With the rise of tools like large language models (LLMs) and AI-powered writing assistants, university students today face unprecedented choices in how they approach their academic tasks. The pressure to perform, coupled with the convenience offered by AI, has led many to develop a dependency on these technologies. The study, conducted by Liu et al., bridges the gap between psychological stressors inherent in academic life and the behavioral adaptations that students exhibit in response to generative AI availability. The researchers tapped into PLS-SEM, a multifaceted statistical technique well-suited for analyzing complex relationships among observed and latent variables, providing a rigorous methodological backbone to the inquiry.

The investigation begins by contextualizing academic stress as a multifaceted construct that includes perceived workload, time pressures, performance anxiety, and social expectations. These stressors cumulatively impact students’ mental health and coping mechanisms. The authors posit that generative AI tools serve as both a coping strategy and potentially an avoidance mechanism, highlighting the dual-edged nature of AI’s integration into academic activities. They suggest that while AI can enhance productivity and learning, unchecked reliance might lead to dependency, thereby affecting students’ cognitive autonomy and critical thinking capabilities.

One of the critical contributions of the study is the deployment of a multiple mediation model to dissect how academic stress influences dependency on generative AI. Unlike simple direct effect models, multiple mediation allows the researchers to unravel indirect pathways through which stress impacts AI dependency. The team explored variables such as anxiety levels, self-efficacy in academic skills, and perceived usefulness of AI tools as mediators. These factors collectively elucidate the psychological processes through which stress translates into behavioral inclination towards AI usage.

PLS-SEM, the analytical tool of choice in the study, is a variance-based structural equation modeling approach that excels in handling small to medium sample sizes and complex model specifications. This method also accommodates measurement error and enables the simultaneous assessment of multiple relationships. Liu and colleagues meticulously validated their scales for constructs like academic stress, anxiety, self-efficacy, and AI dependency using confirmatory factor analysis within the PLS framework, ensuring the robustness of their findings. Their model fit and reliability indices affirmed the suitability of the hypothesized pathways, providing credible empirical support for the theoretical constructs proposed.

The data, collected from a diverse cohort of university students across multiple disciplines, painted a nuanced picture. Academic stress was positively associated with increased anxiety, which in turn diminished students’ confidence in their academic abilities—a phenomenon known as reduced self-efficacy. This diminished self-efficacy then correlated with higher perceived usefulness of generative AI tools, reflecting how students sought external scaffolding to compensate for their self-doubt. Ultimately, a higher perceived usefulness translated into greater dependency on generative AI, underscoring the mediatory role of psychological states in technology reliance.

Beyond these direct relationships, the study’s findings also illustrate a feedback loop where continued dependency on AI may exacerbate academic stress over time, potentially due to concerns over skill degradation or ethical dilemmas linked to AI-assisted work. This cyclical interaction poses important questions about the long-term implications of integrating these technologies into academic processes. The authors call for interventions that balance AI usage with skill development to prevent detrimental effects on students’ learning journeys.

The significance of this research extends into pedagogical and policy domains. As educational institutions increasingly incorporate AI into curricula and resource ecosystems, understanding the psychosocial consequences is paramount. This study serves as a clarion call for educators to foster environments where generative AI is deployed as an augmentative tool rather than a crutch. Strategies that enhance students’ self-efficacy and resilience to academic stress can mediate their reliance on AI, ultimately promoting healthier digital habits.

Furthermore, the ethical tensions addressed indirectly in the study resonate with ongoing debates surrounding academic integrity in the AI era. Dependency on generative AI raises questions about originality, plagiarism, and the essential skill sets that education aims to cultivate. The research by Liu et al. indirectly underscores the necessity for clear guidelines and transparent communication around AI use, ensuring that generative technologies are aligned with educational values and objectives.

The technical rigor of this study, especially its use of PLS-SEM, sets a precedent for future research exploring AI-human interaction within psychological frameworks. By leveraging this sophisticated modeling approach, researchers can unpack complex, multidimensional phenomena that traditional methods might obscure. The authors advocate for continued exploration of cognitive and emotional variables influencing AI engagement, promoting a holistic understanding of the student experience in digitally augmented educational contexts.

In conclusion, this pioneering study delivers critical insights into how academic stress propels university students towards dependency on generative AI technologies, mediated by anxiety, self-efficacy, and perceived tool usefulness. The findings illuminate the delicate interplay between psychological well-being and technological adoption, urging educators, policymakers, and technologists to collaboratively cultivate supportive academic ecosystems. As generative AI continues to evolve, understanding its psychological ramifications will be essential for leveraging its benefits while safeguarding student development and authenticity.

Subject of Research:
The influence of academic stress on university students’ dependency on generative artificial intelligence, analyzed through a multiple mediation model utilizing partial least squares structural equation modeling (PLS-SEM).

Article Title:
Academic stress and university students’ dependency on generative artificial intelligence: a multiple mediation model using PLS-SEM.

Article References:
Liu, X., Liu, Y., Dai, Y. et al. Academic stress and university students’ dependency on generative artificial intelligence: a multiple mediation model using PLS-SEM. BMC Psychol (2026). https://doi.org/10.1186/s40359-026-03986-9

Image Credits: AI Generated

The research is motivated by the accelerating integration of LLMs, particularly GPT-4o, into the workflows of researchers who harness these models to assist with literature reviews and knowledge synthesis. While LLMs demonstrate remarkable proficiency in text generation, this study highlights the concerning phenomenon of “hallucinated” references—citations that are outright fabricated and cannot be traced back to legitimate scientific sources. The scale of this issue is quantified with striking statistical clarity: 19.9% of all AI-generated citations were entirely fictitious, failing to correspond to any existing publication, and a remarkable 45.4% of those that appeared genuine contained substantial bibliographic inaccuracies, such as invalid or incorrect Digital Object Identifiers (DOIs).

These findings surface at a time when academic publishing is seeing a spike in the submission of manuscripts containing AI-generated content, a trend that increasingly tests the boundaries of peer review and editorial scrutiny. The phenomenon of fabricated citations is not a superficial formatting error; it fundamentally disrupts the chain of scientific verification. Such inaccuracies threaten to mislead readers, distort the scientific record, and ultimately undermine the cumulative foundation of knowledge upon which future research depends. The study emphatically argues for the imperative of rigorous human verification across all AI-assisted academic outputs, especially in fields where nuanced expertise is critical to discerning valid references.

An important dimension of the study involves the exploration of how the reliability of GPT-4o’s citations varies according to topic familiarity and prompt specificity. The researchers simulated literature reviews across three mental health topics with differing levels of public and scientific recognition: major depressive disorder, a well-studied and widely recognized condition; binge eating disorder, with moderate familiarity; and body dysmorphic disorder, a relatively obscure topic with limited research coverage. This stratification revealed a clear gradient in fabrication rates, with the least familiar topics suffering the highest incidence of false citations—peaking at nearly 29% for body dysmorphic disorder. In contrast, the well-established field of major depressive disorder recorded a much lower fabrication rate of around 6%.

Moreover, the study delved into the impact of prompt specificity on citation accuracy. When GPT-4o was given highly specialized review prompts, such as focusing exclusively on digital interventions for binge eating disorder, the frequency of fabricated citations increased significantly compared to more general overview prompts. This suggests that the complexity and specificity of the requested information can exacerbate the model’s tendency to “hallucinate” references, compounding the risks posed to academic integrity. Thus, while LLMs can be valuable aides, the nature of the prompts and the subject matter substantially influence the trustworthiness of their bibliographic outputs.

Beyond simply cataloging these errors, the study offers a robust critique of current scholarly reliance on AI tools without adequate safeguards. It underscores that the reliability of AI-generated citations is neither static nor universally dependable but fluctuates depending on the domain knowledge embedded within the training data and the precision of how inquiries are framed. This underscores an acute need for academic institutions, journals, and editorial boards to recognize these shortcomings and institute proactive measures to detect and mitigate the risks of citation fabrication.

Given the persistence of these issues, the authors issue a clarion call for systematic human oversight. They advocate for mandatory verification protocols whereby researchers and students critically appraise every AI-generated citation to confirm its authenticity. Editorial workflows must be enhanced with technological solutions, such as automated detection systems designed to flag references that do not correspond to actual publications or bear suspicious metadata. These measures should be integrated alongside traditional peer review to maintain the scientific rigor and quality standards that underpin credible research.

Training and policy development form another cornerstone of the recommendations. Institutions must equip scholars with the competencies required to engage critically with LLM-generated outputs—teaching them how to devise precise prompts that minimize hallucinations and how to interpret AI assistance with a discerning eye. Clear guidance and ethical frameworks should govern the use of AI in scholarly work, emphasizing transparency and accountability. Without these educational and procedural upgrades, the risk of injecting fabricated or misleading citations into the academic corpus will only grow.

The implications of this study resonate broadly across the scientific communication ecosystem. It presents an urgent narrative that the integration of sophisticated AI tools, although tremendously beneficial in accelerating research workflows, carries latent challenges that, if unaddressed, may degrade the trustworthiness of published knowledge. Researchers utilizing LLMs must, therefore, embrace a cautious and informed approach, viewing these models as supplements rather than replacements for meticulous scholarship.

In conclusion, Linardon and colleagues’ experimental study not only quantifies a troubling phenomenon but also galvanizes the academic community to adopt a vigilant posture when interfacing with AI-generated literature. The nuanced understanding of how topic familiarity and prompt specificity shape citation quality equips stakeholders with critical insights to refine AI usage strategies. This pioneering work marks a significant milestone in acknowledging and confronting the pitfalls of AI hallucination within scientific literature, reinforcing the essential role of human judgment in safeguarding research integrity.

As the landscape of academic publishing continues to evolve under the influence of AI technologies, collaborative efforts between researchers, publishers, and technologists will be crucial in developing robust frameworks and tools to ensure that innovation does not come at the cost of reliability. This study serves as an indispensable wake-up call—and a roadmap—for maintaining the sanctity of citations, the bedrock upon which credible science is founded.

Subject of Research: Not applicable
Article Title: Influence of Topic Familiarity and Prompt Specificity on Citation Fabrication in Mental Health Research Using Large Language Models: Experimental Study
News Publication Date: November 17, 2025
Web References: http://dx.doi.org/10.2196/80371
References:
Linardon J, Jarman H, McClure Z, Anderson C, Liu C, Messer M. Influence of Topic Familiarity and Prompt Specificity on Citation Fabrication in Mental Health Research Using Large Language Models: Experimental Study. JMIR Ment Health 2025;12:e80371
Image Credits: JMIR Publications
Keywords: Academic publishing, Academic ethics, Science communication, Scientific method, Retractions, Medical journals, Scientific journals, Academic journals, Citation analysis