AN AI AND NLP FRAMEWORK FOR EXTRACTING LEADERSHIP COMPETENCIES AND MAPPING PERSONALIZED TRAINING PATHS: A STRATEGIC APPROACH FOR HUMAN RESOURCE DEVELOPMENT

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Published: 2025-08-24
DOI: https://doi.org/10.46281/xpyf6042
Keywords:
Human Capital, Training Optimization, Decision Modeling, Job Profiling, Natural Language Understanding, Leadership Roles, Strategic Planning, Workforce Analytics, Competency Mapping

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Boumedyen Shannaq
Associate Professor, Management of Information Systems Department, University of Buraimi, Al Buraimi Governorate, Sultanate of Oman
https://orcid.org/0000-0001-5867-3986
V. P. Sriram
Associate Professor, Management of Information Systems Department, College of Business, University of Buraimi, Al Buraimi 512, Sultanate of Oman
https://orcid.org/0000-0002-7105-0713
Said Alrawahi
Assistant Professor, HRM Department, College of Business, University of Buraimi, Al Buraimi Governorate, Sultanate of Oman
https://orcid.org/0009-0008-6614-6287
Devarajanayaka Kalenahalli Muniyanayaka
Assistant Professor, College of Business, Business Administration Department, University of Buraimi, Al Buraimi Governorate, Sultanate of Oman
https://orcid.org/0000-0003-0853-4085
Oualid Ali
Head of Computer Sciences Department, College of Arts & Science, Applied Science University, Manama, Bahrain
https://orcid.org/0009-0001-1433-017X

Abstract

The growing demands of Artificial Intelligence (AI) by organizations could enforce a strategic change in the activities of Human Resources (HR). Conventional practices in leadership development do not always align with data-driven guidelines that incorporate job requirements and training directions. This work examines the application of AI, combined with Natural Language Processing (NLP), to unstructured job descriptions to identify essential capabilities and associate them with the best training options for becoming a leader. A framework is proposed in this work that automatically analyses unwritten job descriptions of top-level positions and defines key competencies with AI-based text processing methods. The structure then correlates the competencies with tailor-made training programs by referring to a recommendation system. A graph-based structure is modified to represent and interrelate the competency clusters. At the same time, a multi-criteria decision-making model is applied to evaluate training options based on four criteria: cost, duration, relevance, and impact. Using datasets from related divisions, the system achieved high accuracy in competency extraction, confirming all three proposed assumptions. Results demonstrate a 28% improvement in matching relevance, indicating that it is 28% efficient on matching relevance, 19% efficient on cost efficiency, and 24% better on its planning when compared to the manual methods. Using a weighted scoring mechanism to evaluate training alternatives (e.g., Leadership Workshop scored 4.4/5, Online Financial Course 4.1/5, and Community Outreach 3.5/5), training options were quantitatively scored and ranked according to their relevance, cost, duration, and impact. In addition, the optimized overall strategy of training was the best overall training path strategy that emphasized Strategic Planning & Research, Compliance and Stakeholder Management, and Financial and Operational Management, which provided a measurable benefit over the long-term capability to establish a sense of impact, reduction of risks, and stability. The scalable solution that the proposed AI-powered framework helps to implement is an evidence-based solution that can help develop leadership more efficiently, align talents with organizational requirements, and help recruiters and recruitment leaders to adjust their talent policies to the digital era. 


 JEL Classification Codes:  J24, C61, D80, C69.

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Article Details

Issue

Vol. 10 No. 5 (2025): Continuous Publication

Section

Research Paper/Theoretical Paper/Review Paper/Short Communication Paper
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Author Biographies

Boumedyen Shannaq , Associate Professor, Management of Information Systems Department, University of Buraimi, Al Buraimi Governorate, Sultanate of Oman

Dr. Boumedyen Shannaq is an Associate Professor in Smart Information Systems at the University of Buraimi, specializing in Automation and AI, Machine Learning, and Data Analytics. With over 18 years as a Faculty member, Program Chair, and IS Expert, he advances research in Smart Information Systems, Knowledge Management, and HCI. His work integrates AI-driven solutions to enhance education and workplace productivity. 

V. P. Sriram , Associate Professor, Management of Information Systems Department, College of Business, University of Buraimi, Al Buraimi 512, Sultanate of Oman

Dr. V.P.Sriram is currently working as an Associate Professor in College of Business, University of Buraimi, Sultanate of Oman. He is an International Certified SAP Business One – Functional Consultant and worked as a Team Leader and Senior Functional Consultant in various prime projects in India. Overall He has 18+ yrs of Professional Experience. His breadth of experience includes 14+ years of Academic Teaching Experience in University Level and 4 years of Industry Experience. He has published more 29 research papers in both Scopus and Web of Science indexed International Journals. He has presented research papers in premier institutions in India and Abroad.

Said Alrawahi , Assistant Professor, HRM Department, College of Business, University of Buraimi, Al Buraimi Governorate, Sultanate of Oman

Dr. Said Alrawahi is an Assistant Professor in the Human Resource Management (HRM) Department at the College of Business, University of Buraimi, located in Al Buraimi Governorate, Sultanate of Oman. With a strong academic background and expertise in human resource management, Dr. Alrawahi has dedicated his career to advancing research and teaching in organizational behavior, talent management, employee engagement, and strategic HR practices. His teaching philosophy emphasizes developing students’ practical skills alongside theoretical knowledge, preparing them to meet the challenges of the dynamic global workforce.

Devarajanayaka Kalenahalli Muniyanayaka , Assistant Professor, College of Business, Business Administration Department, University of Buraimi, Al Buraimi Governorate, Sultanate of Oman

Dr. Devarajanayaka Kalenahalli Muniyanayaka is an Assistant Professor in the Business Administration Department at the College of Business, University of Buraimi, Sultanate of Oman. His teaching spans principles of management, strategic management, organizational behavior, and research methods. Dr. Muniyanayaka’s research interests include leadership and organizational performance, human capital development, innovation in SMEs, and sustainability-driven strategy in the Gulf region. He has contributed to peer-reviewed journals and regional conferences, and he actively mentors undergraduate projects and industry-linked case studies. Committed to student-centered learning, he integrates experiential activities and analytics into the classroom to bridge theory and practice.

Oualid Ali , Head of Computer Sciences Department, College of Arts & Science, Applied Science University, Manama, Bahrain

Dr. Oualid Ben Ali is an Associate Professor and Acting Head of the Computer Science Department at Applied Science University, Bahrain. He earned his PhD in Computer Science (2006) and has since held academic and leadership roles across Oman, Bahrain, and the United Arab Emirates (UAE). His portfolio includes quality assurance, program accreditation, curriculum design, and faculty development and training. In teaching and supervision, he emphasizes problem-based learning and industry alignment. Dr. Ben Ali’s scholarly interests span contemporary computer science themes and higher-education quality systems, reflecting his commitment to advancing both technical competence and academic excellence.

How to Cite

Shannaq , B. ., Sriram , V. P., Alrawahi , S., Muniyanayaka , D. K. ., & Ali , O. . (2025). AN AI AND NLP FRAMEWORK FOR EXTRACTING LEADERSHIP COMPETENCIES AND MAPPING PERSONALIZED TRAINING PATHS: A STRATEGIC APPROACH FOR HUMAN RESOURCE DEVELOPMENT. Bangladesh Journal of Multidisciplinary Scientific Research, 10(5), 1-11. https://doi.org/10.46281/xpyf6042
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