Engineering student perceptions of generative AI use in learning trust judgment and information seeking

Abstract

Generative AI is rapidly reshaping higher education, yet its influence on student learning in engineering remains insufficiently understood. In courses that require conceptual understanding, design reasoning, and problem-solving, AI tools may support learning by providing explanations, solution pathways, and feedback. At the same time, they raise questions about trust, verification, and the quality of student thinking. This study investigates student perceptions of generative AI in engineering education, focusing on learning, trust, and information-seeking. This work extends a prior conceptual framework grounded in Ellis’s theory of information-seeking. The study applies a six-stage subset of Ellis’s model—starting, chaining, browsing, differentiating, monitoring, and verifying—to examine how students perceived generative AI during an engineering learning activity. It also examines themes related to perceived usefulness, learning support, trust, judgment, and AI use. Within this sample, respondents generally reported perceiving AI as a support tool for conceptual understanding, efficiency, and checking their reasoning. Among respondents who reported at least some AI use, higher agreement was observed for items concerning ongoing task support, perceived deeper learning, and the application of their own judgment, whereas lower agreement was observed for using AI to understand the problem requirements initially or to check and validate technical work.