AI in Employee Performance Management: Process Mining for Enhancing Productivity and Growth

Maqsood Mahmud; Ahmad Ghayur; Zahra Afridi

doi:10.1109/ICIT64950.2025.11049093

AI in Employee Performance Management: Process Mining for Enhancing Productivity and Growth

Maqsood Mahmud
, Ahmad Ghayur
, Zahra Afridi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This study examines the integration of artificial intelligence (AI) and process mining in employee performance management at Prince Muhammad Bin Fahad University. A survey of 44 staff members assessed AI familiarity, productivity impact, and ethical concerns. Results showed 68% of postgraduate respondents and 75% of participants under age 35 highly accepted AI tools. Correlation and sentiment analyses, supported by predictive modeling, identified key factors influencing AI adoption. Strong associations were observed between AI learning tools and career alignment (r = 0.88), and between fairness and work quality (r = 0.82). Younger participants expressed optimism, while 61% of older respondents raised concerns about data privacy and bias. These findings underscore the importance of transparent, ethical, and user-centric AI systems. The research demonstrates that AI, integrated with process mining, offers strategic value for data-driven decision-making in human resource management.

Original language	English
Title of host publication	AI in Employee Performance Management: Process Mining for Enhancing Productivity and Growth
Publisher	IEEE
Pages	363-368
Number of pages	6
ISBN (Electronic)	979-8-3315-0894-4
ISBN (Print)	979-8-3315-0895-1
DOIs	https://doi.org/10.1109/ICIT64950.2025.11049093
Publication status	Published online - 1 Jul 2025
Event	2025 12th International Conference on Information Technology (ICIT) - Amman, Jordan Duration: 27 May 2025 → 30 May 2025 https://icit.zuj.edu.jo/Home/

Publication series

Name
ISSN (Print)	2831-3380
ISSN (Electronic)	2831-3399

Conference

Conference	2025 12th International Conference on Information Technology (ICIT)
Country/Territory	Jordan
City	Amman
Period	27/05/25 → 30/05/25
Internet address	https://icit.zuj.edu.jo/Home/

Bibliographical note

We gratefully acknowledge the support of the Artificial Intelligence Research Center (AIRC), School of Computing, Ulster University, Belfast, Northern Ireland, United Kingdom, for their support in this research.

Keywords

Artifical Intelligence (AI)
Process Mining
Employee Performance Management
Productivity Enhancement
AI-driven Performance Evaluation

Access to Document

10.1109/ICIT64950.2025.11049093

AAMAuthor Accepted Manuscript, 543 KB

Cite this

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abstract = "This study examines the integration of artificial intelligence (AI) and process mining in employee performance management at Prince Muhammad Bin Fahad University. A survey of 44 staff members assessed AI familiarity, productivity impact, and ethical concerns. Results showed 68\% of postgraduate respondents and 75\% of participants under age 35 highly accepted AI tools. Correlation and sentiment analyses, supported by predictive modeling, identified key factors influencing AI adoption. Strong associations were observed between AI learning tools and career alignment (r = 0.88), and between fairness and work quality (r = 0.82). Younger participants expressed optimism, while 61\% of older respondents raised concerns about data privacy and bias. These findings underscore the importance of transparent, ethical, and user-centric AI systems. The research demonstrates that AI, integrated with process mining, offers strategic value for data-driven decision-making in human resource management.",

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Mahmud, M, Ghayur, A & Afridi, Z 2025, AI in Employee Performance Management: Process Mining for Enhancing Productivity and Growth. in AI in Employee Performance Management: Process Mining for Enhancing Productivity and Growth. IEEE, pp. 363-368, 2025 12th International Conference on Information Technology (ICIT), Amman, Jordan, 27/05/25. https://doi.org/10.1109/ICIT64950.2025.11049093

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AU - Mahmud, Maqsood

AU - Ghayur, Ahmad

AU - Afridi, Zahra

N1 - We gratefully acknowledge the support of the Artificial Intelligence Research Center (AIRC), School of Computing, Ulster University, Belfast, Northern Ireland, United Kingdom, for their support in this research.

PY - 2025/7/1

Y1 - 2025/7/1

N2 - This study examines the integration of artificial intelligence (AI) and process mining in employee performance management at Prince Muhammad Bin Fahad University. A survey of 44 staff members assessed AI familiarity, productivity impact, and ethical concerns. Results showed 68% of postgraduate respondents and 75% of participants under age 35 highly accepted AI tools. Correlation and sentiment analyses, supported by predictive modeling, identified key factors influencing AI adoption. Strong associations were observed between AI learning tools and career alignment (r = 0.88), and between fairness and work quality (r = 0.82). Younger participants expressed optimism, while 61% of older respondents raised concerns about data privacy and bias. These findings underscore the importance of transparent, ethical, and user-centric AI systems. The research demonstrates that AI, integrated with process mining, offers strategic value for data-driven decision-making in human resource management.

AB - This study examines the integration of artificial intelligence (AI) and process mining in employee performance management at Prince Muhammad Bin Fahad University. A survey of 44 staff members assessed AI familiarity, productivity impact, and ethical concerns. Results showed 68% of postgraduate respondents and 75% of participants under age 35 highly accepted AI tools. Correlation and sentiment analyses, supported by predictive modeling, identified key factors influencing AI adoption. Strong associations were observed between AI learning tools and career alignment (r = 0.88), and between fairness and work quality (r = 0.82). Younger participants expressed optimism, while 61% of older respondents raised concerns about data privacy and bias. These findings underscore the importance of transparent, ethical, and user-centric AI systems. The research demonstrates that AI, integrated with process mining, offers strategic value for data-driven decision-making in human resource management.

KW - Artifical Intelligence (AI)

KW - Process Mining

KW - Employee Performance Management

KW - Productivity Enhancement

KW - AI-driven Performance Evaluation

U2 - 10.1109/ICIT64950.2025.11049093

DO - 10.1109/ICIT64950.2025.11049093

M3 - Conference contribution

SN - 979-8-3315-0895-1

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EP - 368

BT - AI in Employee Performance Management: Process Mining for Enhancing Productivity and Growth

PB - IEEE

T2 - 2025 12th International Conference on Information Technology (ICIT)

Y2 - 27 May 2025 through 30 May 2025

ER -

AI in Employee Performance Management: Process Mining for Enhancing Productivity and Growth

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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Cite this