_____________________________________________________________________________________________________ *Corresponding author: E-mail: Olanrewaju.okuyelu.840@my.csun.edu, kevwe.onomeirikefe@gmail.com; Cite as: Kevwe Onome-Irikefe, and Olanrewaju Okuyelu. 2025. “Enhancing Program Performance Evaluation through Artificial Intelligence: A Mixed-Methods Study Using LLM Models”. Advances in Research 26 (4): 512-21. https://doi.org/10.9734/air/2025/v26i41431. Advances in Research Volume 26, Issue 4, Page 512-521, 2025; Article no.AIR.141043 ISSN: 2348-0394, NLM ID: 101666096 Enhancing Program Performance Evaluation through Artificial Intelligence: A Mixed-methods Study Using LLM Models Kevwe Onome-Irikefe a* and Olanrewaju Okuyelu b* a University of Rochester, United States of America. b California State University, Northridge, United States of America. Authors’ contributions This work was carried out in collaboration between both authors. Both authors read and approved the final manuscript. Article Information DOI: https://doi.org/10.9734/air/2025/v26i41431 Open Peer Review History: This journal follows the Advanced Open Peer Review policy. Identity of the Reviewers, Editor(s) and additional Reviewers, peer review comments, different versions of the manuscript, comments of the editors, etc are available here: https://pr.sdiarticle5.com/review-history/141043 Received: 14/05/2025 Accepted: 26/07/2025 Published: 30/07/2025 ABSTRACT 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