Thesis Abstract

Abstract
This thesis investigates the application of Artificial Intelligence (AI) in employee performance evaluation and compares its effectiveness in enhancing traditional evaluation methods. The study aims to explore how AI technologies can revolutionize the performance evaluation process and improve decision-making in human resource management. The research methodology involves a comparative analysis of AI-based performance evaluation systems with traditional methods to identify their strengths, weaknesses, and impact on organizational outcomes. The study also considers the ethical implications and potential challenges associated with AI adoption in performance evaluation. The findings reveal that AI offers several advantages, such as objectivity, efficiency, and accuracy, compared to traditional methods. However, concerns regarding bias, privacy, and trust in AI systems are also identified. The results of this study provide valuable insights for organizations seeking to leverage AI in performance evaluation practices and contribute to the ongoing discourse on the role of technology in human resource management.

Thesis Overview

The project titled "Utilizing Artificial Intelligence in Employee Performance Evaluation: A Comparative Study" aims to explore the integration of artificial intelligence (AI) technologies in the process of evaluating employee performance within organizations. The research focuses on comparing traditional performance evaluation methods with AI-driven approaches to assess their effectiveness, efficiency, and impact on employee performance management. The study begins by providing an introduction to the significance of employee performance evaluation in organizational success, emphasizing the need for accurate, timely, and objective assessments to drive performance improvement and decision-making processes. It delves into the background of the study, highlighting the evolution of performance evaluation practices and the emergence of AI as a transformative tool in the field of human resource management. Identifying the problem of subjectivity, bias, and inefficiency in traditional performance evaluation methods, the research aims to address these challenges by exploring how AI technologies such as machine learning, natural language processing, and data analytics can enhance the evaluation process. The objectives of the study include evaluating the effectiveness of AI in enhancing the accuracy and objectivity of performance assessments, improving feedback mechanisms, and facilitating continuous performance monitoring and development. Acknowledging the limitations of AI technologies, such as data privacy concerns, algorithmic bias, and the need for human oversight, the study outlines the scope of research to focus on specific AI applications in performance evaluation while considering ethical and legal implications. The significance of the study lies in its potential to revolutionize performance management practices, drive organizational efficiency, and enhance employee engagement and satisfaction. The research methodology involves a comparative analysis of traditional performance evaluation methods and AI-driven approaches through a mixed-methods research design. Data collection methods include surveys, interviews, and case studies to gather insights from HR professionals, managers, and employees across various industries. The analysis of findings will be structured to provide a comprehensive discussion on the benefits, challenges, and future implications of integrating AI in performance evaluation. Overall, this research aims to contribute to the existing literature on AI applications in human resource management and provide practical insights for organizations seeking to leverage technology for improving employee performance management. By exploring the potential of AI in transforming performance evaluation processes, this study seeks to offer valuable recommendations for enhancing organizational performance, employee development, and overall business success.