Impact of Artificial Intelligence-Assisted Assessment and Traditional Assessment on Web Design and Development in Computing Education

Abstract

The educational process of developing web design competence remains a persistent challenge for many students and educators, particularly in developing countries where conventional teaching methodologies and assessment models often fall short in promoting higher-order thinking and problem-solving. In this study, we respond to the call for innovative assessment approaches by examining the impacts of assessment models on a web design and development course and students’ cognitive load when adopting the AI-assisted assessment model (AAAM) compared to the traditional assessment model (TAM). We employed a mixed-methods research approach, incorporating a quasi-experimental, non-equivalent pretest–posttest control group design and a qualitative component, involving 63 undergraduate students enrolled in CRE 625. The intervention lasted approximately 10 weeks and focused on web design and development across two universities in a developing country. Consistent with quasi-experimental principles, students were assigned to treatment groups based on pre-existing institutional class structures, thereby controlling allocation using criteria rather than randomization. Two validated instruments were used to assess students’ web design and development competence (WDDC) and cognitive load (CL), and the data were analyzed using ANCOVA to evaluate performance gains and the interaction effect with gender.