Practices of Instructors in Integrating Artificial Intelligence (AI) in Assessing Student Performance among the Community Colleges in Albay

Despite the promising benefits of AI in enhancing educational outcomes, its adoption faces significant barriers, including inadequate technological infrastructure, resistance from educators, and concerns regarding data privacy and ethics. This research aims to examine how instructors at community colleges in Albay, Philippines, are integrating AI into assessment practices, the challenges they encounter, and the broader implications for teaching and learning. This study uses descriptive quantitative research to explore the distinct challenges faced by community colleges, including limited resources and diverse student needs, while highlighting the potential for transforming educational practices. It also contributes to ongoing discussions on the future of education and the role of technology in fostering more inclusive, dynamic, and effective learning environments. The primary data sources for this study were school administrators from Higher Education Institutions in the first, second, and third congressional districts of Albay province. Additionally, full-time and part-time instructors, as well as students from Community Colleges in these districts, were also key data sources. The proposed AI Framework for assessing student performance focuses on three key areas: AI Supervision to ensure fairness and accountability, Operational Elements to ensure secure integration, and Instructional Dimensions aimed at improving teaching by personalizing content and refining strategies based on student data.