ArticleFrontiers in psychiatry2026
Development and psychometric evaluation of the AI emotional engagement scale.
Article in Frontiers in psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: With the increasing integration of artificial intelligence (AI) technologies into daily life, the underlying mechanisms of emotional interactions between users and AI have emerged as a critical research topic in the field of human-computer interaction (HCI). Existing studies have predominantly adopted traditional technology acceptance models or satisfaction scales, which lack targeted applicability in capturing unique features of AI interactions, such as parasocial relationships and emotional dependence. The present study aims to develop a self-report instrument, the AI Emotional Engagement Scale (AEES), and examine its psychometric properties for assessing users' level of AI Emotional Engagement with AI systems. Methods: This study was conducted based on Norman's three-level theory of emotional design. Study 1 (N = 24) extracted core information from interview data using qualitative analysis, and generated an initial item pool through multiple rounds of expert review. Study 2 (N = 103) conducted a pre-test of the initial items to examine indicators including item quality and relevance, optimized the item design, and developed the formal item set. Study 3 (N = 924) verified the factor structure of the AEES via exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), conducted reliability and validity tests of the scale, and finalized the factor structure of the 28-item, three-dimensional scale. Results: Results of EFA indicated that AI Emotional Engagement consists of three dimensions: the visceral level, behavioral level, and reflective level, which was consistent with the theoretical framework, with a cumulative variance contribution rate of 68.32%. Results of CFA demonstrated that the three-factor model had a good fit ( Conclusion: The AEES has favorable psychometric properties, and can serve as a reliable instrument for researchers and practitioners to assess users' AI Emotional Engagement with AI systems.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.