Educational impacts of generative artificial intelligence on learning and performance of engineering students in China

Abstract: With the rapid advancement of generative artificial intelligence (AI), its potential applications in higher education have attracted significant attention. This study investigated how 148 students from diverse engineering disciplines and regions across China used generative AI, focusing on its impact on thei r learning experience and the opportunities and challenges it poses in engineering education. Based on the surveyed data, we explored four key areas: the frequency and application scenarios of AI use among engineering students, its impact on students' learning and performance, commonly encountered challenges in using generative AI, and future prospects for its adoption in engineering education. The results showed that more than half of the participants reported a positive impact of generative AI on their learning efficiency, initiative, and creativity, with nearly half believing it also enhanced their independent thinking. However, despite acknowledging improved study efficiency, many felt their actual academic performance remained largely unchanged and expressed concerns about the accuracy and domain-specific reliability of generative AI. Our findings provide a first-hand insight into the current benefits and challenges generative AI brings to students, particularly Chinese engineering students, while offering several recommendations--especially from the students' perspective--for effectively integrating generative AI into engineering education. Key words: artificial intelligence; pedagogy; learning; student; engineering 1. Introduction Generative artificial intelligence (AI), such as Chat GPT developed by OpenAI, has gained significant attention for its innovative capabilities. By leveraging deep learning, generative AI creates diverse content, including text, images, audio, and video, excelling in creative and interactive tasks [1]. In education, it offers immense potential for personalized learning, instant feedback, and assistance in tasks like data analysis, literature review, and report writing, thereby enhancing learning outcomes [2, 3, 4, 5]. It s ability to support complex problem-solving and research development through precise outputs and simple instructions makes it a transformative tool, particularly in engineering education [6, 7, 8]. Engineering education is founded on an integration of multiple disciplines such as engineering, science, technology, and mathematics [9, 1 0].