Evidence map›Paper›PMID 41069933›Full record

ArticleFrontiers in artificial intelligence2025

AI competence and sentiment: a mixed-methods study of attitudes and open-ended reflections.

Gatis Lāma, Agnese Lastovska

Abstract read
In one paragraph

Article in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Gatis LāmaFaculty of Social Sciences, Riga Stradins University, Riga, Latvia.
Agnese LastovskaUniversity of Latvia, Riga, Latvia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As artificial intelligence (AI) technologies become increasingly integrated into everyday life, understanding how the public perceives and interacts with AI is essential for fostering responsible and secure adoption. This study investigates the relationship between self-assessed AI competence, trust in AI-generated content, and sentiment toward AI among public and private sector employees in Latvia. Using a mixed-methods approach, the research combines quantitative survey data with open-ended qualitative responses to explore how demographic factors influence AI-related perceptions. Results reveal that although participants rate their AI competence and trust relatively highly, a significant portion of respondents either do not use AI or use it only for simple tasks. Sentiment toward AI is generally positive but often neutral, indicating that public attitudes are still forming. Statistically significant differences in AI competence were found across gender, age, and work sector, while trust in AI varied by education and age. Sentiment remained consistent across groups. Importantly, AI competence was positively correlated with trust, which in turn correlated with sentiment. Thematic analysis identified concerns about risk assessment, ethical implications, and the uncertain role of AI in daily life. The study underscores the need to enhance AI literacy and critical evaluation skills to ensure informed trust and societal resilience. These findings inform future strategies for public education, workforce training, and digital security policy in the context of accelerating AI adoption.

Indexed as

AI competenceAI literacyartificial intelligencesentiment analysisthematic analysis

Identifiers

PMID41069933
PMCPMC12504219

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.