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Measuring and understanding emotional attachment in human–AI relationships

Nuo Cheng & Ruifeng Yu

Ergonomics2026https://doi.org/10.1080/00140139.2026.2622539article
AJG 3
Weight
0.37

What the paper says

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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https://doi.org/https://doi.org/10.1080/00140139.2026.2622539

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@article{nuo2026,
  title        = {{Measuring and understanding emotional attachment in human–AI relationships}},
  author       = {Nuo Cheng & Ruifeng Yu},
  journal      = {Ergonomics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/00140139.2026.2622539},
}

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Measuring and understanding emotional attachment in human–AI relationships

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.