2026 ASEE Annual Conference & Exposition

Critical thinking is a fundamental skill in programming education, particularly as automation and generative artificial intelligence (GenAI) increasingly shape how programming is taught and practiced. Programming requires students to analyze ill-structured problems, evaluate multiple solution paths, debug and refine code, and justify design decisions. At the same time, concerns about GenAI have grown, with recent national survey data showing that nearly 90% of faculty in the United States believe these tools may weaken students’ critical thinking skills. Despite these concerns, there is limited synthesis of how critical thinking is currently defined, assessed, and supported within programming education research. This study addresses this gap through a systematized review of empirical studies focused on critical thinking in higher education programming contexts.

Following PRISMA guidelines, we reviewed 19 empirical studies to examine the frameworks used to conceptualize critical thinking, the components emphasized, the instruments used for assessment, and the instructional interventions shown to be effective. The findings reveal substantial variation in how critical thinking is defined and measured, with most studies relying on broad educational or cognitive frameworks rather than critical thinking–specific models. However, there was consistent emphasis on analysis and evaluation as central components of critical thinking in programming. Interventions that showed positive outcomes typically involved authentic programming tasks, structured reflection, and timely feedback. Importantly, several studies demonstrated that GenAI tools, when intentionally designed as supports for reflection and evaluation, can contribute positively to students’ critical thinking. Together, these results suggest that the impact of GenAI on critical thinking is shaped less by the technology itself and more by the pedagogical choices surrounding its use, pointing to the need for clearer frameworks and theory-informed designs in future programming education research.