International Journal of
Information and Education Technology

Abstract

Project-based learning is a critical method for cultivating design thinking in engineering students. However, providing personalized, just-in-time learning support in the complex and often mobile contexts of engineering practice remains a significant challenge. This study investigates the effectiveness of a generative artificial intelligence-driven mobile scaffold, named DesignMate, designed to support students in an electronic system design project. Grounded in cognitive load theory, the mobile application integrates four types of scaffolds: concept integration, process simplification, strategy guidance, and metacognitive stimulation. In a quasi-experimental design, 71 students were divided into an experimental group using DesignMate and a control group. Results indicate that, compared to the control group, the experimental group experienced significantly reduced extraneous cognitive load, along with significant increases in germane cognitive load, intrinsic learning motivation, and self-efficacy. Furthermore, their final design projects demonstrated superior overall quality, novelty, and extensibility. No significant differences were found in intrinsic cognitive load or project usability. This study provides empirical evidence that mobile AI scaffolds enhance design thinking in engineering education, highlighting their potential to deliver personalized cognitive support in project-based learning.