What is it about?

The primary aim of this study is to explore how Artificial Intelligence can enhance the effectiveness of program performance evaluations. By leveraging data-driven techniques, the research aims to identify methods that facilitate more accurate assessments of program outcomes using LLM models, thereby enhancing decision-making processes. The study adopts a mixed-methods design, combining qualitative and quantitative approaches to assess the impact of Artificial Intelligence on program performance evaluation. The research was conducted over twelve months, enabling a detailed analysis of both the immediate and long-term impacts of Artificial Intelligence interventions on program management. The methodology employed in this study is structured around a comprehensive approach to data collection and analysis, ensuring robust insights into program Short Research Article 513 performance evaluations. Qualitative research was conducted to identify relevant metrics for assessment. The qualitative component encompasses in-depth interviews with key stakeholders, providing insights into the contextual factors that influence analytics deployment. Concurrently, the quantitative analysis employs statistical methodologies to evaluate performance metrics both before and after the implementation of AI-driven methodologies. The study's findings highlight the critical role of Artificial Intelligence in enhancing program performance evaluation. Detailed data analysis revealed that employing Artificial Intelligence facilitates the extraction of real-time insights, which significantly assist in strategic decision-making. Programs that integrated advanced Artificial Intelligence and data analytics tools showed improved capability in identifying trends, directly impacting their effectiveness and adaptability. The study concludes that Artificial Intelligence and data analytics enhance program performance evaluations. By providing dynamic, real-time insights and risk assessments, the utilization of Artificial Intelligence and data analytics significantly improves decision-making processes and aids in strategic planning. It emphasizes the importance of robust data quality and governance practices that ensure the accuracy and reliability of evaluations.

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Perspectives

This study is significant as it advances understanding of how Artificial Intelligence (AI) can transform program performance evaluations from static, retrospective assessments into dynamic, data-driven processes. By integrating AI and large language model (LLM) techniques into evaluation frameworks, the research highlights new pathways for enhancing the accuracy, speed, and depth of program analysis; ultimately enabling more informed and timely decision-making. The findings underscore the critical role of AI in extracting real-time insights and identifying performance trends that traditional evaluation methods often overlook. This has substantial implications for organizations seeking to improve accountability, optimize resource allocation, and strengthen strategic planning. Furthermore, the study demonstrates how combining qualitative insights from key stakeholders with quantitative performance metrics produces a more holistic and context-aware understanding of program outcomes. By establishing evidence-based connections between AI adoption and improved program effectiveness, this research contributes to the growing body of literature on data-driven governance and performance management. It offers a practical foundation for policymakers, program managers, and data professionals to implement AI-enhanced evaluation models that promote transparency, adaptability, and measurable impact.

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This page is a summary of: Enhancing Program Performance Evaluation through Artificial Intelligence: A Mixed-methods Study Using LLM Models, Advances in Research, July 2025, Sciencedomain International,
DOI: 10.9734/air/2025/v26i41431.
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