The research team surveyed 368 university students in Malaysia, a country where higher education enrollment has expanded rapidly and smartphone penetration is among the highest in Southeast Asia. Participants completed validated questionnaires measuring three constructs: problematic short-form video application use, temporal dissociation, and short-form video interference with study, a composite outcome capturing self-reported consequences such as energy loss, difficulty completing coursework, and general disruption of academic routines. The researchers then analyzed the data using partial least squares structural equation modeling, or PLS-SEM, a statistical technique well suited to testing theoretically derived pathways among latent psychological variables in a single model.

The theoretical scaffolding of the study draws on two influential frameworks. The first is deficient self-regulation, articulated by media scholars Robert LaRose and colleagues, which reframes what is colloquially called internet addiction not as a clinical pathology but as a failure of the cognitive monitoring systems that normally govern media consumption. Under this view, habitual, unregulated use emerges when automatic processes override deliberate intentions, a dynamic that the endless-scroll architecture of short-form video platforms is almost purpose-built to exploit. The second framework is time displacement, a classic media-effects hypothesis dating back to television research in the 1970s and 1990s, which holds that time devoted to a new medium necessarily comes at the expense of other activities, including sleep, socializing, and study.

Temporal dissociation bridges these two traditions. Rooted in the concept of cognitive absorption developed by Ritu Agarwal and Elena Karahanna, it describes a state in which an immersed user loses awareness of the passage of time, effectively becoming disconnected from the temporal anchors of everyday life. Mihaly Csikszentmihalyi’s work on flow experiences anticipated this phenomenon decades ago: deep absorption in an activity compresses subjective time, and highly immersive digital environments can induce that absorption involuntarily. Short-form video platforms compound the effect through their design. Because each clip is brief and requires no commitment, the perceived cost of watching one more video is negligible, while the algorithmic feed removes natural stopping points. The result is a consumption pattern in which minutes of intended use routinely expand into hours of actual use.

The statistical results confirmed the researchers’ central hypotheses. Problematic short-form video use was positively and significantly associated with study interference, meaning students who reported more compulsive, unregulated engagement with these applications also reported greater disruption to their academic work. More importantly, this relationship was partially mediated by temporal dissociation. In structural equation modeling terms, mediation means that part of the effect of problematic use on study interference operates through the intermediate variable of lost time awareness. Students who used these platforms problematically were more likely to experience situational time distortion, and that distortion, in turn, corresponded to higher levels of self-reported academic interference. The word partial is significant: temporal dissociation explains a substantial share of the pathway but not all of it, suggesting that other mechanisms, such as attentional residue, sleep displacement, or emotional exhaustion, likely operate in parallel.

The findings align with a growing international literature on short-form video and time perception. An experimental study published in Behavioral Sciences in 2025 directly tested whether short-video users lose track of time on TikTok and documented measurable time distortion effects. Other recent work has found that social media addicts show impaired sensitivity in suprasecond timing tasks, overestimating durations in ways consistent with the subjective reports of heavy users. Survey-based studies across China, Malaysia, and beyond have linked short-video addiction to academic procrastination, learning burnout, and reduced academic engagement, often through mediating variables such as mindfulness, anxiety, and fatigue. The Malaysian study adds temporal dissociation to this roster of mechanisms, positioning it as a key correlate that policymakers and educators can target.

Why does the mediating role of temporal dissociation matter practically? Because it changes where interventions should aim. If problematic use harmed academic outcomes only through the sheer volume of time consumed, then screen-time limits and usage caps would be sufficient remedies. But if the harm flows substantially through a distorted sense of time, then interventions must also restore temporal awareness. The study’s authors suggest that their insights offer guidance for policymakers, educators, and researchers aiming to mitigate digital distractions and enhance students’ academic self-regulation in digital learning environments. Concretely, this could mean designing platforms with more salient time cues, such as prominent session-length notifications, or teaching students metacognitive strategies for monitoring their own engagement, an approach consistent with research showing that mindfulness training weakens the link between self-control failure and social media use.

The study also carries methodological weight for a survey of its kind. The authors employed established measurement traditions, building on scales developed for problematic internet use and social media addiction, including the widely used Bergen social media addiction framework. Their analytic pipeline followed recommended two-step procedures for structural equation modeling, assessing the measurement model before testing structural paths, and applying contemporary criteria for discriminant validity and predictive model assessment. The authors acknowledge that generative AI tools were used for language polishing during drafting, with full human review and responsibility retained, and they report no competing interests or external funding. Ethical approval procedures were followed, with voluntary participation and informed consent obtained from all participants.

As with any cross-sectional survey, the design cannot establish causality. It remains possible that students who struggle academically retreat into short-form video, rather than the reverse, or that a third factor such as low conscientiousness drives both problematic use and study interference. Self-reported measures of time distortion are also imperfect proxies for objective usage data, which researchers in psychoinformatics have increasingly captured through logged smartphone behavior. Longitudinal and experimental designs, including the time-distortion experiments now emerging in the literature, will be needed to confirm the direction of the pathway. Still, the convergence of survey evidence, experimental timing studies, and design analyses of algorithmic feeds paints a coherent picture: the platforms are engineered for absorption, absorption erodes time awareness, and eroded time awareness exacts an academic price.

For the millions of students worldwide who study with a phone within arm’s reach, the message of this research is both sobering and actionable. The feeling that hours have slipped away during a scrolling session is not a failure of character but a predictable consequence of interacting with systems optimized to dissolve temporal boundaries. Recognizing temporal dissociation as a distinct, measurable phenomenon gives students, educators, and app designers a concrete target. Whether through regulatory pressure for time-awareness features, campus programs that build self-regulation skills, or individual practices as simple as setting external timers before opening a feed, the goal is the same: to keep minutes from quietly becoming hours, and to protect the finite, irreplaceable time that studying requires.

Subject of Research: Temporal dissociation as a mediator between problematic short-form video use and study interference among university students

Article Title: When minutes become hours: temporal dissociation and study interference among problematic short-form video users

Article References: Ghori, A., & Alivi, M. A. (2026). When minutes become hours: temporal dissociation and study interference among problematic short-form video users. SN Social Sciences, 6(9), Article 417. https://doi.org/10.1007/s43545-026-01712-1

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01712-1

Keywords: short-form video, TikTok, temporal dissociation, time displacement, deficient self-regulation, study interference, academic performance, PLS-SEM, Malaysia, university students, problematic media use, cognitive absorption

The I-PACE framework, short for Interaction of Person-Affect-Cognition-Execution, was originally developed by Matthias Brand and colleagues to explain addictive behaviors ranging from internet-use disorders to gambling. The model proposes that a person’s characteristics, emotional states, and cognitive appraisals interact with executive functions to shape whether a behavior remains controlled or spirals into compulsion. In the context of generative AI, the researchers asked how seven psychological variables—self-efficacy, need for cognition, avoidance learning motivation, positive affect, emotion regulation, perceived usefulness, and cognitive absorption—related to dependence. The choice of variables reflects the model’s core logic: distal factors such as personality-like dispositions do not act directly on behavior, but instead work through more proximal mechanisms such as how useful a technology feels and how deeply it captures attention.

The study’s most striking result concerns cognitive absorption, a state of deep, total immersion in an activity in which time seems to dissolve and attention narrows to the task at hand. Among all the variables examined, cognitive absorption emerged as the strongest direct correlate of generative AI dependence. This finding matters because it reframes what absorption actually is in the AI context. Previous research on problematic internet use and short-form video consumption has often treated absorption as a symptom of addiction, a byproduct of the compulsive behavior itself. Sun and Ma’s analysis instead positions cognitive absorption as an antecedent—a precursor that comes before dependence and helps create it. In practical terms, the students most at risk may be those who find the conversational flow of AI tools mesmerizing, not merely those who use them most often.

Beyond the absorption-centered route, the study identified a second, distinctly different pathway to dependence: an effort-minimizing route that runs through perceived usefulness. Students who came to see generative AI as an efficient shortcut for academic work were more likely to develop dependent patterns of use. This pathway is subtler and arguably more insidious than the absorption route, because it does not involve losing oneself in the technology at all. Instead, it involves a cold calculation: the tool saves effort, the effort saved feels valuable, and gradually the student stops attempting tasks without it. The researchers describe these as two distinct routes to the same destination, and their coexistence helps explain why AI dependence looks so different from one student to another—some are drawn in by fascination, others by convenience.

The indirect pathways in the model reveal equally important nuances. Positive affect and emotion regulation were found to influence dependence only indirectly, operating through perceived usefulness. In other words, students who experience more positive emotions and who regulate their emotions well come to view AI as more useful, and it is that perception of usefulness—not the positive mood itself—that feeds dependence. This finding connects with the broaden-and-build theory of positive emotions, which holds that pleasant emotional states expand a person’s thought-action repertoire and build lasting resources. Here, however, those resources appear to be channeled into an increasingly favorable appraisal of AI tools, which can then tip into overreliance.

Perhaps the most counterintuitive results involve need for cognition, self-efficacy, and avoidance learning motivation. All three were related to dependence only indirectly, rather than exerting direct effects. Need for cognition—the tendency to enjoy and seek out effortful thinking—has long been considered protective against problematic technology use, since students who love thinking hard might be expected to resist shortcuts. The study’s structural model suggests that these dispositions still matter, but that their influence is transmitted through the proximal mechanisms of usefulness and absorption rather than acting on dependence directly. Similarly, avoidance learning motivation—studying not to master material but to escape negative outcomes such as failure or criticism—was tied to dependence only through indirect routes, consistent with avoidance motivation theory’s account of how escape-oriented goals shape technology choices.

Methodologically, the researchers used structural equation modeling, the standard technique for testing networks of direct and indirect relationships among latent psychological constructs. The survey sample of 1,029 students spanned both high school and university populations in China, a country where internet penetration and AI adoption have grown at extraordinary pace according to recent national statistics. The study received ethics approval from the Ethics Committee of Yangzhou University Medical College, and for participants under eighteen, the survey was administered with school approval alongside informed notification to students and their parents or guardians. The authors report no competing financial interests, and the work was supported by the National Natural Science Foundation of China and several provincial and municipal research funds.

The theoretical contribution of the study lies in how it refines the I-PACE model itself. By revealing utility-based indirect pathways, the research extends a framework built primarily around gaming, pornography, and social media into the domain of productivity-oriented AI tools, where the technology is valued not for pleasure but for performance. This distinction is crucial: classic behavioral addiction models emphasize hedonic gratification, yet generative AI dependence may often begin with a rational, even admirable desire to work more efficiently. The study also elevates cognitive absorption from symptom to antecedent, implying that interventions targeting absorption—such as encouraging deliberate pauses, reflective self-monitoring, and mindful engagement with AI outputs—might prevent dependence before it forms, rather than treating it after the fact.

The practical implications reach students, educators, and policymakers alike. For teachers, the two-pathway finding suggests that blanket restrictions on AI use may miss the mark: the student who is mesmerized by chatbot conversations and the student who quietly outsources every assignment need different kinds of guidance. For the first, interventions might focus on attention management and awareness of immersive states; for the second, on rebuilding the perceived value of independent effort and addressing the avoidance motivations that make shortcuts attractive. For policymakers drafting national AI-in-education strategies, the results argue for psychological literacy alongside technical literacy, so that students learn not only how to use AI but how their own minds respond to it.

The study also arrives amid a broader wave of research on AI and mental health. Related work has examined AI dependence among teachers, the role of academic stress and self-efficacy in problematic AI usage, links between AI reliance and critical thinking, and the emergence of deskilling in the generative AI era. International bodies including the OECD and UNDP have published reports on effective and humane uses of AI in education, while Stanford’s annual AI Index documents the technology’s accelerating diffusion. Against this backdrop, Sun and Ma’s contribution is a reminder that the question is no longer whether students will use generative AI, but which psychological currents will carry some of them from casual use into dependence—and how education systems can read those currents early enough to respond.

Subject of Research: Psychological mechanisms of student dependence on generative AI analyzed through the I-PACE behavioral addiction framework

Article Title: Are Students Dependent on Generative AI? Analyzing Psychological Mechanisms Through the I-PACE Framework

Article References: Sun, F., & Ma, R. (2026). Are Students Dependent on Generative AI? Analyzing Psychological Mechanisms Through the I-PACE Framework. International Journal of Mental Health and Addiction. https://doi.org/10.1007/s11469-026-01740-1

Image Credits: AI Generated

DOI: 10.1007/s11469-026-01740-1

Keywords: generative AI dependence, I-PACE model, cognitive absorption, perceived usefulness, emotion regulation, need for cognition, self-efficacy, avoidance motivation, students, behavioral addiction, educational technology, China