Trust in AI for emotional support is not universal; it is shaped by who users are, where they come from, and what they value. Yet research in this area lacks validated psychometric instruments for assessing user perceptions in affective AI contexts and large-scale evidence on how trust formation varies across user segments. To address these gaps, we develop and validate a seven-construct psychometric scale, test a Structural Equation Model (SEM) linking system attributes to Trust and Perceived Benefits as mediators of Actual System Use, and conduct a Multi-Group Analysis (MGA) across five sociodemographic dimensions (gender, age, education, socioeconomic status, cross-national region), drawing on 1,343 active users from seven countries. We find that users experience empathy and anthropomorphism as a unified"Humanlikeness"construct, and that Privacy, Personalization, and Humanlikeness drive Trust while Perceived Bias degrades it. Notably, adoption logic diverges across groups: Privacy shapes women's trust more than men's, Anglosphere (UK, USA) users respond more positively to Humanlikeness than Europeans, and educated and higher-income users require Trust to engage, whereas older adults and lower socioeconomic groups bypass it entirely, relying on perceived practical benefits (e.g., 24/7 availability, non-judgmental support). Our findings extend technology acceptance theory and inform the equitable design of emotional support AI.
Natalia Amat-Lefort, M. Yazan, A. C. Curry et al.· 0 citations
We present the insights gathered at the 1st Workshop on Conversational Search for Complex Information Needs (CoSCIN'26) held on 2 April 2026, in conjunction with the Forty-eighth European Conference on Information Retrieval (ECIR). The workshop brought together keynote presenters and industry representatives for lively discussions on recent developments in conversational search and its future directions, complemented by Q&A sessions for paper presentations. The rapid developments in conversational search enabled interesting discussions that went beyond generative dialogue and factoid question-answering tasks, towards addressing complex information needs and improving the user experience by providing personalized, adaptive answers to users based on style. Moreover, the work that was presented supported exploratory, multi-step information needs and addressed challenges such as longform answer generation, personalization, agent orchestration, and societal considerations, which remain largely open areas of research. The participants shared their views from diverse backgrounds in information retrieval, natural language processing, and human-computer interaction, exchanged results and prospective ideas around conversational AI, and discussed its impact on users and the need for adaptivity. Through these discussions, the workshop fostered collaboration and connected researchers specializing in diverse facets of the topic, ultimately advancing the field toward creating more user-centric, intentionally designed, responsible conversational systems. Date: 2 April 2026. Website: https://convsearch-complex-info-needs.github.io/.
Roxana Petcu, M. Yazan, Mohanna Hoveyda et al.· ACM SIGIR Forum· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
