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Perceived Trustworthiness as a Pathway Linking ADHD Traits to Large Language Models Dependency: Evidence from UK and Chinese Samples

  • Ala Yankouskaya*
  • , Mohammad Amin Kuhail
  • , Haibo Yang
  • , Xiaokun Wang
  • , Jiaojiao Chen
  • , Tina Yunsi Ma
  • , Sameha AlShakhsi
  • , Guandong Xu
  • , Raian Ali
  • *Corresponding author for this work
  • Bournemouth University
  • Zayed University
  • Tianjin Normal University
  • University of Science and Technology Beijing
  • The Education University of Hong Kong

Research output: Contribution to journalArticlepeer-review

Abstract

Large language models (LLMs) create contexts in which individual differences may shape reliance on digital systems. Drawing on neurodiversity, this study examined whether perceived trustworthiness of user’s primary LLM mediates associations between attention-deficit/hyperactivity disorder (ADHD) traits and LMM dependencyinstrumental and relationship LLM dependency. Adults in the UK (n = 567) and China (n = 577) reported ADHD traits, perceived LLM trustworthiness of LLM, and both types of dependency. Across samples, higher ADHD traits were associated with lower trustworthiness, whereas greater trustworthiness was associated with greater instrumental and relationship dependency, yielding negative indirect effects. In the UK, direct ADHD-dependency associations were non-significant after controlling for trust, and mediation produces a significant negative total effect for relationship dependency. In China, negative indirect effect coexisted with positive direct associations, creating opposing pathways whose total effects depends on their relative magnitude. Implications for inclusive LLM design without unintendend dependency and supporting neurodiverse users are discussed.

Original languageEnglish
Number of pages25
JournalInternational Journal of Human-Computer Interaction
Early online dateAug 2026
DOIs
Publication statusE-pub ahead of print - Aug 2026

Keywords

  • Adhd
  • LLMs dependency
  • Neurodiversity
  • Perceived trustworthiness
  • inclusive LLMs design

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