Antecedents, Process, and Consequences of AI‐Based Education in Design Thinking Among Engineering Students: A Theory‐Generating Meta‐Synthesis Approach

Abstract

This study seeks to investigate the antecedents, processes, and outcomes of AI‐based education in fostering design thinking among engineering students. Through a meta‐synthesis of 151 peer‐reviewed articles published between 2015 and 2025, sourced from databases including Web of Science, Scopus, IEEE Xplore, ACM Digital Library, and ScienceDirect, we generated a grounded theory. Findings revealed two major antecedent domains: Students' prerequisites (including positive attitudes, basic skills, and foundational knowledge of design thinking) and Instructors' triple competencies (technological, pedagogical, and content knowledge). These factors collectively shape students' intention to learn design thinking (DT), represented by the core themes of usefulness and success, and moderated by variables such as demographics and ethical awareness. The instructional process was structured around the five core stages of the design thinking framework—Empathy, Definition, Ideation, Prototyping, and Testing—with relevant AI tools integrated into each stage to facilitate activities, yielding 46 open codes. The outcomes were categorized into two themes: the development of design thinking competencies and social‐psychological growth. The findings highlight AI's significant contribution to fostering both technical skills and ethical reasoning, offering an instructional model that can serve as a practical guide for enhancing design thinking skills among engineering students.