AI-Based Career Transition Recommendation System Using Controlled Educational Data Augmentation

Career transition into digital professions is a strategic lever for addressing youth unemployment in Sub-Saharan Africa. However, existing training programs lack objective career guidance tools. This study presents two complementary contributions, evaluated using data collected from 131 professionals who underwent a career transition process into a digital profession. Learn-Orient is a career path recommendation system combining a profile recognition filter based on Gower distance normalized by the IQR and a binary logistic regression model, producing probabilistic estimates with a graded confidence level (High, Moderate, Low). EDU-CDA is a data augmentation method suited for small educational datasets with mixed variables and imbalanced classes, combining conditional SMOTE for continuous variables and conditional Bernoulli sampling for binary variables. Validated by a three-way protocol (distributional overlap 82.9–90.8%, TVD < 0.061, TSTR Δ = 0.025), EDU-CDA transforms the logistic regression model, which is typically the most vulnerable to small sample sizes (AUC = 0.810 in real cross-validation)—into the most stable and high-performing one (AUC = 0.995 in cross-validation, F1 = 0.900 on a real test set of 20 observations). Among the 16 pre-training predictors selected, institutional funding (OR = 32.40) proves to be the most discriminating factor for the Developer profile, while the creativity test score (OR = 0.107) strongly characterizes the Designer profile. The system incorporates a filter for detecting atypical profiles, ensuring responsible use in a decision-making context with significant human stakes.

@article{ADANKON2026,
  title     = {AI-Based Career Transition Recommendation System Using Controlled Educational Data Augmentation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Gerlix ADANKON and Pelagie HOUNGUE and Melckior DEGBOE and Corelle GOGAN},
  doi       = {10.14569/IJACSA.2026.0170618},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170618}
}