Author(s)

Kadokan Coulibaly^1,2, Pacôme Brou^1*, Adlès Francis Kouassi¹, Pamela Yoboue¹, Olivier Asseu^1,2

¹Equipe de Recherche en Mathématique Algorithmique et complexité (MAC), Ecole Supérieure Africaine des Technologies de l’Information et de la Communication (ESTIC), Laboratoire des Sciences et Technologies de l’Information et de la Communication (LASTIC), Abidjan, Côte d’Ivoire.
²Institut National Polytechnique Houphouët-Boigny (INPHB), UMRI des Sciences et Technique de l’Ingénieur (UMRI STI), Yamoussoukro, Côte d’Ivoire.

School career guidance plays a fundamental role in the educational success and professional integration of learners. In a context marked by the massification of education and the growing heterogeneity of profiles, traditional guidance systems, based essentially on academic performance, are no longer sufficient to guarantee an optimal match between students’ potential and the courses of study they choose. This article addresses this issue by exploring an artificial intelligence-based approach, using a multinomial logistic regression model incorporating ten variables: six academic grades out of 20 and six soft skills out of 5. A simulated base of 100 students was used to train a supervised model capable of recommending one of five typical streams. The results showed an accuracy rate of 87%, with 75% of students showing a behavioral compatibility of over 80% with the predicted pathway. Radar analysis revealed significant differences in creativity and autonomy, underlining the value of a fine-tuned reading of profiles. This article highlights the relevance of an AI-assisted guidance system, capable of cross-referencing multidimensional data to propose more accurate, personalized choices aligned with students’ actual skills.

Academic Guidance, Artificial Intelligence, Predictive Model, Multinomial Logistic Regression, Soft Skills, Academic Results

Coulibaly, K. , Brou, P. , Kouassi, A. , Yoboue, P. and Asseu, O. (2025) Intelligent Guidance System: AI Approach to School Choice Alignment Based on Multidimensional Data. Open Journal of Applied Sciences, 15, 1678-1694. doi: 10.4236/ojapps.2025.156115.