




































BANGLADESH JOURNAL OF MULTIDISCIPLINARY SCIENTIFIC RESEARCH 10(5) (2025), 1-11 

1 

        MULTIDISCIPLINARY SCIENTIFIC RESEARCH 
          BJMSR VOL 10 NO 5 (2025) P-ISSN 2687-850X E-ISSN 2687-8518 

         Available online at https://www.cribfb.com 

     Journal homepage: https://www.cribfb.com/journal/index.php/BJMSR 

                                                                                                                                                                                                    Published by CRIBFB, USA 
                                                                                                                                     

AN AI AND NLP FRAMEWORK FOR EXTRACTING LEADERSHIP 

COMPETENCIES AND MAPPING PERSONALIZED TRAINING 

PATHS: A STRATEGIC APPROACH FOR HUMAN RESOURCE 

DEVELOPMENT                                                                      
 

 Boumedyen Shannaq (a)1      V. P. Sriram (b)   Said Alrawahi (c)   Devarajanayaka Kalenahalli Muniyanayaka (d)  

 Oualid  Ali  (e)   
 

(a) Associate Professor, Management of Information Systems Department, University of Buraimi, Al Buraimi Governorate, Sultanate of Oman; E-

mail: boumedyen@uob.edu.om 
(b) Associate Professor, Management of Information Systems Department, College of Business, University of Buraimi, Al Buraimi 512, Sultanate 

of Oman; E-mail: sriram.v@uob.edu.om 
(c) Assistant Professor, HRM Department, College of Business, University of Buraimi, Al Buraimi Governorate, Sultanate of Oman, E-mail:  

said.hk@uob.edu.om 
(d) Assistant Professor, College of Business, Business Administration Department,  University of Buraimi, Al Buraimi Governorate, Sultanate of 

Oman, E-mail: devarajanayaka@uob.edu.om 
(e)Head of Computer Sciences Department, College of Arts & Science, Applied Science University, Manama, Bahrain, E-mail: 

oualid.ali@asu.edu.bh 

 

 

 
A R T I C L E I N F O 

 
 

Article History: 

 

Received: 4th March 2025 

Reviewed & Revised: 4th March 2025 

to 10th August 2025 

Accepted: 20th August 2025 
Published: 24th August 2025 

 

 
Keywords: 
 

Human Capital, Training Optimization, 

Decision Modeling, Job Profiling, Natural 

Language Understanding, Leadership Roles, 

Strategic Planning, Workforce Analytics, 

Competency Mapping 

 

 
JEL Classification Codes: 

 

      J24, C61, D80, C69 

 

 
      Peer-Review Model:  

 

      External peer review was done through  

      double-blind method. 

        

 
A B S T R A C T      

 

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.   

 
 

© 2025 by the authors. Licensee CRIBFB, USA. This open-access article is distributed under the 
terms and conditions of the Creative Commons Attribution (CC BY) license 
(http://creativecommons.org/licenses/by/4.0).  

            

       

INTRODUCTION 

Artificial intelligence (AI) has evolved in changing the human resources strategies in various industries, especially the 

leadership and workforce planning (Benabou et al., 2024; Hamdan, 2025a). With increasing complexity in industries, there 

is an urgent need for organizations to ensure that their leaders are ready for the emerging and measurable competencies 

needed in digital transformation, complex stakeholder management, and innovation demands (Sedkaoui & Benaichouba, 

                                                      
1Corresponding author: ORCID ID: 0000-0001-5867-3986 
© 2025 by the authors. Hosting by CRIBFB. Peer review is the responsibility of CRIBFB, USA.  

https://doi.org/10.46281/xpyf6042 

 
To cite this article: 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 

mailto:oualid.ali@asu.edu.bh
http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://www.openaccess.nl/en
https://doi.org/10.46281/xpyf6042
https://orcid.org/0000-0001-5867-3986
https://orcid.org/0000-0002-7105-0713
https://orcid.org/0009-0008-6614-6287
https://orcid.org/0000-0003-0853-4085
https://orcid.org/0009-0001-1433-017X


Shannaq et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(5) (2025), 1-11

 

2 

2024). However, the traditional approaches to determining leadership requirements and developing training paths are 

manual, time-consuming, and not consistent. 

      The systematic issue associated with this study is a serious one: the absence of scalable, evidence-based transfer 

authorities for automatically deriving competencies and aligning them reliably with appropriate training solutions through 

actual job data. Although efforts have been made to address HR analytics using AI and Natural Language Processing (NLP), 

little has been done to design competency mapping and customized training programs to automate leadership roles (Alenezi 

& Akour, 2025; Hamza & Almi, 2025). This gap is counterproductive because it prevents institutions from developing data-

driven, agile, and adaptive leaders. 

Its strategy is based on actual employment advertisements posted at universities, government organizations, and 

executive employment agencies, which enables it to present high contextual relevance. Process automation supports human 

resource organizations in developing and enhancing talent planning, succession planning, and learning systems in the 

organization.  

This work aims to propose and confirm a framework for an AI-based structure to infer and extract essential 

leadership competencies from unstructured job descriptions, utilizing methods of natural language processing. The offered 

system combines the approach of mapping based on graphs with a multi-criteria decision model that takes into account cost, 

duration, impact, and relevance. 

      This work is structured as follows: Section 2 reviews the previous studies. Section 3 describes the proposed 

methodology. In Section 4, the results of the experiment are discussed. In Section 5, the paper is concluded with significant 

findings, shortcomings, and future research guidelines. 

 

LITERATURE REVIEW 

The integration of AI and HRM significantly improved leadership growth, employment tasks, and increased the productivity 

of all HR activities with a minimum risk (Benabou et al., 2024; Sindhuja & Dunstan Rajkumar, 2025; Madhumithaa et al., 

2025). Due to a lack of innovators and the growing transformation in the competence requisites of the industries, the use of 

intelligent systems in the HR domain has already turned into a necessity (Sedkaoui & Benaichouba, 2024; Jahan, 2023; 

Sharif, Rahman, & Mallik, 2022). The current literature review focuses on the substantial positive progress in HR solutions 

within the scope of AI that took place between 2020 and 2025, in line with the present study's aim: development of AI-

based competency extraction and training paths to occupy leadership roles. Note that through the incorporation of AI tools 

in academic HR, performance will be enhanced. User training and usability of the system mediate part of the outcomes, but 

there is no significant indication of the effect of organizational culture. To implement AI effectively, it requires the elements 

of user-designed and training. 

  Li et al. (2023) proposed LLM4Jobs, using an unsupervised model that utilizes large language models (LLMs) to 

classify job roles, aiming to achieve better results in identifying competencies in resumes and job postings. The same is true, 

where SkillGPT offers uniform skill extraction and ensures accuracy in recruitment processes. These tests demonstrate the 

effectiveness of NLP in processing unstructured data. The ease of use and usefulness of AI can play a significant role in 

enhancing employability by improving soft skills. A SEM-PLS experiment conducted on 429 users indicated that between 

56.1% and 76.8% of the influence of AI on job readiness is mediated by soft skills, positioning AI as a means to enhance 

employment results through skill acquisition in computer-aided training frameworks. Demonstrated that technological 

literacy and cultural fit are known to significantly improve student engagement by increasing the efficacy of face-to-face 

communication, which can be a crucial factor in the study of educational leadership and policy in Oman. 

  In the article by Bevilacqua et al. (2025), the authors analyze the application of AI-based profiling in the field of 

executive education, stating that strategic training is better when personalization is dynamic. According to internal research 

by SAP, AI-optimized learning platforms are less time-consuming in terms of onboarding and increase satisfaction. A study 

published by Chen in 2025 suggested an AI-LDP framework, which integrates AI tools with leadership theories to enhance 

adaptive, personalized leadership training. Some of the identified advantages of the study include the possibility of providing 

real-time feedback, as well as addressing ethical and data privacy-related challenges. According to Vargas Portillo (2025), 

the author has explored the importance of AI in enhancing leadership and management capabilities, indicating that AI 

integration facilitates better decision-making and talent planning. The paper emphasizes the importance of striking a balance 

between AI-driven instruments and human leadership principles, while facilitating comprehensive, data-driven approaches 

to development. 

  NLP is still at the heart of automating the process of parsing resumes and aligning candidate profiles with the needs 

of vacant positions. The new competencies of HR leaders include being data-fluent and AI-aware. One of the purposes is to 

identify the mechanism of how the brain processes conversation using deep NLP models and intracranial neural recordings 

(Cai et al., 2025). They established specific yet overlapping neural patterns in speech production and comprehension, 

demonstrating context-sensitive, time-valid transformations in brain activity when people engaged in a natural human 

conversation. Arfah (2025) reveals some of the radical effects of AI on HRM, including automation and analytics-driven 

efficiencies. Nevertheless, issues of algorithmic bias, transparency, and legality remain. This research also focuses on 

implementing AI with ethical guidance to achieve fairness, responsibility, and a balanced system of human-AI decision-

making in job hunting, training, and strategic HR practices. 

  Praba et al. (2024) note that HRM is being driven towards predictive analytics because of industrial automation. 

Bias is eliminated through the use of hiring bots, such as Mya, which removes elements that indicate bias, including 

appearance and gender; however, this approach should be used ethically. The study conducted by Tong et al. (2021) revealed 

that, although AI feedback has a positive effect on performance, its implementation also influences employee perception 

when transparency is present. According to Atluri and Reddy (2025), Oracle HCM transforms the way talent is acquired 



Shannaq et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(5) (2025), 1-11

 

3 

through AI-based solutions that aim to screen resumes, facilitate predictive recruiting, and bridge biases. They improve the 

quality of recruitment, minimizing time-to-fill and helping HR departments become strategic business enablers in an 

environment driven by data-driven, data-dependent hiring capabilities supported by cognitive automation, performance-

based, and adaptive hiring. Chakraborty and Sharada (2025) explore the potential of AI to revolutionize talent acquisition 

by automating resume checks, candidate matching, and candidate engagement. Based on this, they also assert that the 

combination of AI efficiency with human emotional intelligence should be a synergistic presence, contributing to the 

improvement of decision-making and the availability of a more inclusive, empathetic, and successful candidate experience 

in data-driven recruitment processes. 

  According to Khan et al. (2025), AI should achieve a balance between automation and institutional preparedness, 

as well as ethics, particularly in higher education. Algorithmic accountability and fairness are requirements of the ethical 

dimensions of HRM (Hamdan, 2025a). Alenezi and Akour (2025) highlighted the challenges of applying generic AI models 

to educational HR positions without domain-specific adaptation. 

The majority of previous studies have involved concept tables, automated hiring systems, and generalized online 

training platforms, which are typically based on surveys or business cases. The alignment between the identified 

competencies and the strategic training routes is part of the areas with few studies carried out to automate the process. 

Although previous research, summarized in Table 1, is devoted to theoretical approaches or the overall benefits of HR 

functions in corporations, there are few projects dealing with the automated alignment of training within learning 

institutions. Very little has been said about the use of AI in leadership-specific competency modeling. Moreover, the ethical 

factors and performance validation are understudied in actual practice applications to specific fields, such as higher 

education. 

 

Table 1. Summary of Linked Literature Review  

 
Author(s) Focus Area Key Findings Identified Gaps Contribution of This Work 

Benabou et al. 

(2024) 

Madhumithaa 

et al. (2025) 

AI in HRM AI improves the quality of 

employment and 

competency mapping  

The absence of methodically 

outlined growth paths in the 

leadership domain  

Provides an AI-based model to be used in 

automating the training paths of leadership promises 

Sedkaoui and 

Benaichouba 

(2024) 

HR digital 

transformation 

Transformation stresses 

the importance of 

intelligent systems due to 

changing competency 

requirements.   

Not many real-world applications, 

especially in academia  

Propose a proven AI model based on real data 

concerning university job postings 

Li et al. (2025) NLP and skill 

extraction 

NLP and the use of LLMs 

can help extract 

competencies out of 

unstructured data  

Targets general employment 

opportunities, though not 

applicable to academic 

environments or leadership 
positions  

Deals  with unstructured data in the form of positions 

in university leadership 

Bevilacqua et 

al. (2025) 

AI-based 
executive 

training 

The dynamic 
personalization advances 

strategic learning  

The theoretical profiling does not 
employ a real-time and 

widespread institutional practice  

 offers a practical system that tests its accuracy on 
real institutional positions at leadership levels 

Chen (2025) AI-LDP 

framework 

Suggests customized and 

flexible approaches to 

leadership development,  

 It had not been implemented 

either empirically or technically 

It is purely a conceptual model. Technically 

implements a model using Python and a graphical 

presentation of the data it generates. 

Vargas Portillo 

(2025) 

AI in 

leadership and 

planning 

Demonstrates the balance 

of AI tools and human 

demands of leadership 

required   

No implementation or path of the 

training mapping 

Provides a way of mapping the courses of interest 

based on real competencies to those recommended 

by AI 

Atluri and 

Reddy (2025) 

AI in talent 

acquisition 

Oracle HCM improves the 

process of recruiting due 
to potential AI tools  

Deals with participants of the 

private job market and excludes 
academic leadership training  

Appropriates the same principles to academic 

leadership training based on data provided by public 
universities 

Chakraborty 

and Sharada 

(2025) 

Human-AI 

collaboration in 

hiring 

AI enhances efficiency, 

yet human empathy still 

plays a critical role  

No integration of emotional or 

strategic context in the tone of 

training development  

Strategy, cost, and relevance are taken into account 

during the development of leadership training. 

Strategy, cost, and relevant thought are very 

important factors in the development of training; 

however, the development of automated training 

does not consider emotional and strategic context 

Cai et al. (2025) NLP and neural 

conversation 

modeling 

The NLP discloses 

dynamic patterns of brain 

activity during the 
communication process.  

It is not directly connected to HR, 

but facilitates the contextual 

processing abilities of AI.  

Contextual NLP contextualizes the training by the 

strategic and operational plans. 

Arfah (2025) AI in HR 

operations and 

ethics 

 AI Enhancing 

performance with AI 

requires transparency and 

ethical management. 

No focus on academic training 

systems 

Suggests ethical AI application and functionality in 

the development of university leadership 

Praba et al. 

(2024)  

Tong et al. 

(2021) 

Predictive 

analytics and 

hiring bots 

Prediction tools eliminate 

bias and maximize 

decision-making  

Intentional absences focus on 

training or academically specific 

skills  

Tailors the models of AI to the university. The 

flexibility of the models makes them relevant and 

attainable in terms of results 

Khan et al. 

(2025) 

Hamdan 

(2025b)  

Alenezi and 

Akour (2025) 

AI ethics in 

higher 

education 
HRM 

HRM Inevitable that 

ethical frameworks gain 

domain-specificity  

Existing models are not suited for 

academic leadership without 

adaptation 

 

 



Shannaq et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(5) (2025), 1-11

 

4 

The present study proposes a fully automated AI-based system that utilizes actual university job description data 

from the Gulf region. It is the only method that employs both NLP and MCDM to retrieve leadership competencies and 

match them to individual learning plans, utilizing graph simulation. That fills the gap between the theoretical framework 

and the practical application of AI in the strategic development of HR concepts in academia. This work proposes to create 

and test a computerized program based on AI to derive core capabilities of the jobs listed in positions of leadership and 

match them to strategic learning roadmaps through NLP and multi-criteria decision-making techniques. This study, 

therefore, considers the following assumptions (A1-A3). 

 

A1: The AI-driven NLP systems will be able to retrieve the leadership competencies from the unstructured job description 

effectively.  

A2: A multi-criteria decision model is suitable for matching competencies that have been extracted with appropriate training 

programs.  

A3: Strategic leadership development through AI is significantly more efficient and aligned than its manual counterparts. 

 

MATERIALS AND METHODS 

The system contains two primary modules that enhance HR development strategy performance: 

This module utilizes OpenAI NLP functionality to extract fundamental competencies, including leadership, strategic 

planning, data analysis, and communication skills, from unstructured job descriptions. The system analyzes Top 

management job descriptions to recognize specific competencies, which include "academic leadership," "budget 

management," and "collaborative decision-making." 

         The Training Path Mapping Module selects appropriate training programs from a structured database that contains 

internal courses and external learning platforms, such as Coursera, to provide professional development certificates. 

The research team obtained job descriptions for academic and administrative positions at a respected university in 

the Gulf region. Analysis focuses on strategic positions extending up to the Chancellor, Dean, Director, and Head of 

Department level to guarantee appropriate relevance in the evaluation. 

The Algorithm, implemented in Python programming and executed on Google Colab, utilized the language model 

provided by OpenAI. The extraction of competencies used Natural Language Processing (NLP) methods, including named 

entity recognition (NER), as well as keyword frequency analysis and semantic similarity. The system analyzed two main 

competencies, which appeared across diverse leadership positions as “strategic vision” and “policy formulation.” 

A collection of extracted competencies gets evaluated against structured training programs within a repository. The 

system links the competency of “strategic planning” to a certified leadership development course, whereas “data-driven 

decision-making” corresponds to a business analytics workshop. 

The system evaluation process assesses the accuracy of competency extraction through HR expert validation, as 

well as the relevance of training suggestions through expert scoring. It measures user satisfaction through surveys with HR 

professionals. The proposed methodology is presented in Figure 1. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 1. The proposed Research Methodology 

 

The proposed Algorithm is presented in Figure 2, and Table 2 illustrates the steps involved in creating a directed 

graph, using the Score (Tᵢ) for the Vice Chancellor Training (VCT) as a case example. 

 

Research problem Literature Review System Design 

Data Collection 

Competency 

Extraction Process 

 

Training Path 

Mapping Process 
Evaluation Metrics 

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Shannaq et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(5) (2025), 1-11

 

5 

Table 2. Tasks for the development of the directed graph for VCT 

 
Task Command 

Import the related Python library import matplotlib.pyplot as plt 

import networkx as nx 

Create a directed graph. G = nx.DiGraph() 

Add a central node G.add_node ("Vice Chancellor Training") 

Add training categories and items for the category, items in training_paths.items(): 

G.add_edge("Vice Chancellor Training", category for item in items: 

G.add_edge(category, item) 

Draw graph plt.figure(figsize=(16, 12)) 
pos = nx.spring_layout(G, k=0.45, iterations=50) 

nx.draw(G, pos, with_labels=True, node_color="skyblue", node_size=2200, 

font_size=10, font_weight='bold', edge_color="gray", arrows=False) 

Display graph plt.show() 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 2. The proposed Algorithm 

 

 To develop a mathematical model that combines: 

 The table (training path evaluation), and 

 The mind map diagram (for example: Vice Chancellor Training Framework), 

This assumption can be treated as a decision support model to rank training paths that enhance productivity and reduce risk. 

The proposed model training path selection is a Multi-Criteria Decision-Making (MCDM) problem. 

Let each training category ( C_i ) be composed of a set of training programs ( P_{ij} ) (where j indexes the individual 

programs in category i).  

We define weights for evaluation criteria: 

Relevance (w1 = 0.3) ,  Cost (w2 = 0.2) , Time (w3 = 0.2), Impact (w4 = 0.3) 

The total score \( S_{ij} \) for each training program is calculated as: 

Display final output 
Competencies and 

training paths 

Textual 

output shown 

in notebook 

or console 

Uses print () 

statements to 
display them in 

the console 

5 

Extract key competencies 

 

Job description 

text 

Bullet-point 
list of 

competencies 

extract_competen
cies (text) sends a 

prompt to OpenAI 

asking for a 
bullet-point list of 

competencies 

Map competencies to 

training paths 

List of 

competencies 
List of 

training paths 

Map competencies 

to training paths. 
pathsmap_training

_path 

(competencies_tex
t) sends a prompt 

to OpenAI asking 

for training 
suggestions. 

3 

4 

extract_text_from

_docx (file_path) 

reads and joins all 
non-empty 

paragraphs 

Full job 

description 

text 
Word file (.docx) Read the job description 

document 
2 

Initialize OpenAI client API key 
OpenAI 

client ready 

Uses OpenAI 

(api_key=...) 

Step Action Input Function Output 

1 



Shannaq et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(5) (2025), 1-11

 

6 

S_{ij} = w1 * R_{ij} + w2 * C_{ij} + w3 * T_{ij} + w4 * I_{ij}            (1)  

 

Where: 

( R_{ij}, C_{ij}, T_{ij}, I_{ij} \) Are the respective scores (1-5) for relevance, cost, time, and impact? 

 

In the following section, it visualizes the suggested training routes for leadership positions through associative 

graphs. The strategy facilitates more straightforward interpretation and enables informed decision-making to tailor 

leadership development. 

 

RESULTS 

The experimental framework was tested using an encrypted dataset containing formatted files of job descriptions in a 

university setting. Table 3 presents a sample structure of job description tokens for each file, which were populated with 

detailed tokens for the following elements: Job Title, Position Code, Department/Unit, Reporting Line, Job Overview, 

Responsibilities and Duties, and Qualifications and Competencies. The raw data cannot be published to maintain 

confidentiality and institutional compliance. 

 

Table 3. Sample Structure of Job Description Tokens 

 
VC Job Token 

Field Example Token 

Job Title Director of Academic Affairs 

Position Code DA-203 

Department/Unit Office of the Vice Chancellor for Academic Affairs 

Reporting Line Reports to: Vice Chancellor 

Job Overview Oversees curriculum development and ensures compliance with academic policies. 

Responsibilities Develop academic strategy, monitor program quality, and manage accreditation. 

Qualifications PhD in Education Management, 10+ years in academic leadership. 

Competencies Strategic thinking, decision-making, communication, leadership, and data literacy. 

Name 
 

Signature 
 

 

The dataset was organized into four main occupational groups, with each group comprising numerous positions. 

To identify the competencies, the NLP algorithm was used to analyze the semantics of job responsibilities, the competencies 

required for each job, and the strong alignment of each job with the company's strategies. Then, competencies were 

overlapped with training programs based on a custom-built dictionary founded on current trends and third-party training 

providers. Table 4 shows a sample training path dictionary structure used to match the appropriate job description tokens. 

 

Table 4. Sample Training Path Dictionary Structure 

 
Training Category Associated Programs/Activities 

Strategic Leadership - Leadership development programs  
- Seminars on leadership trends  
- Mentorship from leaders 

Operational Management - Workshops on higher ed operations  
- Job rotation/shadowing 

Strategic Planning and Implementation - Strategic planning workshops  
- Involvement in planning projects 

Financial Management - Advanced finance courses  
- Workshops on financial sustainability 

Academic Excellence and Research - Academic conferences  
- Ongoing research  
- Workshops on academic excellence 

Community Engagement - Community engagement workshops  
- Community outreach programs  
- Meetings with local leaders 

External Stakeholder Management - Stakeholder management workshops  
- Networking with stakeholders 

Team Leadership and Management - Team-focused leadership programs  
- Workshops on diverse team management 

Compliance with Legislation and Regulations - Workshops on education legislation  
- Engage with legal experts 

Communication and Interpersonal Skills - Communication skills workshops  
- Practice public speaking 

Commitment to Diversity and Inclusion - Diversity training programs  
- Events promoting inclusion 

 

The mapping process involved a multi-criteria decision model that ranked the training options based on the 

importance of training relevance, cost, duration, and impact. This enabled the system to produce visual outputs in the form 

of graphs, providing clear links between the extracted competencies and the optimal training routes. 



Shannaq et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(5) (2025), 1-11

 

7 

    To verify the system's functionality, several explanatory graphs were generated. These illustrate the AI model's 

understanding of job requirements and the provision of strategic training suggestions. The findings validate the framework 

for performing competency extraction automation and aiding in the professional judgment of where training funds should 

be allocated to support strategic HR development objectives. 

    The proposed assumptions in this work were all confirmed by the experiment, as shown in Table 5. They were 

developed to utilize an AI-powered NLP and multi-criteria decision-making (MCDM) framework. 

 

Table 5. Assumptions Results 

 
Assumption  Results 

A1: The AI-driven NLP systems will be able to retrieve the leadership competencies from the unstructured job description effectively.  Accepted 

A2: A multi-criteria decision model is suitable for matching competencies that have been extracted with appropriate training programs. Accepted 

A3: Strategic leadership development through AI is significantly more efficient and aligned than its manual counterparts. Accepted 

 

A1 was proven correct, as it successfully parsed an unstructured job description and accurately extracted related leadership 

competencies using Python's natural language processing capabilities. Based on a series of deterministic tokenization, 

keyword exclusion, and semantic clustering, the system was able to extract domain-specific competencies (e.g., strategic 

vision, financial planning) from heterogeneous job description datasets. This rational correspondence of the input text and 

capabilities extraction proves the practical correctness of the AI-NLP module. 

 

A2: was justified through the application of a weighted scoring mechanism with the MCDM approach formulated as: 

Score (Tᵢ) = ∑ (Wⱼ × Rᵢⱼ) 

Where Wⱼ indicates the weight (e.g., relevance = 0.3) and Rᵢⱼ indicates the rating of training i on criterion j. This allowed 

training programs to be ranked in terms of relevance, cost, time, and impact, and provided mapping to extracted 

competencies. 

 

A3: was demonstrated by a directed graph model that presented visual output to associate competencies with training 

pathways, thereby maximizing strategic alignment. The AI system was clearer, reusable, and more adaptable than traditional 

mapping, reducing ambiguity and sustaining ROI-oriented leadership development planning. 

 

This way, the results of the experiments logically and functionally prove all three assumptions. Figure 3 illustrates 

the visualizations of associative graphs for a Vice Chancellor (VC) position, providing a graphical representation of how 

the identified competencies relate to the desired training courses. Such higher representations make complicated decision-

making easier for HR professionals. This strategy aims to enhance productivity, minimize risk, and maximize returns on 

investment (ROI) in digital-era talent development approaches by aligning competencies with training investments. 

 

 
 

 

Figure 3. Sample of the training path for the VC 

 



Shannaq et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(5) (2025), 1-11

 

8 

The Total Score Formula could be formulated as follows:  

 

Score (Tᵢ) = w_R·Rᵢ + w_C·Cᵢ + w_D·Dᵢ + w_I·Iᵢ                      (2) 

 

Where: Tᵢ: training path I, Rᵢ: relevance score of Tᵢ, Cᵢ: cost-efficiency score, Dᵢ: time/duration score, Iᵢ: impact score. 

And Weights: w_R = 0.3 (Relevance), w_C = 0.2 (Cost), w_D = 0.2 (Duration), w_I = 0.3 (Impact)  

 

 Each criterion is given a weight (in parentheses), e.g., Relevance = 0.3, meaning it is 30% of the total score. 

 Each training path is rated from 1 to 5 for each criterion. These are raw scores. 

 Each raw score is then multiplied by its weight to calculate a weighted score (shown in parentheses next to the raw 

score). 

 The Total Score is the sum of all weighted scores for that training path. 

 

Link to Diagram (Vice Chancellor Training Areas) 

Each node (e.g., “Strategic Leadership”) represents a training domain, with sub-nodes (e.g., “Mentorship from successful 

leaders”) as program types. 

These domains align with outcomes like: 

 Productivity (e.g., Strategic Planning, Operational Management) 

 Risk Reduction (e.g., Compliance, Stakeholder Management) 

 

This study highlights that utilizing AI-powered decision-making enhances the effectiveness of leadership 

development. In Table 6, a sample is given to demonstrate that different training paths are ranked by computing the 

relevance, cost, time, and impact of each one. Among the workshops, the Leadership Workshop is the best choice with a 

score of 4.4, which takes both high relevance and effects into account. Alternatively, the Online Financial Course has a 

rating of 4.1, as it is both cost-effective and time-efficient. However, Community Outreach performs less well because it is 

less critical and more costly. In addition, Table 7 provides a sample that demonstrates certain domains, such as Strategic 

Planning and Research, and Compliance and Stakeholder Management, to help businesses boost their productivity and 

mitigate risks. Institutions can design a specific connection between their training programs and their key priorities using 

the model. Besides supporting resource allocation, the intelligent ranking tool serves as a basis for flexible training 

approaches that align with the aims and levels of leaders within the organization. It introduces a new model to automatically 

develop learning paths for individuals based on their job roles. Training evaluation tables and mind map diagrams are 

combined into a new model that helps leaders choose the most suitable development programs. 

 

Table 6. Sample of Ranking Process 

 
Training Path Relevance (0.3) Cost (0.2) Time (0.2) Impact (0.3) Total Score 

Leadership Workshop 5 (1.5) 4 (0.8) 3 (0.6) 5 (1.5) 4.4 

Online Financial Course 4 (1.2) 5 (1.0) 5 (1.0) 3 (0.9) 4.1 

Community Outreach 3 (0.9) 3 (0.6) 4 (0.8) 4 (1.2) 3.5 

 

Table 6 presents an example of ranking three training paths using four weighted factors: relevance (0.3), cost (0.2), 

time (0.2), and impact (0.3). The workshop that scored the highest overall (4.4) was the Leadership Workshop, which 

demonstrated great relevance and impact. The other course was the Online Financial Course, positioned in second place 

(4.1), which gained an Advantage due to its time and cost-effectiveness. The least scoring unit was Community Outreach 

(3.5), which received moderate scores in all criteria. The scores are multiplied by the weighted value of each particular score 

to obtain a weighted score, which is then summed to give the total score. This table illustrates the objective criteria by which 

training can be selected using a multi-criteria decision-making approach. 

 

The Best Overall Path Strategy is shown in Table 7. The purpose of this path is to increase productivity and reduce risk.  

 

Table 7. Best Overall Path Strategy  

 
Priority Rank Domain Focus 

1 Strategic Planning & Research Long-term impact & knowledge leadership 

2 Compliance & Stakeholder Management Risk mitigation and community alignment 

3 Financial & Operational Management Stability, budgeting, and execution efficiency 

 

Table 7 outlines the best overall training path strategy, prioritizing domains that enhance productivity and reduce 

risk. Strategic Planning and research rank first in terms of their long-term impact. Compliance and stakeholder management 

follow, with a focus on risk mitigation. Financial and Operational Management is the third emphasis, focusing on stability, 

budgeting, and efficient execution within organizational leadership roles. This work proposes a scenario to develop a simple 

model for the implementation of the proposed framework. As an example, the Training Path of Leadership Workshop is 

indicated (Example 1) along with the respective values in Table 6. 

 



Shannaq et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(5) (2025), 1-11

 

9 

Association Examples 

Example 1: 

 

 

 

 

 

 

 

 

 

 

 

 Options and Score (Example 1) : 

o Seminars on leadership trends → R=5, C=4 ,D=3 , I=5  

o Mentorship from successful leaders → R=5,C=3 ,D=3 , I=4   

Apply Model: 

 Seminars Score = 0.3(5)+0.2(4)+0.2(3)+0.3(5)=1.5+0.8+0.6+1.5=4.4 

 Mentorship Score = 0.3(5)+0.2(3)+0.2(3)+0.3(4)=1.5+0.6+0.6+1.2=3.9 

 Choose: Seminars on leadership trends → Higher impact and relevance. 

 

DISCUSSIONS 

All three assumptions find empirical confirmation in this study. The A1 suggested that the AI-based SA system of NLP can 

help retrieve leadership competencies with reasonable success from unstructured job descriptions, as proven by creating and 

running a test of a Python-based NLP model. This finding is consistent with the latest breakthroughs in linguistic models 

and job descriptions, stating that it is possible to structure unstructured textual data into a meaningful unit to extract 

competencies. The adequacy of the multi-criteria decision-making (MCDM) model in determining the suitability of 

extracted competencies for targeted training programs (A2) was also confirmed. The weighting model, founded on 

relevance, cost, time, and impact, provided logical training advice, which has proven that MCDM is a suitable methodology 

for making strategic HR decisions. Comparative implementation supported A3, which posits that the development of 

strategic leadership using AI systems is more efficient and aligns with manual processes. With the help of the AI-based 

model, personalized training pathways became possible, and score-based prioritization became visible, demonstrating a 

higher rate of distinctiveness, clarity, and alignment with the strategic targets of the entire institution. 

In comparison to other available works, the approach proposed in the research is more than a theoretical concept; 

it provides a practice-oriented implementation of AI in leadership development. Formative experiences with AI are 

celebrated alongside the tools of leadership insight in the study literature (e.g., Chen, 2025; Vargas Portillo, 2025). However, 

our work represents a conceptually straightforward yet experimental methodology for operationalizing the integration of AI 

and MCDM using real job descriptions as tokens. Such a movement of a concept to practice is a significant addition, and it 

has practical importance for HR departments. Although the role of AI in leadership development has been examined by 

numerous studies, such as those by Chen (2025), Vargas Portillo (2025), or Bevilacqua et al. (2025), these studies mostly 

employ conceptual frameworks, case-based discussions, or industrial observations. Conversely, the present research 

provides an automated framework that retrieves leadership skills directly from unstructured job descriptions and outputs 

customized training routes based on natural language processing (NLP) and multi-criteria decision-making (MCDM). 

     In comparison to previous studies that typically employ survey-based or perceptional data, the current research 

aims to rely on secondary data in the form of actual institutional job descriptions obtained from a particular university in the 

Gulf region. This makes the approach more practical and realistic. Moreover, most previous studies have focused on 

extracting skills (task-level classifications) or talent acquisition (e.g., LLM4Jobs, SkillGPT, Oracle HCM). In contrast, the 

proposed research aims to develop leadership-specific competency modeling and automate the alignment of training with 

the model. This aspect has not been covered in the existing literature, whether in academia or industry. 

     Notably, the use of graph-based simulation over the training of path visualization in the sphere of academic 

leadership development has not been covered by a single piece of previous work examined in the body of literature in 

question. This creates a new element of interpretability among decision-makers, augmenting the strategic return on 

investment (ROI) of leadership training.  

In such a way, this study fills both a methodological and practical gap, as it is a replicable system that does not 

hinge on survey perceptions but implements readable and actionable results by retrieving real institutional materials 

objectively and AI-mapped to generate meaningful results. 

    The ranking in performance data gives additional support. The Leadership Workshop, with a score of 4.4, proved 

to be the most effective strategic investment due to its high relevance and impact. On the other hand, none of the key items 

were rated lower compared to the Online Financial Course (4.1); yet, its cost-efficiency and brief duration of course 

completion are seen as alternative directions to be taken with limited budget opportunities. With 3.5, Community Outreach 

use highlighted the necessity of optimizing the criteria instead of having an exceptionally high score, which beats all others. 

This numerical product highlights the model's usefulness in resource distribution and the evaluation of training impact. 

     These contributions notwithstanding, we would still appreciate limitations. Information security limits the full 

Task: 

Increasing Productivity in Leadership Roles 

 

 
Objective:  

Boost strategic execution 

among vice chancellors. 

 

Relevant Domain:  

Strategic Leadership 

 

1 

3 2 



Shannaq et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(5) (2025), 1-11

 

10 

disclosure of institutional job descriptions, which can hinder external validation. Additionally, the lack of formal statistical 

tests prohibits generalizability; however, the listing of data mapping, scoring logic, and visualization graphs enhances 

internal validity. In addition, the subjectivity model used for assigning weights may introduce bias; however, this is 

compensated for through the transparency and flexibility that the model provides. 

   This method, when practiced, would aid in data-informed, strategic HR planning, particularly in academic 

institutions. The model shifts HR development away from the reactive nature of the development process to a proactive, 

evidence-based approach by facilitating role-based and goal-based program prioritization. The results are immeasurable in 

industries such as education, public administration, and mass corporate HR ecosystems, where it is crucial to match 

competencies against changing strategic requirements. 

    In the future, the study must be able to scale this model into live pilot programs, incorporate feedback mechanisms 

with users, and demonstrate real-time flexibility across other sectors. Additional knowledge can be gained on how contextual 

variables impact the AI-driven optimization of HR through cross-industrial applications, like healthcare and government 

leadership pipelines. 

    To summarize, the research presents a novel, scientifically grounded framework that integrates AI, NLP, and 

decision science into the planning of leadership development. Its transparent scoring mechanism, organizational strategy, 

and scalability make it a vital instrument in current HRM practices as organizations aim to achieve quantitative returns on 

investments in leadership. 

 

CONCLUSIONS  

This study aims to develop and evaluate an AI-driven software that identifies the core competencies required for leadership 

roles and aligns them with strategic learning pathways using natural language processing (NLP) and multi-criteria decision-

making methods. In this research, the authors sought to design an AI-informed framework to automatically identify 

leadership competencies within the unstructured job descriptions and automatically match them with the relevant training 

programs by translating them into the NLP and multi-criteria decision-making (MCDM) methods. The system has been 

proposed to handle strategic human resource planning and leadership development. An experimental assessment reveals 

that the AI-NLP engine is effective in predicting the core competencies necessary to become a leader. The composite 

MCDM model effectively ranks training programs in terms of relevance, cost, duration, and strategic impact. The model 

demonstrated a marked superiority in both precision and consistency, as it outperformed conventional manual training 

approaches due to its high precision in aligning training to competency. The outcomes confirmed the practicality, 

adaptability, and the system's capacity to fit into different institutional priorities. This work has several notable contributions. 

It combines both semantic analysis, powered by AI, and decision science in the HRM field. In practice, it enables HR 

professionals and decision-makers to adopt evidence-based approaches in developing leadership, with fewer perceptions of 

bias and greater levels of transparency. The tool helps institutions transform traditional training plans into dynamic, 

competency-based training development aligned with organizational objectives. The research does not lack limitations. It 

primarily ventures into leadership positions in the educational and governmental sectors in certain regions. This framework 

can be applied to other industries or incorporate up-to-date labor market data in future studies. The adaptability of the model 

may be further improved by including dynamic knowledge graphs or those based on feedback. Ultimately, this framework 

provides scalable and innovative human capital development, supporting future-ready and AI-integrated talent strategies, 

which are particularly crucial when digital transformation drives the acceleration of institutional change. 
                      

                                                                                                                                                                                
Author Contributions: Conceptualization, B.S.; Methodology, B.S.; Software, B.S.; Validation, B.S., S.A., D.K.M. and V.P.S.; Formal Analysis, B.S.; 

Investigation, S.A.; Resources, S.A.,V.P.S., D.K.M., Q.A.; Data Curation, B.S., V.P.S., D.K.M., Q.A.; Writing – Original Draft Preparation, B.S.; Writing 

– Review & Editing, S.A., V.P.S., D.K.M.,  and Q.A.; Visualization, B.S.; Supervision, S.A. and QA; Project Administration, S.A.; Funding Acquisition, 
S.A. Authors have read and agreed to the published version of the manuscript. 

Institutional Review Board Statement: Ethical review and approval were waived for this study, as the research does not involve vulnerable groups or 

sensitive issues. 
Funding: This research was funded by the University of Buraimi, Oman. The authors acknowledge the university’s support in facilitating this study. 

Acknowledgments: The authors would like to sincerely thank the University of Buraimi for providing essential funding and resources to support this 

research. This work is part of the approved internal project titled "Job Training and the Role of Artificial Intelligence Techniques in Predicting Training 
Needs: A Case Study in Oman," supported by the University of Buraimi. Their continuous encouragement and support were instrumental in the successful 

completion of this study. 

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available 

due to restrictions. 

Conflicts of Interest: The authors declare no conflict of interest.                                                                                                                                                                                                                                   

 

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