Ideological and Political Education in Fundamental Engineering Courses Under the Context of Artificial Intelligence: Construction and Goal Optimization

This study integrates ideological and political education (IPE) into engineering fundamentals courses using data mining and artificial intelligence (AI) techniques. The aim is to optimize course design to meet the demands of digital education transformation. Teaching objectives are framed around knowledge, skills, and emotions, with IPE case studies focusing on integrity, innovation, social responsibility, and patriotism. The “One Integration, Two Cultivations, Five Concurrent” teaching model is proposed to cultivate students' scientific spirit and social responsibility. The study utilizes the Chao Xing platform for online teaching, incorporating case-based learning throughout offline classes. Discipline competitions are employed to enhance teacher-student competency. AI-driven personalized learning paths and real-time feedback systems optimize the teaching process. A data-driven evaluation system is developed, using hierarchical analysis to assess IPE content, teaching strategies, implementation methods, and outcomes. This results in a complete evaluation matrix linked to IPE goals. Additionally, statistical and hierarchical analyses of student performance in the “Fundamentals of Mechanical Design” course (2023–2024) at Harbin University of Science and Technology are conducted, providing insights for the digital transformation of education and AI-enhanced course improvements.