This research examines the factors influencing student satisfaction with the services offered by the Office of Student Affairs and Services (OSAS) at Cebu Technological University - Argao Campus. The study gathered data from 242 students using a survey questionnaire and used Ordinary Least Squares (OLS) model analysis to measure the impact of personnel competency and professionalism, service experience and interaction, and service commitment and environmental factors on overall satisfaction. The findings emphasize the significance of having knowledgeable, competent, and compassionate staff, as they suggest that personnel competency and professionalism greatly influences student satisfaction. The result also stresses the importance of service experience and interaction, indicating that higher education institutions should prioritize being responsive to student inquiries, providing a safe campus environment, and promoting open communication with students. Additionally, the study highlights the importance of environmental factors and service commitment, arguing that improving student satisfaction requires operational enhancements such as quicker service, easy navigation, and facility maintenance. Addressing these issues could lead to higher satisfaction levels, aligning with the university's goal of creating a welcoming and engaging learning environment

Despite advances in using multi-criteria decision-making (MCDM) methods and their fuzzy set extensions for human evaluations of large language models (LLMs), several gaps remain in the literature, particularly in task-specific evaluations that offer a more tractable and interpretable approach. Thus, this work develops a generalized intuitionistic fuzzy MCDM approach that bridges methodological gaps by outlining two contributions. First, the integration of SWARA (Stepwise Weight Assessment Ratio Analysis) and WINGS (Weighted Influence Non-linear Gauge System) is demonstrated to compute the priority weights of the evaluation criteria, thereby augmenting the independence limitation in prior relevant studies. Second, we introduce a newly improved IF-EDAS (intuitionistic fuzzy Evaluation based on Distance from Average Solution) that preserves more uncertain information and provides a more natural extension of the canonical EDAS framework, starting with the adoption of the IFWAM (intuitionistic fuzzy weighted arithmetic mean) operator for a more intuitive approach in generating the intuitionistic fuzzy average solution vector. Also, the proposed IF-EDAS variant employs three decision rules and the Hamming distance metric in its novel computational approach. The proposed hybrid approach was deployed in two case studies evaluating five popular LLMs for text summarization across seven interdependent criteria. Results show that SWARA initially prioritizes accuracy, coherence, and consistency, but these were revised when accounting for criteria interdependence, with coherence and language quality emerging as the most preferred criteria. Both case studies suggest that Gemini may perform favorably, while Copilot may consistently rank last. The findings of the case studies share similar insights with those of three other similar IF-EDAS variants, although our claims may have limited external validity, which requires more case studies and experts in future task-specific human evaluations. The proposed approach, along with its deployment in two case studies, demonstrates human evaluations of LLMs with greater computational interpretability, which contribute to the general MCDM literature.