DOI: 10.12688/f1000research.186798.1 ISSN: 2046-1402

Protocol for evaluating the efficacy of GoaleTest, an AI-based vocational guidance system using RIASEC and Nonlinear Vector Alignment

Miguel Ángel Martí-Escolano, Avilene Rodríguez Lara
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Abstract* Background Vocational guidance is a critical intervention in educational transitions, yet traditional approaches rely on static assessments with limited personalization. Artificial intelligence (AI)-based systems offer the potential to integrate psychometric profiles, academic data, and contextual variables to generate individualized recommendations. This protocol describes the evaluation of GoaleTest, an AI-driven vocational guidance system that matches students’ psychological profiles based on Holland’s RIASEC model with undergraduate program options using a proprietary nonlinear vector alignment algorithm that incorporates signal amplification, antisignal penalization, and nonlinear affinity mapping. Methods A quasi-experimental longitudinal design will be employed. Participants will be 200–400 final-year secondary education students allocated to either an experimental group receiving GoaleTest-based guidance or a control group receiving traditional guidance, using cluster assignment at classroom or school level to minimize contamination. At baseline, all participants will complete the RIASEC questionnaire embedded in the GoaleTest platform, which generates a career affinity score for each available undergraduate program. Primary outcomes are Career Satisfaction (CS), assessed at 6 and 12 months via a purpose-designed 10-item self-report scale, and Academic Retention Index (ARI), operationalized as continued enrollment confirmed through institutional records at 6 and 12 months. Covariates include socioeconomic status, prior academic performance, and learning environment. Internal consistency of the RIASEC instrument will be evaluated using Cronbach’s alpha. Criterion-related, predictive, convergent, discriminant, and ecological validity will be assessed using correlation analyses, group comparisons (t-tests/ANOVAs), logistic regression, and sensitivity analyses. Statistical analyses will be conducted using R version 4.4.1 and IBM SPSS Statistics version 30. Expected Outcomes It is hypothesized that higher GoaleTest affinity scores will be significantly associated with higher career satisfaction (H1), that students with affinity scores above the upper tertile will show significantly lower dropout rates during the first academic year compared to lower-affinity students and the control group (H2), and that integrating longitudinal academic performance data will improve the algorithm’s predictive accuracy through adaptive recalibration of career-specific weightings (H3). This study aims to provide empirical evidence on the validity and educational utility of AI-based vocational guidance systems and to inform the development of scalable, transparent orientation tools for higher education.

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