SynthesisFrontiers in psychology2026
Trust and interaction design in AI-enabled systems: a systematic literature review.
Synthesis in Frontiers in psychology, 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.
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Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As artificial intelligence (AI) becomes increasingly integrated into everyday systems, understanding how users perceive and interact with AI is critical for effective design. This systematic literature review analyzes 1,565 peer-reviewed scholarly articles published post-2023 from IEEE Xplore, ACM Digital Library, Scopus, and Web of Science, ultimately including 33 publications that met the inclusion criteria. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) framework, this review examines how user trust in AI-enabled systems is defined and measured, and which interaction design factors influence it. The findings reveal that the literature reflects diverse contributions from authors, institutions, and disciplines, while also employing varied definitions and methodologies, contributing to a fragmented body of work. In addition, this review identifies nine key design factors, including explainability, anthropomorphism, confidence, interface, errors, security, transparency, competence, and perceived control. Overall, greater consensus within the field is needed to support the development of stronger and more cohesive bodies of work for each design factor.
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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.