This paper presents the design, development, and evaluation of the REDI Monitoring System, a web-based platform aimed at enhancing the management and monitoring of funded research projects at Romblon State University (RSU). The system provides streamlined functionalities for proposal creation, submission, collaborator management, and administrative oversight, tailored to the needs of both students and faculty members. The development process adhered to established software engineering standards to ensure robustness and usability. A comprehensive testing phase was conducted with 50 participants, including students and faculty, following the ISO/IEC/IEEE 29119 software testing framework. Results demonstrated high user satisfaction, with over 90% of participants finding the system user-friendly and reliable. Minor areas for improvement were identified in notification delivery and interface responsiveness for faculty users. The REDI Monitoring System presents an effective and efficient solution that supports RSU’s research administration processes, fostering greater collaboration and transparency in funded research activities.

The integration of artificial intelligence (AI) into higher education is transforming instructional systems by changing how faculty design, organize, deliver, and evaluate technology-enhanced learning experiences. This transformation requires educators to develop not only technological proficiency but also the ability to integrate technology and AI with pedagogical strategies and disciplinary content. This study evaluates a TPACK-based faculty development intervention and examines its application to the design and evaluation of technology-enhanced instructional modules. A mixed-methods research design was employed with 20 tertiary educators. Quantitative data were collected using a validated TPACK self-assessment administered before and after a three-day training workshop, while qualitative responses and developed instructional modules provided additional evidence of participants’ experiences and practices. Descriptive statistics and paired-samples t-tests were used to examine changes in TPACK competencies, complemented by thematic analysis of qualitative responses. The findings demonstrate significant improvements in Content Knowledge (CK), Technological Pedagogical Knowledge (TPK), and overall TPACK following the intervention. The overall TPACK score increased significantly, with a mean difference of 0.25 and a p-value of 0.021. The intervention also resulted in the development and institutional validation of technology- and AI-enhanced instructional modules, with 16 of the 20 modules (80%) successfully approved for academic use. Qualitative findings further indicate increased awareness of purposeful technology selection, AI-supported learning, student engagement, verification of AI-generated information, and intentional instructional design. Based on these findings, the study conceptualizes faculty development and instructional-material creation as an AI-integrated instructional system in which technological knowledge, pedagogy, content, AI capabilities, module design, evaluation, and institutional validation operate as interconnected components. The resulting TPACK-based framework provides a structured approach for strengthening faculty capability and engineering technology-enhanced learning modules for AI-supported higher education.