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February 5, 2026Ergonomics5 citations

Measuring and understanding emotional attachment in human–AI relationships

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NCNuo ChengRYRuifeng Yu

Key Points

  • The research aims to develop and validate a scale measuring emotional attachment in human-AI relationships.
  • Study 1 conducted exploratory factor analysis to create the AI Attachment Scale with 15 items.
  • Study 2 utilized confirmatory factor analysis to validate the scale and explore its theoretical framework.
  • Participants included 531 in Study 1 and 375 in Study 2.
  • Anthropomorphism emerged as the strongest predictor of AI attachment orientations.
  • Attachment anxiety positively predicted AI attachment (β = 0.44).
  • Attachment avoidance negatively predicted AI attachment (β = -0.53).
  • AI attachment significantly predicted behavioral intentions (β = 0.50).

Abstract

Users increasingly develop emotional connections with AI chatbots that extend beyond utilitarian functions, yet no validated multidimensional scale exists to measure these bonds. This research developed and validated the AI Attachment Scale (AIAS) through two studies: scale development (Study 1) followed by validation and framework testing (Study 2). Study 1 employed exploratory factor analysis (N = 531) to establish a 15-item scale capturing three dimensions: Emotional Support, Separation Distress, and Secure Base. Study 2 used confirmatory factor analysis (N = 375) to validate the scale structure and propose a theoretical framework linking individual differences to AI attachment and behavioural outcomes. Results showed anthropomorphism as the strongest predictor of AI attachment orientations. Attachment anxiety positively predicted AI attachment (β = 0.44), while attachment avoidance negatively predicted it (β = -0.53). AI attachment significantly predicted behavioural intentions (β = 0.50). This research provides a validated measure of human-AI attachment and practical guidance for emotional design in AI chatbots.

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Cite This Study

Cheng et al. (2026) studied this question.

synapsesocial.com/papers/69843405f1d9ada3c1fb1bd8https://doi.org/10.1080/00140139.2026.2622539
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