The research team, led by Kiran Abdul Sattar of the Institute of Family Medicine at Jinnah Sindh Medical University, surveyed 310 Bachelor of Medicine and Bachelor of Surgery students spanning every academic year. The scale of AI adoption they documented is striking. Ninety-four point one nine percent of participants reported having used ChatGPT, and 90.97 percent said they used artificial intelligence primarily for academic assistance. In a cohort of future physicians, nearly universal engagement with generative AI is no longer a prediction — it is the present reality, and understanding what drives that engagement has become a central question for medical educators worldwide.

To untangle the psychological drivers of AI dependency, the researchers measured four latent constructs using validated instruments: self-efficacy, assessed with six items; academic stress, also six items; performance expectation, captured with four items; and procrastination, measured with the Academic Situational Procrastination Scale. AI dependency itself was quantified with a five-item scale. Rather than treating these variables in isolation, the team employed Partial Least Squares Structural Equation Modeling, or PLS-SEM, a statistical framework that estimates relationships among unobserved latent variables through their measured indicators. The analysis was performed using SmartPLS 3.3.3, allowing the researchers to test how all four psychological factors simultaneously shaped AI dependency.

The structural model told a coherent story, though not the one many might expect. Together, the four constructs explained 24.4 percent of the variance in AI dependency — an R² of 0.244, respectable for a model of human behavior in a single cross-sectional sample. Within that model, procrastination emerged as the dominant force, showing the strongest positive association with AI dependency at a standardized path coefficient of β = 0.427 with a p-value of 0.001. In effect size terms, this corresponded to an f² of 0.239, a medium effect — meaning procrastination was not merely statistically detectable but substantively important in predicting which students came to depend on AI.

The interpretation is intuitively compelling. A student who habitually delays studying, defers assignments, and postpones exam preparation faces a moment when the work simply must be done, and generative AI offers an immediate, frictionless shortcut. The chatbot does not judge, does not require scheduling, and does not demand the sustained effort that independent learning does. In that light, AI dependency may function less as a technological phenomenon and more as a symptom of impaired self-regulation — a digital extension of the same executive-function weaknesses that produce procrastination in the first place. For medical educators, this reframing matters enormously, because interventions targeting time management and self-discipline may do more to promote healthy AI use than any policy restricting the technology itself.

Two other factors showed smaller but statistically significant positive associations with AI dependency. Performance expectation — a student’s belief that using AI will improve their academic results — carried a path coefficient of β = 0.143 (p = 0.013), a small effect. Self-efficacy, the confidence students have in their own capabilities, showed a small positive association as well, at β = 0.128 (p = 0.046). The self-efficacy finding is the most counterintuitive of the study. Classic self-regulation theory might predict that confident students would need AI less; instead, students who believed in their own competence appear to have felt freer to experiment with AI tools, integrating them into their workflow without anxiety about losing control. Confidence, in this reading, enables exploration rather than guarding against dependence.

The null result may prove the most talked-about finding of all. Academic stress showed essentially no relationship with AI dependency, with a path coefficient of β = -0.007 and a p-value of 0.935 — about as close to zero as statistics allows. The popular narrative that overwhelmed, anxious students turn to chatbots as a coping mechanism found no support in this sample. Whatever drives students toward AI, the authors conclude, it appears to be rooted in self-regulatory and motivational factors rather than in distress. That distinction carries practical weight: wellness programs and stress-reduction initiatives, however valuable for student mental health, are unlikely on their own to change how medical students use artificial intelligence.

The stakes of getting this right are unusually high in medical education. Overreliance on AI during the formative years of training threatens precisely the capacities that define a competent physician — critical thinking, clinical reasoning, and the ability to solve problems when no algorithm is available. A medical graduate who has outsourced the struggle of learning may struggle to tolerate the ambiguity of real patients, where no prompt produces a definitive answer. At the same time, AI used well can accelerate learning, personalize study, and free students to focus on higher-order reasoning. The difference between these outcomes lies not in the technology but in the psychological profile of the student using it, which is exactly what this study helps map.

The authors emphasize that their findings point toward a specific intervention strategy: programs focused on self-regulation and time management could support balanced AI integration in medical education while preserving independent learning skills. Teaching students to recognize procrastination triggers, structure their study time, and set boundaries around AI use may address the root cause of dependency rather than its symptoms. Such approaches align with a broader shift in educational psychology, which increasingly treats self-regulated learning as the master skill that determines whether new technologies amplify or erode genuine competence.

As with any cross-sectional study, the usual caveats apply. The data capture a single moment in time at a single institution, and associations — however precisely quantified — cannot establish causation. It remains possible that AI dependency itself fosters procrastination, or that both share an unmeasured underlying cause. Still, with 94 percent of medical students in this sample already using ChatGPT and the model accounting for a meaningful share of the variance in dependency, the study provides a rigorous empirical foundation for a conversation that medical schools everywhere can no longer postpone. The generation now filling anatomy labs will practice medicine alongside artificial intelligence for their entire careers; the urgent task, this research suggests, is not to keep them away from these tools but to ensure that discipline, not delay, governs how they use them.

Subject of Research: Psychological predictors of AI dependency among medical students in Pakistan

Article Title: Association of self-efficacy, stress, performance expectation, and procrastination with AI dependency among medical students at a public sector university in Pakistan

Article References: Abdul Sattar, K., Fatima, S., Ali, S. K., Zafar, H., & Abbas, S. M. (2026). Association of self-efficacy, stress, performance expectation, and procrastination with AI dependency among medical students at a public sector university in Pakistan. BMC Medical Education. https://doi.org/10.1186/s12909-026-10535-w

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10535-w

Keywords: artificial intelligence, medical students, procrastination, self-efficacy, academic stress, performance expectation, AI dependency, medical education, PLS-SEM, ChatGPT, self-regulation, Pakistan