Development and Validation of the German Version of the Large Language Model Dependency Scale

Development and Validation of the German Version of the Large Language Model Dependency Scale | 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 Development and Validation of the German Version of the Large Language Model Dependency Scale Magnus Liebherr, Ala Yankouskaya, Sameha AlShakhsi, Christian Montag, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8997779/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract As large language models (LLMs) become increasingly embedded in everyday work, education, and decision-making, concerns have emerged regarding users’ reliance on these systems and the psychological mechanisms underlying such reliance. In German-speaking contexts, public discourse on generative AI is strongly shaped by issues of data protection, transparency, autonomy, and regulatory oversight, yet empirically validated instruments to assess dependency on LLMs are lacking. Addressing this gap, the present study translated and validated the German version of the Large Language Model Dependency Scale (LLM-D12-DE). A sample of 402 German-speaking active LLM users completed the German LLM-D12. Confirmatory factor analysis supported the hypothesized two-factor structure comprising Instrumental Dependency and Relationship Dependency. Both dimensions demonstrate excellent internal consistency, strong composite reliability, and satisfactory convergent validity. Scalar measurement invariance across gender was established, allowing meaningful latent mean comparisons. Network analysis revealed two coherent but distinct item clusters corresponding to instrumental reliance and relational attachment, with central items reflecting decision-related reliance and perceived reduction of loneliness. External validation analyses showed that both dependency dimensions were positively associated with AI acceptance attitudes, perceived trustworthiness of LLMs, and internet addiction severity. Relationship Dependency was additionally associated with lower need for cognition. Usage characteristics, particularly higher frequency of LLM use and multimodal interaction, emerged as robust predictors of dependency levels. Overall, the findings demonstrate that the LLM-D12-DE is a psychometrically sound and contextually appropriate instrument for assessing dependency on LLMs in German-speaking populations. The scale enables systematic research on cognitive and relational aspects of human-LLM interaction and provides an empirical basis for educational, clinical, and regulatory discussions on responsible AI use. Large Language Models Dependency Relational Dependency Instrumental Dependency GenAI Full Text Additional Declarations No competing interests reported. Supplementary Files Appendix1.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 14 Apr, 2026 Reviews received at journal 12 Apr, 2026 Reviews received at journal 03 Apr, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviews received at journal 22 Mar, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviews received at journal 13 Mar, 2026 Reviewers agreed at journal 11 Mar, 2026 Reviewers invited by journal 10 Mar, 2026 Editor invited by journal 10 Mar, 2026 Editor assigned by journal 06 Mar, 2026 Submission checks completed at journal 06 Mar, 2026 First submitted to journal 06 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-8997779","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":604333497,"identity":"bdb843f2-8417-4a2e-ada3-7b6aa9da3384","order_by":0,"name":"Magnus 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