Developing Adaptive Systems and Digital Learning Resources for Academic English Reading: Needs Analysis of Science and Engineering Graduates in China

The increasing globalization of scientific research requires graduate students in STEM fields to have strong academic English reading ability. However, academic English reading curricula in China remain underdeveloped. While AI-powered adaptive learning systems and digital learning resources present a promising solution to this issue, there lacks sufficient evidence regarding the specific types of resources and materials that should be included into these systems. Therefore, a comprehensive needs analysis is essential to understand the challenges and requirements of students engaged in academic English reading. This study, grounded in Chen's personal needs analysis model, explores the primary difficulties faced by Chinese science and engineering graduate students in academic English reading, as well as the specific learning needs in the AI era. It is found that non-English major graduates are acutely aware of their academic English reading deficiencies and highly motivated to improve. Furthermore, the academic English reading needs vary among students pursuing academic master's degree, professional master's degree, and doctoral studies. The findings provide an empirical foundation for optimizing academic English reading curricula and integrating digital learning resources. Additionally, it establishes a data-driven basis for developing an adaptive learning system with personalized features, ultimately enhancing teaching effectiveness and addressing students' individualized learning needs.