Evidence map›Paper›PMID 41928345›Full record

ArticleBMC psychology2026

Erudit AI SaaS: an artificial intelligence tool based on RoBERTa for classifying employee engagement.

Claudia García-Navarro, Manuel Pulido-Martos

Abstract readValidation Study
In one paragraph

Article in BMC psychology, 2026. 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

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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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.

Claudia García-NavarroDepartment of Psychology, University of Jaén, Campus Las Lagunillas, Edificio Humanidades y Ciencias de la Educación (C5), Jaén, 23071, Spain. cgn00004@red.ujaen.es.ORCID http://orcid.org/0000-0001-6306-5424
Manuel Pulido-MartosDepartment of Psychology, University of Jaén, Campus Las Lagunillas, Edificio Humanidades y Ciencias de la Educación (C5), Jaén, 23071, Spain.ORCID http://orcid.org/0000-0002-9274-3682

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEngagement has been defined since its inception by several currents, mainly two different ones such as those of Kahn (1990) and Schaufeli (2002). Based on these two main currents, self-report instruments for its measurement have been increasing over time. However, in addition to the limitations of the instrument itself (low reliability, low use, etc.), these instruments also have limitations inherent to self-report measures in general (social desirability, correction time, etc.). To solve this problem, and taking into account that the way of communicating through corporate tools in companies (slack, Gmail, Microsoft teams, etc.,) has increased considerably, this study proposes the validation of an artificial intelligence tool (Erudit AI SaaS) that allows to know the level of engagement of employees by analyzing the text they write in their corporate tools.

methodsTo test whether the tool is really assessing the level of engagement we compared the self-report instruments UWES-9 and JRA with the Erudit AI SaaS. Thirty-five employees were selected as participants for the study and the instruments were administered on five different occasions. To determine convergent and divergent validity, bivariate correlations were performed, in the case of reliability, this was calculated using intraclass correlation indexes.

resultsThe results showed good results for convergent validity between the Erudit AI SaaS and the self-report instruments UWES-9 and JRA. Divergent validity also yielded good results between the Erudit AI SaaS and MBI. The reliability, however, was moderate.

conclusionsThese findings provide preliminary evidence supporting the construct validity of Erudit AI SaaS for estimating employee engagement through textual data extracted from corporate communication platforms. However, while initial validity and reliability are encouraging, further research with larger samples and more robust statistical analyses is needed to generalize the results and strengthen the tool’s psychometric foundation.

Indexed as

Artificial IntelligencePsychometricsWork EngagementHumansReproducibility of ResultsSelf ReportArtificial intelligenceBERTEngagementNLPValidity

Identifiers

PMID41928345
PMCPMC13169601

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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.