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Record W7147293485 · doi:10.2196/86054

A Serious Game for Soft Skills Assessment in Human Resources: A Cross-Sectional Within-Participant Convergent Validity Study (Preprint)

2025· article· en· W7147293485 on OpenAlexvenueno aff
Maxime Boutrouille, Léo Fichet, Jérôme Dinet

Bibliographic record

VenueJMIR Serious Games · 2025
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsnot available
Fundersnot available
KeywordsConvergent validitySoft skillsSerious gameKey (lock)

Abstract

fetched live from OpenAlex

Abstract Background Soft skills are increasingly assessed in human resources, but commonly used methods (eg, interviews and self-report questionnaires) have well-known limitations. Serious games have been proposed as a complementary assessment format because they can standardize administration, embed assessment in interactive scenarios, and capture behavioral traces. However, evidence for their psychometric validity remains limited and heterogeneous. Establishing convergent validity against well-established reference instruments is a key step in supporting their use as assessment tools. Objective This study aimed to evaluate the convergent validity of Yuzu, a serious game that assesses (1) active listening via a gamified questionnaire inspired by the Active-Empathic Listening Scale (AELS), (2) decision-making under uncertainty via a gamified adaptation of the Iowa Gambling Task (IGT), and (3) teamwork style via dialogue choices inspired by the SYMLOG (System for the Multiple Level Observation of Groups) model. Methods We conducted a cross-sectional, within-participant convergent validity study in France with 39 adults (n=23 women; mean age 27.79, SD 7.96 y). Participants completed a single laboratory session on a desktop PC with headphones (Yuzu build v2, developed by Yuzu). Participants completed the 3 Yuzu modules and the corresponding reference instruments, administered separately (the AELS, an online IGT via PsyToolkit, and a simplified SYMLOG questionnaire). Primary outcomes were the associations between Yuzu and reference scores for each construct (active listening total score, IGT exploitation-phase net score, and SYMLOG dimension scores). Convergent validity was examined using Spearman correlations (2-sided α=.05). Agreement was additionally examined using Bland-Altman analyses for active listening and equivalence testing using two one-sided tests (TOST) for IGT net scores. Results At α=.05, active listening showed strong convergence between Yuzu and the AELS total score (Spearman ρ=0.890, 95% CI 0.804-0.948; P <.001), with minimal systematic bias (mean difference of 0.024, 95% CI −0.031 to 0.078). Decision-making scores were statistically equivalent across modalities based on TOST. The mean net score difference was 1.35 (90% CI −1.22 to 3.93), within the equivalence bounds [−5,+5] (TOST lower: P =.001; TOST upper: P =.01). Teamwork dialogue scores did not converge with the SYMLOG dimensions (dominance: ρ=−0.070, 95% CI −0.420 to 0.280; P =.69; positivity: ρ=0.082, 95% CI −0.266 to 0.418; P =.64; task orientation: ρ=0.134, 95% CI −0.260 to 0.494; P =.44), consistent with a ceiling effect toward cooperative choices. Conclusions This study provides convergent validity evidence for 2 complementary assessment modalities embedded in a single serious game, showing that a gamified AELS-inspired module and a gamified IGT adaptation can closely match established reference measures while supporting standardized administration. In contrast, the dialogue-choice teamwork module showed limited sensitivity and no convergence, suggesting that interpersonal profiling in serious games may require more discriminating scenario design and stronger controls for social desirability. Unlike many previous studies that evaluated a single game component, this study provides a module-by-module convergent validity blueprint within a single platform by using matched reference instruments, thereby informing both research and human resources deployment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.056
GPT teacher head0.432
Teacher spread0.376 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
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