A multi-dimensional needs analysis dataset for Industrial Engineering ESP education based on the IE-SMM framework

English for Specific Purposes (ESP) education based on needs analysis is recognized for its ability to significantly reduce learning inefficiencies and eliminate blind spots. ESP needs analysis in China, however, has historically relied on instructors’ intuition and experience rather than direct, learner-centered evaluations. To address this gap, a comprehensive survey was conducted—representing the first dataset of its kind for Industrial Engineering (IE) ESP courses at Chinese universities. A key innovation in this data collection was the development and implementation of the Industrial Engineering-Specific Mapping Model (IE-SMM) framework, which establishes a structured correlation between professional IE competencies and linguistic requirements. Based on this framework, a Likert-type questionnaire comprising four dimensions and 23 items was used to gather rating data from 183 participants. Following rigorous technical validation of the questionnaire’s reliability and data consistency, the resulting dataset provides a detailed map of the difficulties and bottlenecks encountered in ESP learning. This study not only supports more purposeful IE ESP curriculum design but also offers a validated foundation and case study for interdisciplinary ESP research. To our knowledge, this is the only publicly available dataset specifically aligned with the real-world needs of IE ESP learners in the Chinese context.