A Study on Identifying Priorities for EdTech and Generative AI Faculty Support in a University Teaching and Learning Center

This study identifies priority areas of EdTech and generative AI faculty support that university teaching and learning centers (CTL) should strengthen. Drawing on survey responses from 151 faculty members, the study employed a modified needs index based on the Borich approach, the Locus for Focus (LfF) model, Importance-Performance Analysis (IPA), and thematic analysis with convergent mixed-methods triangulation. Because present and required levels were measured as dichotomous multiple-response items, the index is interpreted as a relative priority among the surveyed areas; a sensitivity analysis restricted to program participants reproduced identical rank order and quadrant classifications. EdTech competencies for creative teaching and learner-centered instructional design emerged as higher relative priorities in the HH quadrant. The IPA results identified plagiarism detection and citation guidelines for AI-assisted student work as a Q1 area requiring priority improvement, while all seven items showed high absolute importance, indicating that lower-quadrant items should be maintained rather than reduced. Qualitative findings strongly converged with the quantitative results, supporting an integrated short-, mid-, and long-term faculty support strategy. The findings offer practical implications for faculty support in fisheries, marine, and vocational higher education, where practice-based instruction and academic integrity in AI-assisted work are pressing concerns.