Learning desk

MultiAgent EDU StackGather good sources. Teach what matters.
T5TauricResearch/TradingAgentsT5A Man Who Invented Modern AI (Before Everyone Else) – Jürgen Schmidhuber [video]T5GPT-4 finished training four years ago todayT5AI Settles a 25 Year-Old Problem We Left BehindT5What it was like working on LLMs and security at Meta (2022-2026)T5Ask HN: How do you go from writing code to deploying with agents?T5What Happened: OpenAI and HuggingFaceT5Apple says Mac users in China can connect to Alibaba's Qwen AI serviceT5Show HN: Try Benzi – A coding harness/agent beating Claude Code itself on SonnetT5The AI Apocalypse Is HereT3Auto mode is now the default in Claude Code for Pro, Max, and Team plansT5Show HN: Tura – Build agent that uses 80% less token and delivers better resultsT5TauricResearch/TradingAgentsT5A Man Who Invented Modern AI (Before Everyone Else) – Jürgen Schmidhuber [video]T5GPT-4 finished training four years ago todayT5AI Settles a 25 Year-Old Problem We Left BehindT5What it was like working on LLMs and security at Meta (2022-2026)T5Ask HN: How do you go from writing code to deploying with agents?T5What Happened: OpenAI and HuggingFaceT5Apple says Mac users in China can connect to Alibaba's Qwen AI serviceT5Show HN: Try Benzi – A coding harness/agent beating Claude Code itself on SonnetT5The AI Apocalypse Is HereT3Auto mode is now the default in Claude Code for Pro, Max, and Team plansT5Show HN: Tura – Build agent that uses 80% less token and delivers better results
← Dispatches

Development and validation of the German version of the large language model dependency scale (LLM-D12)

Primary research

#1117

T1new
Topic
unassigned (set during synthesis)
First seen
2026-08-01 07:16:00
Last seen
2026-08-01 07:16:00

Source raw items (1)

  • Semantic Scholar2026-08-01 07:15:27
    Development and validation of the German version of the large language model dependency scale (LLM-D12)

    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 N  = 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.