V3I5P149

Generative Artificial Intelligence Integrated Pedagogy and Quality of Student Output in Web Design Courses Among Information Technology Students

Reuben M. Llobia^1*, Dr. Rodelio B. Pasion²

Abstract

This study examined the relationship between Generative Artificial Intelligence (GenAI)-integrated pedagogy and the quality of student outputs in web design courses among Information Technology students at Gingoog City Colleges, Inc. Specifically, it determined students’ GenAI usage profile, level of GenAI-integrated pedagogy, differences in pedagogy according to GenAI usage, quality of web design outputs, and the relationship between pedagogy and student output quality. The study employed a descriptive-correlational research design involving 80 second-year Bachelor of Science in Information Technology (BSIT) students selected through purposive sampling and total enumeration, alongside three expert evaluators who assessed student outputs using a standardized web design rubric. Data were gathered using a researcher-developed questionnaire with a Cronbach’s alpha of 0.971, indicating excellent internal consistency. Statistical tools included frequency and percentage, weighted mean, standard deviation, Kruskal-Wallis H-test, and Pearson r. Findings revealed that 61 students used GenAI for coding, 45 for research, and 39 used it several times weekly, with ChatGPT (57 users) and Canva AI (51 users) as the most-used tools. The overall level of GenAI-integrated pedagogy yielded a grand mean of 2.68 (Neutral/Average), while students’ web design outputs obtained a grand mean of 1.79 (Developing). Furthermore, the findings showed no significant relationship between GenAI-integrated pedagogy and output quality (p > 0.05), indicating that the use of GenAI alone does not necessarily improve student performance.

Keywords:

Generative Artificial Intelligence, Genai-Integrated Pedagogy, Web Design, Student Output Quality, Information Technology Students