularly suitable for research exploring the psychological impacts of rapid digital transformation and technological integration, offering insights into both individual and societal responses to AI adoption.\u003c/p\u003e \u003cp\u003eA first-order general factor model was treated using the robust weighted least squares estimator (WLSMV) appropriate for Likert-type data. Conceptually justified correlated residuals were freely estimated between closely related items (Q1\u0026ndash;Q2, Q2\u0026ndash;Q3, Q4\u0026ndash;Q5, Q6\u0026ndash;Q7) to account for shared item-specific variance without altering the unidimensional structure. The resulting model demonstrated excellent incremental fit, with a Comparative Fit Index (CFI\u0026thinsp;=\u0026thinsp;0.995) and Tucker-Lewis Index (TLI\u0026thinsp;=\u0026thinsp;0.989), while the Standardized Root Mean Square Residual (SRMR\u0026thinsp;=\u0026thinsp;0.049) indicated minimal residual discrepancy; the RMSEA was elevated (0.108), but this was interpreted cautiously given its sensitivity to model complexity and ordinal indicators. All factor loadings on the general anxiety factor were statistically significant and of moderate to high magnitude, ranging from 0.659 (Q1) to 0.854 (Q3), indicating that each item contributed substantially to the latent construct. Examination of the R-squared values revealed that the general factor explained 43\u0026ndash;73% of the variance across items, with the highest variance accounted for in items reflecting concentration difficulties and persistent worry. Reliability analyses using McDonald\u0026rsquo;s omega indicated strong internal consistency for the total scale (ω\u0026thinsp;=\u0026thinsp;0.89), supporting the unidimensionality and stability of the measure.\u003c/p\u003e\n\u003ch3\u003eQuality of Life scale\u003c/h3\u003e\n\u003cp\u003eA self-developed measure of quality of life was constructed. At the same time, the instrument is original to the researcher, the formulation of several items was guided by established theoretical models and widely used instruments, including the World Health Organization Quality of Life Brief version (WHOQOL-BREF), the General Health Questionnaire (GHQ), and prior research on psychological well-being, life satisfaction, perceived stress, and self-efficacy [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The scale comprises 12 items designed to capture a spectrum of psychological and emotional dimensions, integrating both negative indicators reflecting lower quality of life, such as stress, low self-esteem, and difficulty coping, and positive indicators representing adaptive functioning, self-efficacy, and enjoyment of daily activities, with phrasing adapted to the cultural context of the Saudi population. Respondents rate each item on a five-point Likert scale (Always, Often, Sometimes, Rarely, and Never), with negatively phrased items reverse-scored to allow calculation of a total score. Higher scores indicate a higher perceived quality of life, while lower scores reflect diminished well-being and elevated psychological distress or impaired coping. This measure therefore provides a culturally sensitive and psychometrically informed tool for assessing quality of life in relation to psychological stress, coping, and overall well-being.\u003c/p\u003e \u003cp\u003eA first-order general factor model initially exhibited inadequate fit in the confirmatory factor analysis. In accordance with best-practice recommendations for Likert-type data, all items were treated as ordered categorical variables and the model was estimated using the robust weighted least squares estimator (WLSMV) to obtain appropriate parameter estimates and fit statistics for ordinal indicators. The hypothesized measurement structure retained a single latent factor representing overall quality of life, without specifying subordinate dimensions, thereby preserving the theoretical assumption of unidimensionality. To enhance model fit while maintaining the substantive integrity of the construct, a limited number of residual covariances were freely estimated between conceptually proximate items sharing similar wording or content (e.g., Q1\u0026ndash;Q2, Q7\u0026ndash;Q8, Q10\u0026ndash;Q11). These correlated residuals were theoretically justified as capturing shared item-specific variance not explained by the general factor, rather than indicating additional latent constructs. Following these theoretically guided modifications, the model demonstrated good overall fit, as evidenced by strong incremental fit indices (CFI\u0026thinsp;=\u0026thinsp;0.944; TLI\u0026thinsp;=\u0026thinsp;0.923) and absolute fit indices within recommended thresholds (RMSEA\u0026thinsp;=\u0026thinsp;0.046; SRMR\u0026thinsp;=\u0026thinsp;0.010), indicating that the unidimensional model adequately reproduced the observed covariance structure. Examination of the standardized factor loadings revealed that all items loaded significantly on the general quality-of-life factor, with coefficients ranging from moderate to high in magnitude (approximately |0.42| to |0.79|), suggesting that each item made a meaningful contribution to the latent construct. The strongest loadings were observed for items reflecting core aspects of perceived well-being, whereas relatively lower\u0026mdash;yet still substantial\u0026mdash;loadings were found for items capturing more context-specific evaluations.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDigital stress test\u003c/h2\u003e \u003cp\u003eThe situational measure of digital stress was developed based on previous instruments assessing stress and digital overload, as denoted by Xie et al. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], with adaptations to ensure cultural and cognitive relevance for the Saudi context. Item formulations were designed to capture both psychological and emotional dimensions, reflecting individuals\u0026rsquo; internal experiences and affective responses to daily digital stimuli. The instrument aims to quantify the degree of psychological engagement and emotional reactivity resulting from continuous exposure to digital environments. It comprises five primary dimensions, each represented by five situational items, with responses recorded on a 5-point Likert-type scale ranging from 0 (no engagement) to 4 (high engagement), thereby capturing gradations of cognitive and emotional stress.\u003c/p\u003e \u003cp\u003eThe first dimension, availability pressure, assesses the perceived psychological strain associated with expectations to respond immediately to digital messages and notifications. Items simulate scenarios such as receiving an urgent work-related notification after a long day and feeling guilt if one does not respond promptly; receiving a request from a colleague during family time; encountering a time-sensitive notification while reflecting at the end of the day; receiving an urgent work message during personal tasks; and receiving a message from a supervisor while reading, generating internal conflict between leisure and social obligations.\u003c/p\u003e \u003cp\u003eThe second dimension, social acceptance anxiety, evaluates concern over how one\u0026rsquo;s online posts and comments are perceived by others. Items include considering posting a personal photo while fearing judgment; hesitating to contribute to an online discussion due to potential criticism; monitoring reactions to a personal achievement post and experiencing stress if expected engagement is lacking; posting an idea and noticing comments that trigger concern about acceptance or rejection; and intending to share sensitive content while anticipating possible misunderstanding or disagreement.\u003c/p\u003e \u003cp\u003eThe third dimension, fear of missing out, captures feelings of social exclusion and digital deprivation. Situations include observing friends\u0026rsquo; photos from trips not attended, following posts about events missed, discovering that friends participated in activities without invitation, comparing oneself to peers\u0026rsquo; educational or collaborative achievements, and seeing friends enjoying evening experiences in preferred locations, generating feelings of inadequacy and disconnection.\u003c/p\u003e \u003cp\u003eThe fourth dimension, digital overconnectivity, assesses stress resulting from exposure to multiple simultaneous digital stimuli and its impact on attention and cognitive functioning. Items involve receiving a flood of notifications after a busy day, experiencing frequent phone vibrations during meals, handling concurrent emails and chats, attempting to complete a report amid constant digital interruptions, and managing multiple messages during an online meeting, all eliciting progressively greater cognitive strain and stress.\u003c/p\u003e \u003cp\u003eThe fifth dimension, digital vigilance, evaluates compulsive engagement with messages and notifications and their interference with daily activities. Items simulate scenarios such as receiving notifications during a family gathering, placing the phone aside before sleep while remaining mentally alert, reaching for the phone during a lecture in anticipation of urgent messages, feeling compelled to check posts during casual outings, and searching for a misplaced phone, producing heightened anxiety and feelings of disconnection.\u003c/p\u003e \u003cp\u003eAll items were constructed to reflect a gradient of psychological and emotional engagement, ranging from minimal concern or low arousal to high immersion and distress. This design allows for a comprehensive assessment of digital stress as a multidimensional construct, capturing the complex interplay of cognitive, emotional, and behavioral responses in digital contexts.\u003c/p\u003e \u003cp\u003econfirmatory factor analysis supporting a five-factor first-order structure encompassing Availability Pressure, Social Acceptance Anxiety, Fear of Missing Out, Digital Overconnectivity, and Digital Vigilance, all subsumed under a higher-order general digital stress factor. The second-order CFA indicated excellent model fit, with CFI\u0026thinsp;=\u0026thinsp;0.984, TLI\u0026thinsp;=\u0026thinsp;0.983, RMSEA\u0026thinsp;=\u0026thinsp;0.027, and SRMR\u0026thinsp;=\u0026thinsp;0.032, reflecting strong concordance between the observed data and the hypothesized hierarchical structure. Standardized factor loadings for first-order factors ranged from 0.580 to 0.788, and loadings on the second-order factor ranged from 0.871 to 0.918, confirming that each dimension meaningfully contributed to the overarching construct. Item-level R\u0026sup2; values ranged from 0.336 to 0.649, while first-order factors accounted for 0.758 to 0.843 of variance, indicating coherent subscale representation. Omega reliability analysis demonstrated excellent internal consistency, with Omega Total\u0026thinsp;=\u0026thinsp;0.96 and Omega Hierarchical\u0026thinsp;=\u0026thinsp;0.85, indicating that the general factor explained 71% of the common variance (ECV\u0026thinsp;=\u0026thinsp;0.71), while subscale Omegas ranged from 0.57 to 0.66, supporting reliable assessment of the individual dimensions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStandardized Factor Loadings for the five-Factor of Digital stress Model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandardized Loading\u003c/p\u003e \u003cp\u003e(First-Order)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLoading on General Factor (Bifactor model)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u0026sup2; (Item)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOmega Subscale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAvailability Pressure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.336\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial Acceptance Anxiety\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFear of Missing Out\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDigital Overconnectivity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDigital Vigilance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall / Second-Order Factor\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEthical consideration\u003c/h2\u003e \u003cp\u003eThis study was conducted in full compliance with established ethical standards for research involving human participants. Prior to data collection, the research protocol was reviewed and approved by the Research Ethics Committee of the Faculty of Arts and Humanities, King Abdulaziz University (Serial Number: REC-FAH-KAU-2026-003, dated 31 December 2025).\u003c/p\u003e \u003cp\u003eParticipation in the study was entirely voluntary. All participants were provided with clear and comprehensive information regarding the purpose of the study, the nature of their involvement, and their right to decline participation or withdraw from the study at any time without penalty or adverse consequences. Informed consent was obtained from all participants before their inclusion in the study.\u003c/p\u003e \u003cp\u003eTo ensure confidentiality and privacy, no personally identifiable information was collected. Data were anonymized at the point of collection and used exclusively for scientific research purposes. Access to the data was restricted to the principal investigator, and all data were securely stored in password-protected electronic files in accordance with institutional data protection guidelines.\u003c/p\u003e \u003cp\u003eThe study posed minimal risk to participants. No deceptive procedures were employed, and no physical, psychological, or social harm was anticipated as a result of participation. Participants were not exposed to distressing content, and the survey instruments were designed to assess attitudes and experiences related to AI use without inducing discomfort or coercion.\u003c/p\u003e \u003cp\u003e All procedures adhered to the ethical principles of respect for persons, beneficence, and justice, as well as to internationally recognized guidelines for human-subject research. The findings of the study will be reported in an aggregate form to ensure anonymity and will be used solely for scholarly purposes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive statistics\u003c/h2\u003e \u003cp\u003eDescriptive analyses of the study variables revealed meaningful variation in both AI Dependency and Digital Stress among participants. The AI Dependency Scale, encompassing the overall score Ai-Dep and its subscales, Direct Interaction Reliance (DIR), Practical/Applied Dependency (PAD), and Cognitive/Affective Reliance (CAR, showed that participants exhibited moderate to high levels of reliance on digital tools and AI-assisted resources in daily life. The total AI Dependency score averaged 43.8 (SD\u0026thinsp;=\u0026thinsp;11.1), with subscale means of 17.1 for DIR, 14.9 for PAD, and 11.7 for CAR, indicating that participants were particularly engaged in direct interactions with digital tools and moderately dependent on practical or applied functionalities, while cognitive and affective reliance was slightly lower. Examination of variability measures indicated that the median absolute deviation (MAD) ranged from 2 for DIR to 7 for the total Ai-Dep score, reflecting individual differences in dependency patterns. Median scores closely aligned with the means, suggesting relatively symmetric distributions, while the 75th percentile (P75) values\u0026mdash;20 for DIR, 18 for PAD, 14 for CAR, and 51 for Ai-Dep total\u0026mdash;highlighted the upper range of dependency levels. Notably, the prevalence of participants scoring above the P75 ranged from 16.9% for DIR to 24.8% for Ai-Dep, demonstrating that approximately one-fifth to one-quarter of the sample exhibited high AI Dependency.\u003c/p\u003e \u003cp\u003eThe Digital Stress Scale, which comprised subscales of Availability Pressure (AP), Social Acceptance Anxiety (SAA), Fear of Missing Out (FoMO), Digital Overconnectivity (DO), and Digital Vigilance (DV), similarly revealed moderate levels of digital stress across participants. Subscale means ranged from 17.9 (AP) to 18.9 (SAA and DO), with the total Digital Stress score averaging 90.0 (SD\u0026thinsp;=\u0026thinsp;12.5), indicating that participants experienced noticeable psychological and emotional engagement related to digital environments. Median scores were comparable to the means, with MAD values ranging from 3 to 4, reflecting consistency across participants, while P75 values spanned 20 to 102 across subscales and total scores. The prevalence of high scorers above P75 ranged from 21.0% for DV to 24.0% for PAD, AP, and DO, indicating that a substantial portion of the sample experienced elevated stress levels associated with digital overconnectivity, social pressures, and vigilance. Additionally, general well-being measures revealed that Anxiety scores averaged 16.2 (SD\u0026thinsp;=\u0026thinsp;6.48), with a P75 of 21 and 22.5% of participants scoring above this threshold, while Quality of Life (QoL) scores averaged 39.4 (SD\u0026thinsp;=\u0026thinsp;4.41), with a P75 of 42 and 19.4% of participants above this value. Overall, these descriptive findings illustrate both the central tendencies and the upper-bound prevalence of AI Dependency and Digital Stress in the sample, highlighting meaningful variability and suggesting that while most participants reported moderate levels of digital reliance and stress, a notable subset exhibited high engagement and susceptibility to digital-induced psychological strain.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics for Network Variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP75\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePrevalence\u0026thinsp;\u0026gt;\u0026thinsp;P75 (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQoL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAi-Dep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFoMO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eShapiro-Wilk tests were conducted to evaluate the assumption of normality across all numeric study variables, including AI Dependency and Digital Stress subscales, as well as Anxiety and Quality of Life. Results indicated significant deviations from normality for most variables, with W statistics ranging from 0.943 for QoL to 0.992 for the overall AI Dependency score (Ai-Dep), and corresponding p-values well below 0.001 for most measures. Specifically, Anxiety (W\u0026thinsp;=\u0026thinsp;0.956, p \u0026lt; .001) and Quality of Life (QoL; W\u0026thinsp;=\u0026thinsp;0.943, p \u0026lt; .001) exhibited significant deviations from normality. Similarly, the AI Dependency subscales, Direct Interaction Reliance (DIR; W\u0026thinsp;=\u0026thinsp;0.974, p \u0026lt; .001), Practical/Applied Dependency (PAD; W\u0026thinsp;=\u0026thinsp;0.980, p \u0026lt; .001), and Cognitive/Affective Reliance (CAR; W\u0026thinsp;=\u0026thinsp;0.982, p \u0026lt; .001)\u0026mdash;demonstrated notable non-normal distributions, whereas the overall AI Dependency score showed only a modest departure from normality (W\u0026thinsp;=\u0026thinsp;0.992, p \u0026lt; .001). Digital Stress subscales, encompassing Availability Pressure (AP), Social Acceptance Anxiety (SAA), Fear of Missing Out (FoMO), Digital Overconnectivity (DO), and Digital Vigilance (DV), also deviated significantly from normality, with W values ranging from 0.946 to 0.983 (all p \u0026lt; .001), while the total Digital Stress score indicated a slightly smaller departure (W\u0026thinsp;=\u0026thinsp;0.975, p \u0026lt; .001. Despite the statistical significance of these departures, the W statistics were generally high, indicating modest skewness and kurtosis. Consequently, parametric analyses, including correlation and regression procedures, remain interpretable and appropriate for these data, while acknowledging the presence of slight non-normality in the distributions of several subscales.\u003c/p\u003e \u003cp\u003ePearson correlations among AI Dependency subscales, Digital Stress subscales, Anxiety, and Quality of Life (QoL) are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. As anticipated, subscales within each construct demonstrated moderate to strong positive correlations, indicating good internal coherence. Within the AI Dependency Scale, Direct Interaction Reliance (DIR) correlated strongly with Practical/Applied Dependency (PAD; r\u0026thinsp;=\u0026thinsp;0.712) and Cognitive/Affective Reliance (CAR; r\u0026thinsp;=\u0026thinsp;0.649), while the total AI Dependency score (Ai-Dep) was highly correlated with each subscale, DIR (r\u0026thinsp;=\u0026thinsp;0.876), PAD (r\u0026thinsp;=\u0026thinsp;0.928), and CAR (r\u0026thinsp;=\u0026thinsp;0.880), highlighting the hierarchical structure of the construct. Similarly, the Digital Stress subscales, including Availability Pressure (AP), Social Acceptance Anxiety (SAA), Fear of Missing Out (FoMO), Digital Overconnectivity (DO), and Digital Vigilance (DV), showed moderate to strong correlations with the total Digital Stress score (r\u0026thinsp;=\u0026thinsp;0.63\u0026ndash;0.87), supporting the coherence of the higher-order factor. Anxiety was modestly associated with Ai-Dep (r\u0026thinsp;=\u0026thinsp;0.228) and with the Digital Stress subscales, reflecting the psychological link between digital dependency and stress-related affective states. In contrast, QoL demonstrated weaker correlations with both AI Dependency and Digital Stress measures, suggesting that while higher reliance on digital tools and increased digital stress may relate to psychological distress, their immediate association with overall quality of life is more nuanced.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation matrix between study variables underlining the network analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQoL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDIR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCAR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAi-Dep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSAA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFoMO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eDV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eDigital stress\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQoL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAi-Dep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe observed low correlations between overall digital stress and key study variables highlighted the limitations of conventional bivariate analyses in capturing the complex interplay of students\u0026rsquo; psychological responses. This prompted the inclusion of digital stress dimensions in a network analysis, revealing their central role in structuring cognitive, emotional, and behavioral dynamics, and providing a more precise understanding of how digital stress shapes AI dependency, anxiety, and quality of life in the academic environment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eNetwork Estimation\u003c/h2\u003e \u003cp\u003eAn EBICglasso Gaussian Graphical Model was estimated using qgraph, with partial correlations computed via cor_auto. The resulting network depicted AI Dependency (Ai-Dep), Anxiety, Quality of Life (QoL), and the five digital stress dimensions (Availability Pressure [AP], Social Acceptance Anxiety [SAA], Fear of Missing Out [FoMO], Digital Overconnectivity [DO], and Digital Vigilance [DV]) as nodes connected by weighted edges representing partial correlations.\u003c/p\u003e \u003cp\u003eThe network visualization (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) indicated that digital stress components formed a dense subnetwork, with AP, SAA, and FoMO emerging as highly interconnected nodes. AI Dependency was centrally located and connected to both cognitive\u0026ndash;affective and digital stress variables, while QoL was positioned peripherally, indicating its outcome-like role.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCentrality Analyses\u003c/h2\u003e \u003cp\u003eCentrality indices revealed that Digital Overconnectivity (DO, Strength\u0026thinsp;=\u0026thinsp;0.911), Fear of Missing Out (FoMO, Strength\u0026thinsp;=\u0026thinsp;0.879), and Availability Pressure (AP, Strength\u0026thinsp;=\u0026thinsp;0.886) were the most central nodes in terms of connectivity. AI Dependency exhibited moderate strength (0.304) but meaningful expected influence (0.304\u0026ndash;0.304) as a structurally foundational node connecting cognitive reliance with stress responses. QoL and Anxiety had lower centrality values, consistent with their roles as outcome or mediating variables rather than initiators of network activation. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes key centrality metrics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCentrality Measures for Network Variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrength\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCloseness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBetweenness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInExpectedInfluence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOutExpectedInfluence\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQoL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAi-Dep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFoMO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThese results indicate that digital stress dimensions, particularly DO, FoMO, and AP, act as hubs driving network dynamics, whereas AI Dependency serves as a structural bridge linking cognitive reliance to these hubs (see, Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe psychological network analysis revealed that Betweenness indices\u0026mdash;representing the \u0026ldquo;bridging\u0026rdquo; role of nodes, were near zero for most central variables, including AI Dependency (Ai-Dep), Availability Pressure (AP), Social Acceptance Anxiety (SAA), Fear of Missing Out (FoMO), and Digital Overconnectivity (DO). From a network psychology perspective, this does not imply that these variables are unimportant; rather, they function as structural hubs that directly influence the dissemination of effects throughout the network without serving as bridges between distinct clusters of variables. In other words, these variables exert their influence broadly across the network, either directly or indirectly, reflecting their structural centrality in organizing students\u0026rsquo; digital psychological experiences.\u003c/p\u003e \u003cp\u003eConversely, the low Closeness indices for variables such as AI Dependency, Quality of Life (QoL), and Anxiety suggest that these nodes occupy peripheral positions within the network. Psychologically, this can be interpreted as these variables being more reactive than generative\u0026mdash;they tend to reflect the influence of central digital stressors rather than initiating network activity. For instance, AI Dependency does not directly induce anxiety; instead, it establishes a cognitive environment that shapes how students respond to digital pressures, which in turn affects their experienced anxiety and perceived quality of life.\u003c/p\u003e \u003cp\u003eThe near equivalence of In- and Out-Expected Influence indices indicates that the impact of each node on the network and the influence it receives from others are roughly balanced. This reflects a reciprocal and dynamic nature within the digital stress network, wherein each variable can both affect and be affected by other nodes to a similar degree, endowing the system with integrated dynamism.\u003c/p\u003e \u003cp\u003eFrom this analysis, it can be concluded that the most influential variables in structuring students\u0026rsquo; digital psychological experience are DO, FoMO, and AP, which serve as pressure hubs determining network activation and the distribution of psychological impact. In contrast, variables such as Anxiety and QoL occupy peripheral positions and largely reflect the downstream consequences of these dynamics rather than driving them. This interpretation provides a nuanced understanding of how AI Dependency and digital stress shape students\u0026rsquo; psychological experiences and highlights potential targets for interventions aimed at enhancing mental health in technology-saturated educational environments. Overall, the findings indicate that students\u0026rsquo; psychological experiences in digital learning contexts emerge from a dynamic interplay of cognitive and emotional interactions: central digital stressors drive attention and cognitive load, while AI Dependency functions as an internal regulatory factor, redistributing cognitive effort and shifting the locus of control toward technological resources. Consequently, accumulated cognitive\u0026ndash;emotional tensions manifest in elevated anxiety and altered perceptions of quality of life. In essence, negative psychological states do not arise as immediate reactions to technology or digital pressures but are the product of continuous psychological reorganization, wherein students increasingly rely on digital tools to offload evaluative and decision-making demands, reflecting a fundamental shift in perceived competence and self-regulatory control (see Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe psychological network analysis shows a complex structure implied the experiences of university students in AI-saturated learning environments. The findings suggest that psychological effects cannot be reduced to simple linear relationships between usage intensity and stress or anxiety. Rather, the network reflects a dynamic system in which cognitive, emotional, and relational variables interact through conditional partial connections, indicating deeper patterns of psychological organization beyond mere frequency or intensity of AI use.\u003c/p\u003e \u003cp\u003eWithin this framework, AI dependency (Ai-Dep) emerges as a structurally foundational variable. Its significance is not necessarily indicated by high observable centrality metrics but rather by its explanatory role within the overall system. From a network perspective, Ai-Dep functions as a predisposing cognitive condition or a relatively stable psychological tendency that reorganizes the individual\u0026rsquo;s engagement with other digital stressors, rather than acting as a highly active daily interaction node. Psychologically, this dependency reflects a gradual shift in the locus of cognitive control, wherein individuals increasingly delegate evaluation and self-judgment to external outputs implicitly assumed to be more efficient. This pattern does not signify a direct loss of cognitive capacity but rather a redistribution of cognitive effort, potentially creating a perceptual gap between technologically supported performance and unaided self-efficacy.\u003c/p\u003e \u003cp\u003eWithin this network, anxiety occupies a pivotal position as a mediating variable linking cognitive dependency to digital stress. Network properties indicate that anxiety does not function as a continuous affective state but as a conditional variable that is activated when cognitive dependency intersects with high digital performance demands. This form of anxiety extends beyond traditional academic anxiety to reflect a cognitive\u0026ndash;motivational tension associated with diminished perceptions of competence and autonomy when performing tasks without technological support, consistent with Self-Determination Theory\u0026rsquo;s propositions regarding the frustration of autonomy and competence needs.\u003c/p\u003e \u003cp\u003eAvailability pressure (AP) serves a critical contextual role, representing a psychological climate characterized by continuous digital presence and immediate responsiveness. Network analysis indicates that AP acts as an enabling factor for activating other variables, increasing the likelihood of AI tool use to reduce temporal and cognitive burdens, thereby reinforcing the dependency cycle. AP thus represents not merely temporal pressure but a digital\u0026ndash;cultural framework that redefines performance and achievement standards within the academic environment.\u003c/p\u003e \u003cp\u003eSocial Acceptance Anxiety (SAA) functions to translate individual cognitive pressure into relational and identity-level stress. It reflects concerns about losing social standing or relative position in competitive digital academic spaces, where technical proficiency and rapid task completion become implicit criteria for acceptance. Within the network, SAA links self-worth to digital presence and engagement, thereby heightening students\u0026rsquo; propensity to adopt AI-dependent strategies not only to achieve academic outcomes but also to maintain a sense of belonging and status.\u003c/p\u003e \u003cp\u003eFear of Missing Out (FoMO) acts as an accelerative emotional mechanism within the network, amplifying availability pressure and promoting hyper-connectivity. FoMO extends beyond concern over lost information or opportunities to represent ongoing tension associated with desynchronization from academic and social peers. Network analysis shows that FoMO contributes to increased digital interaction density, raising the likelihood of experiencing elevated digital load.\u003c/p\u003e \u003cp\u003eDigital overload (DO) and digital vigilance (DV) represent central convergence points in the network, reflecting cumulative effects of multiple cognitive and emotional stressors. DO embodies cognitive fatigue arising from input exceeding processing capacity, while DV reflects a perceptual pattern characterized by sustained readiness for digital stimuli even in their absence. Importantly, this description is structural and psychological, not clinical, capturing patterns of attention and vigilance in high-density digital environments.\u003c/p\u003e \u003cp\u003eFinally, quality of life (QoL) appears as a distal outcome reflecting the long-term cumulative effects of network dynamics. Its network position suggests that QoL is influenced by the interplay of stress, dependency, and cognitive load rather than acting as an active driver within the current model. While QoL may play a dynamic role in longitudinal contexts, here it functions as a holistic indicator of system-level psychological health.\u003c/p\u003e \u003cp\u003eOverall, these findings indicate that the relationship between AI use and mental health is neither directly causal nor unidirectional. Rather, it reflects a complex psycho-digital structure in which concepts of competence, autonomy, belonging, and performance are reorganized. Network analysis positions AI dependency as a structural condition, with availability pressures and social acceptance anxiety forming the contextual stressors, FoMO acting as an accelerant, and the heaviest psychological outcomes manifesting through digital overload and continuous vigilance. These processes cumulatively contribute to gradual declines in quality of life, positioning AI not as a neutral tool but as a factor reshaping the psychological architecture of human experience in digital academic environments.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings of the present study indicate that reliance on artificial intelligence (AI) cannot be reduced to a neutral technical behavior or merely a supportive educational tool; rather, it constitutes a complex psychological structure in which cognitive, motivational, and emotional processes intersect. This perspective aligns with prior observations that repeated interaction with intelligent systems reshapes self-regulatory patterns and decision-making processes, extending beyond simple functional usage [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. By moving from a logic of \u0026ldquo;use\u0026rdquo; to a logic of \u0026ldquo;dependency,\u0026rdquo; this study positions AI reliance as a relatively stable psychological state with multiple manifestations across networks of anxiety, digital stress, and quality of life. Unlike most previous studies, which considered these variables as independent or adjacent factors [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], network analysis in this study reveals a connected structure in which AI dependency serves as a central node reorganizing students\u0026rsquo; psychological experiences within digital academic environments.\u003c/p\u003e \u003cp\u003eThe psychological network demonstrates that AI dependency is more strongly associated with dimensions of digital stress, such as digital vigilance, fear of missing out, and pressure from constant availability, than with general anxiety or quality of life. This pattern is consistent with prior findings indicating that psychological effects of smart technologies often first emerge as digital pressures and compulsive monitoring behaviors before manifesting as overt anxiety or reductions in well-being [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] Consequently, the psychological impact of AI dependency may operate initially through restructuring the individual\u0026rsquo;s relationship with digital environments rather than through immediate affective symptoms, partially contradicting earlier assumptions of direct links between technology use and anxiety [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This provides a new explanatory dimension that extends beyond previous studies focusing on anxiety or life satisfaction as end-point outcomes, without examining the intermediate mechanisms that translate dependency from cognitive to affective levels.\u003c/p\u003e \u003cp\u003eFrom a cognitive offloading perspective, high AI reliance facilitates the transfer of core cognitive functions, reasoning, evaluation, and decision-making from the individual to the AI system, gradually weakening self-monitoring processes. This interpretation is supported by prior empirical evidence showing that excessive reliance on digital media correlates with reduced internal cognitive effort and memory retention [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Cognitive offloading does not merely reduce effort but also reshapes perceived sources of competence, rendering successful performance implicitly dependent on system availability. Network analysis indicates that this form of offloading does not initially elicit overt anxiety but generates sustained digital vigilance and fear of detachment from intelligent sources, consistent with evidence on the cognitive and emotional costs of digital interruptions.\u003c/p\u003e \u003cp\u003eUnlike studies attributing AI-related anxiety to technological complexity or opacity [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], the present study draws on Self-Determination Theory to propose that anxiety emerges indirectly from the frustration of basic psychological needs for autonomy and competence, rather than from AI use per se [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] Increased reliance on intelligent systems undermines perceived control and diminishes confidence in independent task performance, creating anticipatory anxiety concerning potential failure when AI support is unavailable. This framework advances beyond explanations centered on technology exposure and aligns with research showing that motivational need frustration drives sustained emotional responses in digital contexts [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExamining quality of life within the network revealed that it is not directly affected by AI dependency but emerges as a distal outcome of accumulated anxiety and digital stress. This aligns with cumulative well-being models, suggesting that declines in quality of life typically result from sustained stressors rather than immediate triggers [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Such a framework reconciles previous mixed findings regarding AI\u0026rsquo;s impact on well-being, where some studies reported positive effects [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and others reported adverse outcomes [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The present study suggests that these discrepancies may reflect the omission of mediating pathways through which short-term functional benefits transform into long-term psychological costs when use evolves into dependency.\u003c/p\u003e \u003cp\u003eThis study is among the first to provide empirical evidence from an Arab context that AI dependency interacts with cultural characteristics, academic expectations, and self-monitoring norms, potentially explaining relatively elevated dimensions such as digital social acceptance and fear of evaluation. These findings correspond with cross-cultural literature emphasizing the role of societal context in shaping technology-mediated experiences [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMethodologically, the study advances prior work by employing psychological network analysis to uncover non-linear relationships, identify central variables, and characterize internal dynamics of the psychological system, as recommended in complex psychological research [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. This approach enhances interpretive precision and informs interventions targeting core nodes such as cognitive dependency and digital vigilance rather than focusing exclusively on final outcomes like anxiety or reduced quality of life.\u003c/p\u003e \u003cp\u003eIn sum, the current findings offer a comprehensive explanatory model in which AI dependency, through cognitive offloading and motivational need frustration, reshapes the student\u0026rsquo;s psychological experience in digital academic environments. This restructuring has cumulative effects on anxiety, digital stress, and quality of life. The study not only extends existing knowledge but organizes it within a theoretically and methodologically rigorous framework, highlighting new avenues for research and interventions in the era of AI.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study offers a nuanced understanding of AI dependency as a central psychological construct that reorganizes cognitive, motivational, and emotional processes within the digital learning experiences of university students. By conceptualizing reliance on AI as more than mere usage, the research highlights how cognitive offloading and frustration of autonomy and competence contribute to heightened anxiety, digital stress, and ultimately, declines in quality of life. The study\u0026rsquo;s strengths lie in its use of psychological network analysis, which uncovers non-linear, system-level interactions and identifies key structural nodes, such as AI dependency, availability pressure, and social acceptance anxiety, thereby offering actionable targets for interventions aimed at mitigating maladaptive reliance and fostering digital well-being. Furthermore, the inclusion of an Arab university context expands the cross-cultural generalizability of theoretical models and provides empirical evidence of culturally specific dynamics in AI-mediated academic environments. At the same time, the findings underscore potential risks: unregulated or excessive dependency may erode cognitive autonomy, amplify vigilance and hyper-connectivity, and produce cumulative psychological strain that is not immediately visible. Overall, the study advances both theoretical and practical understanding of AI\u0026rsquo;s psychological impact, offering a framework for designing balanced, culturally informed strategies that maximize the benefits of AI while minimizing its cognitive and emotional costs.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDep\u0026ndash;Artificial Intelligence Dependency\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAvailability Pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQoL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuality of Life\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSAA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSocial Acceptance Anxiety\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFoMO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFear of Missing Out\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDigital Overconnectivity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDigital Vigilance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDIR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDigital Interaction Reliance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePractical Applied Dependency\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCAR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCognitive and Affective Reliance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis section contains all the declarations required for submission, organized according to the journal\u0026apos;s guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in full compliance with the Declaration of Helsinki (https://www.wma.net/policies-post/wma-declaration-of-helsinki/). The research protocol was reviewed and granted prior ethical approval by the Research Ethics Committee of the Faculty of Arts and Humanities, King Abdulaziz University (Serial Number: REC-FAH-KAU-2026-003, dated 31 December 2025). Participation in the study was entirely voluntary. All participants were provided with clear and comprehensive information regarding the purpose of the study, and informed consent to participate was obtained online from all participants before their inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This manuscript does not contain any person\u0026rsquo;s data in any form (including individual details, images, or videos).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. Access to the data may be subject to ethical considerations to ensure participant confidentiality and privacy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was funded by KAU Endowment (WAQF) at King Abdulaziz University, Jeddah, Saudi Arabia. The authors, therefore, acknowledge with thanks WAQF and the Deanship of Scientific Research (DSR) for financial support. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read and agreed to the published version of the manuscript. Conceptualization, F.K.E. and M.A.M.; Methodology, F.K.E., J.F.A., and M.A.M.; Formal Analysis, M.A.M.; Investigation, F.K.E. and J.F.A.; Data Curation, J.F.A.; Writing\u0026mdash;Original Draft Preparation, M.A.M.; Writing\u0026mdash;Review and Editing, F.K.E. and J.F.A.; Supervision, F.K.E.; Project Administration, F.K.E.; Funding Acquisition, F.K.E. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was funded by KAU Endowment (WAQF) at King Abdulaziz University, Jeddah, Saudi Arabia. The authors, therefore, acknowledge with thanks WAQF and the Deanship of Scientific Research (DSR) for financial support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eStanford Human-Centered AI. AI Index Report 2025. Stanford University; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrasanth A, Densy JV, Surendran P, Bindhya T. Role of artificial intelligence and business decision making. \u003cem\u003eInt J Adv Comput Sci Appl\u003c/em\u003e. 2023;14(6):22\u0026ndash;31. 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Psychol Methods. 2018;23(4):617.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Artificial intelligence, AI dependency, cognitive offloading, anxiety, digital stress, quality of life, university students, network analysis","lastPublishedDoi":"10.21203/rs.3.rs-8870746/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8870746/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe integration of artificial intelligence (AI) into higher education has accelerated, yet little is known about the psychological mechanisms underlying students\u0026rsquo; reliance on AI. This study conceptualizes AI dependency as a complex cognitive\u0026ndash;motivational construct that extends beyond mere usage, influencing anxiety, digital stress, and quality of life.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA 521 participants (predominantly undergraduate, 80% female) recruited via snowball sampling at King Abdulaziz University. Self-administered standardized instruments assessed AI dependency, AI-related general anxiety, digital stress, quality of life, and AI dependency. 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.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAI dependency emerged as a structurally foundational variable, reorganizing students\u0026rsquo; cognitive and emotional experiences. It was closely linked to digital stressors, including digital vigilance and fear of missing out, while anxiety functioned as a mediator connecting cognitive reliance to environmental pressures. Social acceptance anxiety translated cognitive pressures into relational\u0026ndash;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.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAI 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\u0026rsquo;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.\u003c/p\u003e","manuscriptTitle":"AI-Dependency and Its Relationships with Anxiety, Quality of Life, and Digital Stress","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-22 15:00:31","doi":"10.21203/rs.3.rs-8870746/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-02T02:22:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"44032915081902253965510314838551654055","date":"2026-03-27T03:32:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260013605545219445520657359520528698251","date":"2026-03-18T10:13:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-18T09:38:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-16T12:05:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-20T10:53:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-18T09:19:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2026-02-18T09:14:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"777ec970-38bb-4d8d-ae12-353342c7992d","owner":[],"postedDate":"March 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-22T15:00:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-22 15:00:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8870746","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8870746","identity":"rs-8870746","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}