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The transition from education to employment remains a major challenge, particularly for individuals who may require accessibility support during competency assessment and occupational guidance. However, many current approaches remain fragmented because they evaluate soft skills, accessibility conditions, and occupational requirements as separate dimensions. This study presents an accessibility-aware computational proof of concept for employability analytics using workplace simulation logic, derived competency indicators, semantic modeling, clustering, person–job fit estimation, and heuristic multi-objective optimization. The framework integrates open secondary employability data, O*NET-derived occupational descriptors, and simulated accessibility scenarios within a reproducible analytical pipeline. The results show differentiated computational employability profiles, with mean person–job fit values of 0.85, 0.74, and 0.63 for high, medium, and low profiles, respectively. The derived competency indicators showed high internal consistency ($\alpha =0.905$), although they are interpreted as exploratory proxy dimensions rather than as an exploratory psychometric scale. Principal component analysis indicated a dominant general employability factor, with the first component explaining 75.3% of the variance. The optimization layer produced interpretable heuristic convergence patterns and modeled scenario assignments under predefined validity, accessibility, alignment, and diagnostic criteria. Person–job fit was interpreted under sensitivity scenarios involving alternative competency weights, scalarization parameters, and accessibility assumptions. The study does not include observed participants with disabilities, measured accessibility support use, field simulator interaction records, or longitudinal employment outcomes. Therefore, the term accessibility-aware refers to the computational framework’s design orientation. At the same time, the empirical evidence should be interpreted as a secondary-data-based proof of concept rather than as validation of an inclusive simulator for future users with accessibility needs. The main numerical indicators were: high-profile mean fit = 0.85, medium-profile mean fit = 0.74, low-profile mean fit = 0.63, Cronbach’s alpha = 0.905, first principal component variance = 75.3%, and heuristic iterations = 900. Full article

Aligning information technology (IT) workforce demands with educational assessments is essential for bridging skills gaps; yet, no prior corpus maps IT task reasoning to Programme for International Student Assessment (PISA) proficiency levels. This paper introduces a large language model (LLM)-powered framework aligning IT competencies with PISA 2022 and the OECD (Organisation for Economic Co-operation and Development) Learning Compass 2030, drawing on O*NET v30.2 (Occupational Information Network), ESCO (European Skills, Competences, Qualifications, and Occupations) v1.2.1, PISA descriptors and OECD definitions. The framework operates in two stages: Stage 1 aligns 562 IT task statements with minimum PISA 2022 proficiency levels via LLM annotation and cross-model validation; and Stage 2 extends this mapping to the OECD Learning Compass 2030 through the semantic clustering of task embeddings and a bidirectional gap analysis of 95 ESCO transversal skills. Using Gemini 2.5 Flash, 562 tasks are annotated with minimum PISA levels across Mathematical, Reading, and Science literacy (first stage). Annotation reliability is assessed through a five-model cross-validation against a blind human domain expert (treated as a reference benchmark, not a gold standard) on a stratified 100-task sample (17.8% of the corpus), with agreement ranging from fair (Gemini 2.5 Flash, κ = 0.29) to moderate (Claude Haiku 4.5, κ = 0.50; LLaMA 3.3 70B, κ = 0.44). A bias-correction sensitivity analysis confirms that distributional findings remain stable after accounting for the primary annotator’s systematic overestimation, and OLS-calibrated alignment against O*NET ability ratings provides directional plausibility support. Validated tasks are embedded and clustered into 25 technical profiles via K-Means, each classified against OECD dimensions. The framework is extended to 95 ESCO transversal skills in 24 clusters. Bidirectional analysis reveals that, while every PISA proficiency level is engaged by at least one transversal cluster, 33% of these clusters, covering creative, ethical, social–emotional, and dispositional competencies, fall entirely outside PISA’s cognitive scope. This boundary mapping identifies where the PISA-based alignment is valid and where complementary tools are required for a full readiness assessment. Full article