A COMPARATIVE ANALYSIS OF AI - DRIVEN TRAININGANDMONITORING VS. TRADITIONAL METHODS INEMPLOYEELEARNING AND PRODUCTIVITY MEASUREMENT

Aim: This study aims to evaluate the effectiveness of AI-driven training and monitoringsystemscompared to traditional employee training and productivity measurement methods in corporateand organizational settings. Specifically, it seeks to analyze how AI-driven approaches impact learning outcomes, skill retention, engagement levels, and overall workplace productivity. Additionally, the study will assess whether AI-based monitoring provides more accurate, realtime, and personalized insights into employee performance compared to conventional evaluationtechniques. The findings will contribute to understanding the potential advantages andlimitations of AI-driven solutions in optimizing workforce development and productivitymeasurement. Materials and Methods: This study conducts a comparative analysis of AI-driventraining and monitoring versus traditional employee learning and productivity measurement methods. Participants from various corporate sectors, including IT, finance, healthcare, andmanufacturing, will be divided into two groups: one using AI-powered training and monitoringtools, and the other following traditional instructor-led training and conventional evaluationtechniques. The AI-driven group will utilize advanced Learning Management Systems (LMS)such as Courseware for Business and Linked-in Learning, offering adaptive learning andrealtime feedback. AI-powered monitoring tools, including workplace analytic and biometricfeedback, will assess engagement and performance. The traditional group led sessions, printed materials, and standardized performance reviews. A quasi-experimental design will be employed, beginning with a ore-training assessment to establish baselineknowledge and productivity levels. During training, the AI-driven group will receive adaptiverecommendations, while the traditional group follows fixed schedules. Apost-trainingassessment will evaluate skill retention and productivity improvements, with a long-termfollow-up over three to six months to measure sustained impact. Data will be analyzed throughquantitative methods (t-tests, ANOVA) to compare performance indicators like task completiontime, accuracy, and engagement. Qualitative analysis will include employee surveys andinterviews to assess experiences and perceived effectiveness. This approach will determinewhether AI-driven methods provide a significant advantage over traditional trainingandproductivity evaluation techniques. Conclusion: This study aims to determine the effectivenessof AI-driven training and monitoring compared to traditional methods in employee learningandproductivity measurement. By analyzing both quantitative performance metrics and qualitativeemployee feedback, the research will provide insights into how AI-powered approachesinfluence knowledge retention, engagement, and workplace efficiency. If AI-driven trainingproves superior, it could support the adoption of more personalized and data-driven learningstrategies in corporate settings. Conversely, if traditional methods remain equally effective, organizations may consider hybrid approaches that balance AI’s adaptability with the structuredguidance of conventional training. Ultimately, this study will contribute to a better understandingof AI’s role in optimizing workforce development and performance evaluation