The Hypothetical Learning Trajectories of AI Usage in Learning Integral for Aerospace Engineering Students

The increasing use of AI among students has transformed learning habits, often shifting from deep conceptual understanding to quick solution retrieval. Mathematics education in aerospace engineering requires innovative approaches to enhance students' conceptual understanding and problem-solving skills. This study implemented Realistic Mathematics Education (RME) for Aerospace Engineering Students (AES) using a design research methodology. It is focused only on the development of the hypothetical learning trajectory (HLT) in learning integration strategies for the first and second year of AES using artificial intelligence (AI). After working with the HLT during the first and second cycle, this study discovered that the students' high expectations of AI while solving integration approaches did not match. Students still require more assistance to grasp the AI answer, such as lecturer clarification or video explanation on YouTube. Students frequently use AI to solve problems without fully comprehending the actual procedure. Due to the time constraints, they use the AI answer immediately rather than paraphrasing it to their understanding. Consequently, we found that students realise their inability to depend completely on AI for deep understanding. As a result, AI is used to facilitate the recollection of existing knowledge or the confirmation of the final response rather than to understand new material. AI supports teaching but is not a substitute.