saucis

Sakarya University Journal of Computer and Information Sciences

2636-8129

Sakarya University

10.35377/saucis...1488149

Computer Software

Bilgisayar Yazılımı

Harnessing AI for Leadership Development: Predictive Model for Leadership Assessment

https://orcid.org/0000-0002-4125-8361

Alomairi

Adel

ALTINBAS UNIVERSITY

https://orcid.org/0000-0001-9145-1939

Ibrahim

Abdullahi Abdu

ALTINBAS UNIVERSITY

03 28 2025

8 1 112 122 05 22 2024 03 13 2025

2018

Sakarya University Journal of Computer and Information Sciences

The present paper has been devoted to the study conducted with the purpose of examining the possibility of applying Machine Learning techniques in classifying leadership based on structured survey data. The objective was to create a predictive model that would allow classifying leadership into three groups – Low, Medium, and High – based on behavior scores. The model was expected to offer a reliable tool for improving leadership development programs and recruitment processes by providing a precise and scalable leadership classification, The study illustrates the potential of advanced ML techniques for rethinking the traditional approaches to the assessment of leadership. Due to the use of advanced ensemble modeling, it was possible to ensure the high accuracy of 93.3% in leadership predicting. Such outcomes can generate considerable advantages for organizational development strategies. The use of ensemble machine learning in the domain of organizational behavior studies can be considered as a valuable academic contribution as it has demonstrated the capacity of determining the application of ensemble techniques for enhancing leadership studies. at the same time, it offers a useful instrument to develop more sophisticated and data-driven practices for leadership development.

Machine learning Leadership style classification Ensemble learning Predictive Analytics in HR Quantitative leadership evaluation AI in organizational development

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